WorldWideScience

Sample records for power spectrum forecasts

  1. Linear Algorithms for Radioelectric Spectrum Forecast

    Directory of Open Access Journals (Sweden)

    Luis F. Pedraza

    2016-12-01

    Full Text Available This paper presents the development and evaluation of two linear algorithms for forecasting reception power for different channels at an assigned spectrum band of global systems for mobile communications (GSM, in order to analyze the spatial opportunity for reuse of frequencies by secondary users (SUs in a cognitive radio (CR network. The algorithms employed correspond to seasonal autoregressive integrated moving average (SARIMA and generalized autoregressive conditional heteroskedasticity (GARCH, which allow for a forecast of channel occupancy status. Results are evaluated using the following criteria: availability and occupancy time for channels, different types of mean absolute error, and observation time. The contributions of this work include a more integral forecast as the algorithm not only forecasts reception power but also the occupancy and availability time of a channel to determine its precision percentage during the use by primary users (PUs and SUs within a CR system. Algorithm analyses demonstrate a better performance for SARIMA over GARCH algorithm in most of the evaluated variables.

  2. Daily Average Wind Power Interval Forecasts Based on an Optimal Adaptive-Network-Based Fuzzy Inference System and Singular Spectrum Analysis

    Directory of Open Access Journals (Sweden)

    Zhongrong Zhang

    2016-01-01

    Full Text Available Wind energy has increasingly played a vital role in mitigating conventional resource shortages. Nevertheless, the stochastic nature of wind poses a great challenge when attempting to find an accurate forecasting model for wind power. Therefore, precise wind power forecasts are of primary importance to solve operational, planning and economic problems in the growing wind power scenario. Previous research has focused efforts on the deterministic forecast of wind power values, but less attention has been paid to providing information about wind energy. Based on an optimal Adaptive-Network-Based Fuzzy Inference System (ANFIS and Singular Spectrum Analysis (SSA, this paper develops a hybrid uncertainty forecasting model, IFASF (Interval Forecast-ANFIS-SSA-Firefly Alogorithm, to obtain the upper and lower bounds of daily average wind power, which is beneficial for the practical operation of both the grid company and independent power producers. To strengthen the practical ability of this developed model, this paper presents a comparison between IFASF and other benchmarks, which provides a general reference for this aspect for statistical or artificially intelligent interval forecast methods. The comparison results show that the developed model outperforms eight benchmarks and has a satisfactory forecasting effectiveness in three different wind farms with two time horizons.

  3. Supercluster simulations: impact of baryons on the matter power spectrum and weak lensing forecasts for Super-CLASS

    Science.gov (United States)

    Peters, Aaron; Brown, Michael L.; Kay, Scott T.; Barnes, David J.

    2018-03-01

    We use a combination of full hydrodynamic and dark matter only simulations to investigate the effect that supercluster environments and baryonic physics have on the matter power spectrum, by re-simulating a sample of supercluster sub-volumes. On large scales we find that the matter power spectrum measured from our supercluster sample has at least twice as much power as that measured from our random sample. Our investigation of the effect of baryonic physics on the matter power spectrum is found to be in agreement with previous studies and is weaker than the selection effect over the majority of scales. In addition, we investigate the effect of targeting a cosmologically non-representative, supercluster region of the sky on the weak lensing shear power spectrum. We do this by generating shear and convergence maps using a line-of-sight integration technique, which intercepts our random and supercluster sub-volumes. We find the convergence power spectrum measured from our supercluster sample has a larger amplitude than that measured from the random sample at all scales. We frame our results within the context of the Super-CLuster Assisted Shear Survey (Super-CLASS), which aims to measure the cosmic shear signal in the radio band by targeting a region of the sky that contains five Abell clusters. Assuming the Super-CLASS survey will have a source density of 1.5 galaxies arcmin-2, we forecast a detection significance of 2.7^{+1.5}_{-1.2}, which indicates that in the absence of systematics the Super-CLASS project could make a cosmic shear detection with radio data alone.

  4. Short term and medium term power distribution load forecasting by neural networks

    International Nuclear Information System (INIS)

    Yalcinoz, T.; Eminoglu, U.

    2005-01-01

    Load forecasting is an important subject for power distribution systems and has been studied from different points of view. In general, load forecasts should be performed over a broad spectrum of time intervals, which could be classified into short term, medium term and long term forecasts. Several research groups have proposed various techniques for either short term load forecasting or medium term load forecasting or long term load forecasting. This paper presents a neural network (NN) model for short term peak load forecasting, short term total load forecasting and medium term monthly load forecasting in power distribution systems. The NN is used to learn the relationships among past, current and future temperatures and loads. The neural network was trained to recognize the peak load of the day, total load of the day and monthly electricity consumption. The suitability of the proposed approach is illustrated through an application to real load shapes from the Turkish Electricity Distribution Corporation (TEDAS) in Nigde. The data represents the daily and monthly electricity consumption in Nigde, Turkey

  5. Wind power forecast

    Energy Technology Data Exchange (ETDEWEB)

    Pestana, Rui [Rede Electrica Nacional (REN), S.A., Lisboa (Portugal). Dept. Systems and Development System Operator; Trancoso, Ana Rosa; Delgado Domingos, Jose [Univ. Tecnica de Lisboa (Portugal). Seccao de Ambiente e Energia

    2012-07-01

    Accurate wind power forecast are needed to reduce integration costs in the electric grid caused by wind inherent variability. Currently, Portugal has a significant wind power penetration level and consequently the need to have reliable wind power forecasts at different temporal scales, including localized events such as ramps. This paper provides an overview of the methodologies used by REN to forecast wind power at national level, based on statistical and probabilistic combinations of NWP and measured data with the aim of improving accuracy of pure NWP. Results show that significant improvement can be achieved with statistical combination with persistence in the short-term and with probabilistic combination in the medium-term. NWP are also able to detect ramp events with 3 day notice to the operational planning. (orig.)

  6. Short-term wind power combined forecasting based on error forecast correction

    International Nuclear Information System (INIS)

    Liang, Zhengtang; Liang, Jun; Wang, Chengfu; Dong, Xiaoming; Miao, Xiaofeng

    2016-01-01

    Highlights: • The correlation relationships of short-term wind power forecast errors are studied. • The correlation analysis method of the multi-step forecast errors is proposed. • A strategy selecting the input variables for the error forecast models is proposed. • Several novel combined models based on error forecast correction are proposed. • The combined models have improved the short-term wind power forecasting accuracy. - Abstract: With the increasing contribution of wind power to electric power grids, accurate forecasting of short-term wind power has become particularly valuable for wind farm operators, utility operators and customers. The aim of this study is to investigate the interdependence structure of errors in short-term wind power forecasting that is crucial for building error forecast models with regression learning algorithms to correct predictions and improve final forecasting accuracy. In this paper, several novel short-term wind power combined forecasting models based on error forecast correction are proposed in the one-step ahead, continuous and discontinuous multi-step ahead forecasting modes. First, the correlation relationships of forecast errors of the autoregressive model, the persistence method and the support vector machine model in various forecasting modes have been investigated to determine whether the error forecast models can be established by regression learning algorithms. Second, according to the results of the correlation analysis, the range of input variables is defined and an efficient strategy for selecting the input variables for the error forecast models is proposed. Finally, several combined forecasting models are proposed, in which the error forecast models are based on support vector machine/extreme learning machine, and correct the short-term wind power forecast values. The data collected from a wind farm in Hebei Province, China, are selected as a case study to demonstrate the effectiveness of the proposed

  7. Online Short-term Solar Power Forecasting

    DEFF Research Database (Denmark)

    Bacher, Peder; Madsen, Henrik; Nielsen, Henrik Aalborg

    2011-01-01

    This poster presents two approaches to online forecasting of power production from PV systems. The methods are suited for online forecasting in many applications and here they are used to predict hourly values of solar power for horizons up to 32 hours.......This poster presents two approaches to online forecasting of power production from PV systems. The methods are suited for online forecasting in many applications and here they are used to predict hourly values of solar power for horizons up to 32 hours....

  8. Method of forecasting power distribution

    International Nuclear Information System (INIS)

    Kaneto, Kunikazu.

    1981-01-01

    Purpose: To obtain forecasting results at high accuracy by reflecting the signals from neutron detectors disposed in the reactor core on the forecasting results. Method: An on-line computer transfers, to a simulator, those process data such as temperature and flow rate for coolants in each of the sections and various measuring signals such as control rod positions from the nuclear reactor. The simulator calculates the present power distribution before the control operation. The signals from the neutron detectors at each of the positions in the reactor core are estimated from the power distribution and errors are determined based on the estimated values and the measured values to determine the smooth error distribution in the axial direction. Then, input conditions at the time to be forecast are set by a data setter. The simulator calculates the forecast power distribution after the control operation based on the set conditions. The forecast power distribution is corrected using the error distribution. (Yoshino, Y.)

  9. Ensemble forecasting using sequential aggregation for photovoltaic power applications

    International Nuclear Information System (INIS)

    Thorey, Jean

    2017-01-01

    Our main objective is to improve the quality of photovoltaic power forecasts deriving from weather forecasts. Such forecasts are imperfect due to meteorological uncertainties and statistical modeling inaccuracies in the conversion of weather forecasts to power forecasts. First we gather several weather forecasts, secondly we generate multiple photovoltaic power forecasts, and finally we build linear combinations of the power forecasts. The minimization of the Continuous Ranked Probability Score (CRPS) allows to statistically calibrate the combination of these forecasts, and provides probabilistic forecasts under the form of a weighted empirical distribution function. We investigate the CRPS bias in this context and several properties of scoring rules which can be seen as a sum of quantile-weighted losses or a sum of threshold-weighted losses. The minimization procedure is achieved with online learning techniques. Such techniques come with theoretical guarantees of robustness on the predictive power of the combination of the forecasts. Essentially no assumptions are needed for the theoretical guarantees to hold. The proposed methods are applied to the forecast of solar radiation using satellite data, and the forecast of photovoltaic power based on high-resolution weather forecasts and standard ensembles of forecasts. (author) [fr

  10. Online short-term solar power forecasting

    DEFF Research Database (Denmark)

    Bacher, Peder; Madsen, Henrik; Nielsen, Henrik Aalborg

    2009-01-01

    This paper describes a new approach to online forecasting of power production from PV systems. The method is suited to online forecasting in many applications and in this paper it is used to predict hourly values of solar power for horizons of up to 36 hours. The data used is fifteen......-minute observations of solar power from 21 PV systems located on rooftops in a small village in Denmark. The suggested method is a two-stage method where first a statistical normalization of the solar power is obtained using a clear sky model. The clear sky model is found using statistical smoothing techniques....... Then forecasts of the normalized solar power are calculated using adaptive linear time series models. Both autoregressive (AR) and AR with exogenous input (ARX) models are evaluated, where the latter takes numerical weather predictions (NWPs) as input. The results indicate that for forecasts up to two hours...

  11. Power distribution forecasting device for reactors

    International Nuclear Information System (INIS)

    Tsukii, Makoto

    1981-01-01

    Purpose: To save expensive calculations on the forecasting of reactor power distribution. Constitution: Core status (CSD) such as entire coolant flow rate, pressures in the reactor, temperatures at the outlet and inlet and positions for control rods are inputted into a power distribution calculation device to calculate the power distribution based on physical models intermittently. Further, present power distribution is calculated based on in-core neutron flux measured values and CSD in a process control computer. Further, the ratio of the calculation results of the latter to those of the former is calculated, stored and inputted into a correction device to correct the forecast power distribution obtained by the power distribution calculation device. This enables to forecast the power distribution with excellent responsivity in the reactor site. (Furukawa, Y.)

  12. Wind power forecasting accuracy and uncertainty in Finland

    Energy Technology Data Exchange (ETDEWEB)

    Holttinen, H.; Miettinen, J.; Sillanpaeae, S.

    2013-04-15

    Wind power cannot be dispatched so the production levels need to be forecasted for electricity market trading. Lower prediction errors mean lower regulation balancing costs, since relatively less energy needs to go through balance settlement. From the power system operator point of view, wind power forecast errors will impact the system net imbalances when the share of wind power increases, and more accurate forecasts mean less regulating capacity will be activated from the real time Regulating Power Market. In this publication short term forecasting of wind power is studied mainly from a wind power producer point of view. The forecast errors and imbalance costs from the day-ahead Nordic electricity markets are calculated based on real data from distributed wind power plants. Improvements to forecasting accuracy are presented using several wind forecast providers, and measures for uncertainty of the forecast are presented. Aggregation of sites lowers relative share of prediction errors considerably, up to 60%. The balancing costs were also reduced up to 60%, from 3 euro/MWh for one site to 1-1.4 euro/MWh to aggregate 24 sites. Pooling wind power production for balance settlement will be very beneficial, and larger producers who can have sites from larger geographical area will benefit in lower imbalance costs. The aggregation benefits were already significant for smaller areas, resulting in 30-40% decrease in forecast errors and 13-36% decrease in unit balancing costs, depending on the year. The resulting costs are strongly dependent on Regulating Market prices that determine the prices for the imbalances. Similar level of forecast errors resulted in 40% higher imbalance costs for 2012 compared with 2011. Combining wind forecasts from different Numerical Weather Prediction providers was studied with different combination methods for 6 sites. Averaging different providers' forecasts will lower the forecast errors by 6% for day-ahead purposes. When combining

  13. On probabilistic forecasting of wind power time-series

    DEFF Research Database (Denmark)

    Pinson, Pierre

    power dynamics. In both cases, the model parameters are adaptively and recursively estimated, time-adaptativity being the result of exponential forgetting of past observations. The probabilistic forecasting methodology is applied at the Horns Rev wind farm in Denmark, for 10-minute ahead probabilistic...... forecasting of wind power generation. Probabilistic forecasts generated from the proposed methodology clearly have higher skill than those obtained from a classical Gaussian assumption about wind power predictive densities. Corresponding point forecasts also exhibit significantly lower error criteria....

  14. Wind Power Forecasting Error Frequency Analyses for Operational Power System Studies: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Florita, A.; Hodge, B. M.; Milligan, M.

    2012-08-01

    The examination of wind power forecasting errors is crucial for optimal unit commitment and economic dispatch of power systems with significant wind power penetrations. This scheduling process includes both renewable and nonrenewable generators, and the incorporation of wind power forecasts will become increasingly important as wind fleets constitute a larger portion of generation portfolios. This research considers the Western Wind and Solar Integration Study database of wind power forecasts and numerical actualizations. This database comprises more than 30,000 locations spread over the western United States, with a total wind power capacity of 960 GW. Error analyses for individual sites and for specific balancing areas are performed using the database, quantifying the fit to theoretical distributions through goodness-of-fit metrics. Insights into wind-power forecasting error distributions are established for various levels of temporal and spatial resolution, contrasts made among the frequency distribution alternatives, and recommendations put forth for harnessing the results. Empirical data are used to produce more realistic site-level forecasts than previously employed, such that higher resolution operational studies are possible. This research feeds into a larger work of renewable integration through the links wind power forecasting has with various operational issues, such as stochastic unit commitment and flexible reserve level determination.

  15. Wind Power Forecasting Error Distributions: An International Comparison

    DEFF Research Database (Denmark)

    Hodge, Bri-Mathias; Lew, Debra; Milligan, Michael

    2012-01-01

    Wind power forecasting is essential for greater penetration of wind power into electricity systems. Because no wind forecasting system is perfect, a thorough understanding of the errors that may occur is a critical factor for system operation functions, such as the setting of operating reserve...... levels. This paper provides an international comparison of the distribution of wind power forecasting errors from operational systems, based on real forecast data. The paper concludes with an assessment of similarities and differences between the errors observed in different locations....

  16. Use of wind power forecasting in operational decisions.

    Energy Technology Data Exchange (ETDEWEB)

    Botterud, A.; Zhi, Z.; Wang, J.; Bessa, R.J.; Keko, H.; Mendes, J.; Sumaili, J.; Miranda, V. (Decision and Information Sciences); (INESC Porto)

    2011-11-29

    The rapid expansion of wind power gives rise to a number of challenges for power system operators and electricity market participants. The key operational challenge is to efficiently handle the uncertainty and variability of wind power when balancing supply and demand in ths system. In this report, we analyze how wind power forecasting can serve as an efficient tool toward this end. We discuss the current status of wind power forecasting in U.S. electricity markets and develop several methodologies and modeling tools for the use of wind power forecasting in operational decisions, from the perspectives of the system operator as well as the wind power producer. In particular, we focus on the use of probabilistic forecasts in operational decisions. Driven by increasing prices for fossil fuels and concerns about greenhouse gas (GHG) emissions, wind power, as a renewable and clean source of energy, is rapidly being introduced into the existing electricity supply portfolio in many parts of the world. The U.S. Department of Energy (DOE) has analyzed a scenario in which wind power meets 20% of the U.S. electricity demand by 2030, which means that the U.S. wind power capacity would have to reach more than 300 gigawatts (GW). The European Union is pursuing a target of 20/20/20, which aims to reduce greenhouse gas (GHG) emissions by 20%, increase the amount of renewable energy to 20% of the energy supply, and improve energy efficiency by 20% by 2020 as compared to 1990. Meanwhile, China is the leading country in terms of installed wind capacity, and had 45 GW of installed wind power capacity out of about 200 GW on a global level at the end of 2010. The rapid increase in the penetration of wind power into power systems introduces more variability and uncertainty in the electricity generation portfolio, and these factors are the key challenges when it comes to integrating wind power into the electric power grid. Wind power forecasting (WPF) is an important tool to help

  17. Short-term load forecasting of power system

    Science.gov (United States)

    Xu, Xiaobin

    2017-05-01

    In order to ensure the scientific nature of optimization about power system, it is necessary to improve the load forecasting accuracy. Power system load forecasting is based on accurate statistical data and survey data, starting from the history and current situation of electricity consumption, with a scientific method to predict the future development trend of power load and change the law of science. Short-term load forecasting is the basis of power system operation and analysis, which is of great significance to unit combination, economic dispatch and safety check. Therefore, the load forecasting of the power system is explained in detail in this paper. First, we use the data from 2012 to 2014 to establish the partial least squares model to regression analysis the relationship between daily maximum load, daily minimum load, daily average load and each meteorological factor, and select the highest peak by observing the regression coefficient histogram Day maximum temperature, daily minimum temperature and daily average temperature as the meteorological factors to improve the accuracy of load forecasting indicators. Secondly, in the case of uncertain climate impact, we use the time series model to predict the load data for 2015, respectively, the 2009-2014 load data were sorted out, through the previous six years of the data to forecast the data for this time in 2015. The criterion for the accuracy of the prediction is the average of the standard deviations for the prediction results and average load for the previous six years. Finally, considering the climate effect, we use the BP neural network model to predict the data in 2015, and optimize the forecast results on the basis of the time series model.

  18. Power density forecasting device for nuclear power plant

    International Nuclear Information System (INIS)

    Fukuzaki, Takaharu; Kiguchi, Takashi.

    1978-01-01

    Purpose: To attain effective reactor operation in a bwr type reactor by forecasting the power density of the reactor after adjustment and comparing the same with the present status of the reactor by the on-line calculation in a short time. Constitution: The present status for the reactor is estimated in a present status decision section based on a measurement signal from the reactor and it is stored in an operation result collection section. The reactor status after the forecasting is estimated in a forecasting section based on a setting signal from a forecasting condition setting section and it is compared with the result value from the operation results collection section. If the forecast value does not coincide with the result value in the above comparison, the setting value in the forecast condition setting section is changed in the control section. The above procedures are repeated so as to minimize the difference between the forecast value and the result value to thereby exactly forecast the reactor status and operate the reactor effectively. (Moriyama, K.)

  19. A survey on wind power ramp forecasting.

    Energy Technology Data Exchange (ETDEWEB)

    Ferreira, C.; Gama, J.; Matias, L.; Botterud, A.; Wang, J. (Decision and Information Sciences); (INESC Porto)

    2011-02-23

    The increasing use of wind power as a source of electricity poses new challenges with regard to both power production and load balance in the electricity grid. This new source of energy is volatile and highly variable. The only way to integrate such power into the grid is to develop reliable and accurate wind power forecasting systems. Electricity generated from wind power can be highly variable at several different timescales: sub-hourly, hourly, daily, and seasonally. Wind energy, like other electricity sources, must be scheduled. Although wind power forecasting methods are used, the ability to predict wind plant output remains relatively low for short-term operation. Because instantaneous electrical generation and consumption must remain in balance to maintain grid stability, wind power's variability can present substantial challenges when large amounts of wind power are incorporated into a grid system. A critical issue is ramp events, which are sudden and large changes (increases or decreases) in wind power. This report presents an overview of current ramp definitions and state-of-the-art approaches in ramp event forecasting.

  20. Short-term spatio-temporal wind power forecast in robust look-ahead power system dispatch

    KAUST Repository

    Xie, Le

    2014-01-01

    We propose a novel statistical wind power forecast framework, which leverages the spatio-temporal correlation in wind speed and direction data among geographically dispersed wind farms. Critical assessment of the performance of spatio-temporal wind power forecast is performed using realistic wind farm data from West Texas. It is shown that spatio-temporal wind forecast models are numerically efficient approaches to improving forecast quality. By reducing uncertainties in near-term wind power forecasts, the overall cost benefits on system dispatch can be quantified. We integrate the improved forecast with an advanced robust look-ahead dispatch framework. This integrated forecast and economic dispatch framework is tested in a modified IEEE RTS 24-bus system. Numerical simulation suggests that the overall generation cost can be reduced by up to 6% using a robust look-ahead dispatch coupled with spatio-temporal wind forecast as compared with persistent wind forecast models. © 2013 IEEE.

  1. Baseline and Target Values for PV Forecasts: Toward Improved Solar Power Forecasting: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Jie; Hodge, Bri-Mathias; Lu, Siyuan; Hamann, Hendrik F.; Lehman, Brad; Simmons, Joseph; Campos, Edwin; Banunarayanan, Venkat

    2015-08-05

    Accurate solar power forecasting allows utilities to get the most out of the solar resources on their systems. To truly measure the improvements that any new solar forecasting methods can provide, it is important to first develop (or determine) baseline and target solar forecasting at different spatial and temporal scales. This paper aims to develop baseline and target values for solar forecasting metrics. These were informed by close collaboration with utility and independent system operator partners. The baseline values are established based on state-of-the-art numerical weather prediction models and persistence models. The target values are determined based on the reduction in the amount of reserves that must be held to accommodate the uncertainty of solar power output. forecasting metrics. These were informed by close collaboration with utility and independent system operator partners. The baseline values are established based on state-of-the-art numerical weather prediction models and persistence models. The target values are determined based on the reduction in the amount of reserves that must be held to accommodate the uncertainty of solar power output.

  2. The distribution of wind power forecast errors from operational systems

    Energy Technology Data Exchange (ETDEWEB)

    Hodge, Bri-Mathias; Ela, Erik; Milligan, Michael

    2011-07-01

    Wind power forecasting is one important tool in the integration of large amounts of renewable generation into the electricity system. Wind power forecasts from operational systems are not perfect, and thus, an understanding of the forecast error distributions can be important in system operations. In this work, we examine the errors from operational wind power forecasting systems, both for a single wind plant and for an entire interconnection. The resulting error distributions are compared with the normal distribution and the distribution obtained from the persistence forecasting model at multiple timescales. A model distribution is fit to the operational system forecast errors and the potential impact on system operations highlighted through the generation of forecast confidence intervals. (orig.)

  3. Probabilistic forecasting of wind power generation using extreme learning machine

    DEFF Research Database (Denmark)

    Wan, Can; Xu, Zhao; Pinson, Pierre

    2014-01-01

    an extreme learning machine (ELM)-based probabilistic forecasting method for wind power generation. To account for the uncertainties in the forecasting results, several bootstrapmethods have been compared for modeling the regression uncertainty, based on which the pairs bootstrap method is identified......Accurate and reliable forecast of wind power is essential to power system operation and control. However, due to the nonstationarity of wind power series, traditional point forecasting can hardly be accurate, leading to increased uncertainties and risks for system operation. This paper proposes...... with the best performance. Consequently, a new method for prediction intervals formulation based on theELMand the pairs bootstrap is developed.Wind power forecasting has been conducted in different seasons using the proposed approach with the historical wind power time series as the inputs alone. The results...

  4. Probabilistic Wind Power Ramp Forecasting Based on a Scenario Generation Method

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Qin [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Florita, Anthony R [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Krishnan, Venkat K [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Hodge, Brian S [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Cui, Mingjian [University of Texas at Dallas; Feng, Cong [University of Texas at Dallas; Wang, Zhenke [University of Texas at Dallas; Zhang, Jie [University of Texas at Dallas

    2018-02-01

    Wind power ramps (WPRs) are particularly important in the management and dispatch of wind power and currently drawing the attention of balancing authorities. With the aim to reduce the impact of WPRs for power system operations, this paper develops a probabilistic ramp forecasting method based on a large number of simulated scenarios. An ensemble machine learning technique is first adopted to forecast the basic wind power forecasting scenario and calculate the historical forecasting errors. A continuous Gaussian mixture model (GMM) is used to fit the probability distribution function (PDF) of forecasting errors. The cumulative distribution function (CDF) is analytically deduced. The inverse transform method based on Monte Carlo sampling and the CDF is used to generate a massive number of forecasting error scenarios. An optimized swinging door algorithm is adopted to extract all the WPRs from the complete set of wind power forecasting scenarios. The probabilistic forecasting results of ramp duration and start-time are generated based on all scenarios. Numerical simulations on publicly available wind power data show that within a predefined tolerance level, the developed probabilistic wind power ramp forecasting method is able to predict WPRs with a high level of sharpness and accuracy.

  5. Forecasting Canadian nuclear power station construction costs

    International Nuclear Information System (INIS)

    Keng, C.W.K.

    1985-01-01

    Because of the huge volume of capital required to construct a modern electric power generating station, investment decisions have to be made with as complete an understanding of the consequences of the decision as possible. This understanding must be provided by the evaluation of future situations. A key consideration in an evaluation is the financial component. This paper attempts to use an econometric method to forecast the construction costs escalation of a standard Canadian nuclear generating station (NGS). A brief review of the history of Canadian nuclear electric power is provided. The major components of the construction costs of a Canadian NGS are studied and summarized. A database is built and indexes are prepared. Based on these indexes, an econometric forecasting model is constructed using an apparently new econometric methodology of forecasting modelling. Forecasts for a period of 40 years are generated and applications (such as alternative scenario forecasts and range forecasts) to uncertainty assessment and/or decision-making are demonstrated. The indexes, the model, and the forecasts and their applications, to the best of the author's knowledge, are the first for Canadian NGS constructions. (author)

  6. Using Bayes Model Averaging for Wind Power Forecasts

    Science.gov (United States)

    Preede Revheim, Pål; Beyer, Hans Georg

    2014-05-01

    For operational purposes predictions of the forecasts of the lumped output of groups of wind farms spread over larger geographic areas will often be of interest. A naive approach is to make forecasts for each individual site and sum them up to get the group forecast. It is however well documented that a better choice is to use a model that also takes advantage of spatial smoothing effects. It might however be the case that some sites tends to more accurately reflect the total output of the region, either in general or for certain wind directions. It will then be of interest giving these a greater influence over the group forecast. Bayesian model averaging (BMA) is a statistical post-processing method for producing probabilistic forecasts from ensembles. Raftery et al. [1] show how BMA can be used for statistical post processing of forecast ensembles, producing PDFs of future weather quantities. The BMA predictive PDF of a future weather quantity is a weighted average of the ensemble members' PDFs, where the weights can be interpreted as posterior probabilities and reflect the ensemble members' contribution to overall forecasting skill over a training period. In Revheim and Beyer [2] the BMA procedure used in Sloughter, Gneiting and Raftery [3] were found to produce fairly accurate PDFs for the future mean wind speed of a group of sites from the single sites wind speeds. However, when the procedure was attempted applied to wind power it resulted in either problems with the estimation of the parameters (mainly caused by longer consecutive periods of no power production) or severe underestimation (mainly caused by problems with reflecting the power curve). In this paper the problems that arose when applying BMA to wind power forecasting is met through two strategies. First, the BMA procedure is run with a combination of single site wind speeds and single site wind power production as input. This solves the problem with longer consecutive periods where the input data

  7. Forecasting nuclear power supply with Bayesian autoregression

    International Nuclear Information System (INIS)

    Beck, R.; Solow, J.L.

    1994-01-01

    We explore the possibility of forecasting the quarterly US generation of electricity from nuclear power using a Bayesian autoregression model. In terms of forecasting accuracy, this approach compares favorably with both the Department of Energy's current forecasting methodology and their more recent efforts using ARIMA models, and it is extremely easy and inexpensive to implement. (author)

  8. Short-Term Power Plant GHG Emissions Forecasting Model

    International Nuclear Information System (INIS)

    Vidovic, D.

    2016-01-01

    In 2010, the share of greenhouse gas (GHG) emissions from power generation in the total emissions at the global level was about 25 percent. From January 1st, 2013 Croatian facilities have been involved in the European Union Emissions Trading System (EU ETS). The share of the ETS sector in total GHG emissions in Croatia in 2012 was about 30 percent, where power plants and heat generation facilities contributed to almost 50 percent. Since 2013 power plants are obliged to purchase all emission allowances. The paper describes the short-term climate forecasting model of greenhouse gas emissions from power plants while covering the daily load diagram of the system. Forecasting is done on an hourly domain typically for one day, it is possible and more days ahead. Forecasting GHG emissions in this way would enable power plant operators to purchase additional or sell surplus allowances on the market at the time. Example that describes the operation of the above mentioned forecasting model is given at the end of the paper.(author).

  9. INFERENCE AND SENSITIVITY IN STOCHASTIC WIND POWER FORECAST MODELS.

    KAUST Repository

    Elkantassi, Soumaya

    2017-10-03

    Reliable forecasting of wind power generation is crucial to optimal control of costs in generation of electricity with respect to the electricity demand. Here, we propose and analyze stochastic wind power forecast models described by parametrized stochastic differential equations, which introduce appropriate fluctuations in numerical forecast outputs. We use an approximate maximum likelihood method to infer the model parameters taking into account the time correlated sets of data. Furthermore, we study the validity and sensitivity of the parameters for each model. We applied our models to Uruguayan wind power production as determined by historical data and corresponding numerical forecasts for the period of March 1 to May 31, 2016.

  10. INFERENCE AND SENSITIVITY IN STOCHASTIC WIND POWER FORECAST MODELS.

    KAUST Repository

    Elkantassi, Soumaya; Kalligiannaki, Evangelia; Tempone, Raul

    2017-01-01

    Reliable forecasting of wind power generation is crucial to optimal control of costs in generation of electricity with respect to the electricity demand. Here, we propose and analyze stochastic wind power forecast models described by parametrized stochastic differential equations, which introduce appropriate fluctuations in numerical forecast outputs. We use an approximate maximum likelihood method to infer the model parameters taking into account the time correlated sets of data. Furthermore, we study the validity and sensitivity of the parameters for each model. We applied our models to Uruguayan wind power production as determined by historical data and corresponding numerical forecasts for the period of March 1 to May 31, 2016.

  11. Development and testing of improved statistical wind power forecasting methods.

    Energy Technology Data Exchange (ETDEWEB)

    Mendes, J.; Bessa, R.J.; Keko, H.; Sumaili, J.; Miranda, V.; Ferreira, C.; Gama, J.; Botterud, A.; Zhou, Z.; Wang, J. (Decision and Information Sciences); (INESC Porto)

    2011-12-06

    Wind power forecasting (WPF) provides important inputs to power system operators and electricity market participants. It is therefore not surprising that WPF has attracted increasing interest within the electric power industry. In this report, we document our research on improving statistical WPF algorithms for point, uncertainty, and ramp forecasting. Below, we provide a brief introduction to the research presented in the following chapters. For a detailed overview of the state-of-the-art in wind power forecasting, we refer to [1]. Our related work on the application of WPF in operational decisions is documented in [2]. Point forecasts of wind power are highly dependent on the training criteria used in the statistical algorithms that are used to convert weather forecasts and observational data to a power forecast. In Chapter 2, we explore the application of information theoretic learning (ITL) as opposed to the classical minimum square error (MSE) criterion for point forecasting. In contrast to the MSE criterion, ITL criteria do not assume a Gaussian distribution of the forecasting errors. We investigate to what extent ITL criteria yield better results. In addition, we analyze time-adaptive training algorithms and how they enable WPF algorithms to cope with non-stationary data and, thus, to adapt to new situations without requiring additional offline training of the model. We test the new point forecasting algorithms on two wind farms located in the U.S. Midwest. Although there have been advancements in deterministic WPF, a single-valued forecast cannot provide information on the dispersion of observations around the predicted value. We argue that it is essential to generate, together with (or as an alternative to) point forecasts, a representation of the wind power uncertainty. Wind power uncertainty representation can take the form of probabilistic forecasts (e.g., probability density function, quantiles), risk indices (e.g., prediction risk index) or scenarios

  12. Simulation of regional day-ahead PV power forecast scenarios

    DEFF Research Database (Denmark)

    Nuno, Edgar; Koivisto, Matti Juhani; Cutululis, Nicolaos Antonio

    2017-01-01

    Uncertainty associated with Photovoltaic (PV) generation can have a significant impact on real-time planning and operation of power systems. This obstacle is commonly handled using multiple forecast realizations, obtained using for example forecast ensembles and/or probabilistic forecasts, often...... at the expense of a high computational burden. Alternatively, some power system applications may require realistic forecasts rather than actual estimates; able to capture the uncertainty of weatherdriven generation. To this end, we propose a novel methodology to generate day-ahead forecast scenarios of regional...... PV production matching the spatio-temporal characteristics while preserving the statistical properties of actual records....

  13. Estimation of the uncertainty in wind power forecasting

    International Nuclear Information System (INIS)

    Pinson, P.

    2006-03-01

    WIND POWER experiences a tremendous development of its installed capacities in Europe. Though, the intermittence of wind generation causes difficulties in the management of power systems. Also, in the context of the deregulation of electricity markets, wind energy is penalized by its intermittent nature. It is recognized today that the forecasting of wind power for horizons up to 2/3-day ahead eases the integration of wind generation. Wind power forecasts are traditionally provided in the form of point predictions, which correspond to the most-likely power production for a given horizon. That sole information is not sufficient for developing optimal management or trading strategies. Therefore, we investigate on possible ways for estimating the uncertainty of wind power forecasts. The characteristics of the prediction uncertainty are described by a thorough study of the performance of some of the state-of-the-art approaches, and by underlining the influence of some variables e.g. level of predicted power on distributions of prediction errors. Then, a generic method for the estimation of prediction intervals is introduced. This statistical method is non-parametric and utilizes fuzzy logic concepts for integrating expertise on the prediction uncertainty characteristics. By estimating several prediction intervals at once, one obtains predictive distributions of wind power output. The proposed method is evaluated in terms of its reliability, sharpness and resolution. In parallel, we explore the potential use of ensemble predictions for skill forecasting. Wind power ensemble forecasts are obtained either by converting meteorological ensembles (from ECMWF and NCEP) to power or by applying a poor man's temporal approach. A proposal for the definition of prediction risk indices is given, reflecting the disagreement between ensemble members over a set of successive look-ahead times. Such prediction risk indices may comprise a more comprehensive signal on the expected level

  14. The new IEA Wind Task 36 on Wind Power Forecasting

    DEFF Research Database (Denmark)

    Giebel, Gregor; Cline, Joel; Frank, Helmut

    Wind power forecasts have been used operatively for over 20 years. Despite this fact, there are still several possibilities to improve the forecasts, both from the weather prediction side and from the usage of the forecasts. The new International Energy Agency (IEA) Task on Forecasting for Wind E...... forecasts, including probabilistic forecasts. This WP will also organise benchmarks, in cooperation with the IEA Task WakeBench. Thirdly, we will be engaging end users aiming at dissemination of the best practice in the usage of wind power predictions....

  15. Short-Term Solar Collector Power Forecasting

    DEFF Research Database (Denmark)

    Bacher, Peder; Madsen, Henrik; Perers, Bengt

    2011-01-01

    This paper describes a new approach to online forecasting of power output from solar thermal collectors. The method is suited for online forecasting in many applications and in this paper it is applied to predict hourly values of power from a standard single glazed large area flat plate collector...... enabling tracking of changes in the system and in the surrounding conditions, such as decreasing performance due to wear and dirt, and seasonal changes such as leaves on trees. This furthermore facilitates remote monitoring and check of the system....

  16. Introducing distributed learning approaches in wind power forecasting

    DEFF Research Database (Denmark)

    Pinson, Pierre

    2016-01-01

    Renewable energy forecasting is now of core interest to both academics, who continuously propose new forecast methodologies, and forecast users for optimal operations and participation in electricity markets. In view of the increasing amount of data being collected at power generation sites, thanks...

  17. PROBING THE INFLATON: SMALL-SCALE POWER SPECTRUM CONSTRAINTS FROM MEASUREMENTS OF THE COSMIC MICROWAVE BACKGROUND ENERGY SPECTRUM

    International Nuclear Information System (INIS)

    Chluba, Jens; Erickcek, Adrienne L.; Ben-Dayan, Ido

    2012-01-01

    In the early universe, energy stored in small-scale density perturbations is quickly dissipated by Silk damping, a process that inevitably generates μ- and y-type spectral distortions of the cosmic microwave background (CMB). These spectral distortions depend on the shape and amplitude of the primordial power spectrum at wavenumbers k ∼ 4 Mpc –1 . Here, we study constraints on the primordial power spectrum derived from COBE/FIRAS and forecasted for PIXIE. We show that measurements of μ and y impose strong bounds on the integrated small-scale power, and we demonstrate how to compute these constraints using k-space window functions that account for the effects of thermalization and dissipation physics. We show that COBE/FIRAS places a robust upper limit on the amplitude of the small-scale power spectrum. This limit is about three orders of magnitude stronger than the one derived from primordial black holes in the same scale range. Furthermore, this limit could be improved by another three orders of magnitude with PIXIE, potentially opening up a new window to early universe physics. To illustrate the power of these constraints, we consider several generic models for the small-scale power spectrum predicted by different inflation scenarios, including running-mass inflation models and inflation scenarios with episodes of particle production. PIXIE could place very tight constraints on these scenarios, potentially even ruling out running-mass inflation models if no distortion is detected. We also show that inflation models with sub-Planckian field excursion that generate detectable tensor perturbations should simultaneously produce a large CMB spectral distortion, a link that could potentially be established with PIXIE.

  18. Value of Improved Wind Power Forecasting in the Western Interconnection (Poster)

    Energy Technology Data Exchange (ETDEWEB)

    Hodge, B.

    2013-12-01

    Wind power forecasting is a necessary and important technology for incorporating wind power into the unit commitment and dispatch process. It is expected to become increasingly important with higher renewable energy penetration rates and progress toward the smart grid. There is consensus that wind power forecasting can help utility operations with increasing wind power penetration; however, there is far from a consensus about the economic value of improved forecasts. This work explores the value of improved wind power forecasting in the Western Interconnection of the United States.

  19. Forecasting winds over nuclear power plants statistics

    International Nuclear Information System (INIS)

    Marais, Ch.

    1997-01-01

    In the event of an accident at nuclear power plant, it is essential to forecast the wind velocity at the level where the efflux occurs (about 100 m). At present meteorologists refine the wind forecast from the coarse grid of numerical weather prediction (NWP) models. The purpose of this study is to improve the forecasts by developing a statistical adaptation method which corrects the NWP forecasts by using statistical comparisons between wind forecasts and observations. The Multiple Linear Regression method is used here to forecast the 100 m wind at 12 and 24 hours range for three Electricite de France (EDF) sites. It turns out that this approach gives better forecasts than the NWP model alone and is worthy of operational use. (author)

  20. Sharing wind power forecasts in electricity markets: A numerical analysis

    DEFF Research Database (Denmark)

    Exizidis, Lazaros; Pinson, Pierre; Kazempour, Jalal

    2016-01-01

    In an electricity pool with significant share of wind power, all generators including conventional and wind power units are generally scheduled in a day-ahead market based on wind power forecasts. Then, a real-time market is cleared given the updated wind power forecast and fixed day......-ahead decisions to adjust power imbalances. This sequential market-clearing process may cope with serious operational challenges such as severe power shortage in real-time due to erroneous wind power forecasts in day-ahead market. To overcome such situations, several solutions can be considered such as adding...... flexible resources to the system. In this paper, we address another potential solution based on information sharing in which market players share their own wind power forecasts with others in day-ahead market. This solution may improve the functioning of sequential market-clearing process through making...

  1. Advances in electric power and energy systems load and price forecasting

    CERN Document Server

    2017-01-01

    A comprehensive review of state-of-the-art approaches to power systems forecasting from the most respected names in the field, internationally. Advances in Electric Power and Energy Systems is the first book devoted exclusively to a subject of increasing urgency to power systems planning and operations. Written for practicing engineers, researchers, and post-grads concerned with power systems planning and forecasting, this book brings together contributions from many of the world’s foremost names in the field who address a range of critical issues, from forecasting power system load to power system pricing to post-storm service restoration times, river flow forecasting, and more. In a time of ever-increasing energy demands, mounting concerns over the environmental impacts of power generation, and the emergence of new, smart-grid technologies, electricity price forecasting has assumed a prominent role within both the academic and industrial ar nas. Short-run forecasting of electricity prices has become nece...

  2. Hourly weather forecasts for gas turbine power generation

    Directory of Open Access Journals (Sweden)

    G. Giunta

    2017-06-01

    Full Text Available An hourly short-term weather forecast can optimize processes in Combined Cycle Gas Turbine (CCGT plants by helping to reduce imbalance charges on the national power grid. Consequently, a reliable meteorological prediction for a given power plant is crucial for obtaining competitive prices for the electric market, better planning and stock management, sales and supplies of energy sources. The paper discusses the short-term hourly temperature forecasts, at lead time day+1 and day+2, over a period of thirteen months in 2012 and 2013 for six Italian CCGT power plants of 390 MW each (260 MW from the gas turbine and 130 MW from the steam turbine. These CCGT plants are placed in three different Italian climate areas: the Po Valley, the Adriatic coast, and the North Tyrrhenian coast. The meteorological model applied in this study is the eni-Kassandra Meteo Forecast (e‑kmf™, a multi-model approach system to provide probabilistic forecasts with a Kalman filter used to improve accuracy of local temperature predictions. Performance skill scores, computed by the output data of the meteorological model, are compared with local observations, and used to evaluate forecast reliability. In the study, the approach has shown good overall scores encompassing more than 50,000 hourly temperature values. Some differences from one site to another, due to local meteorological phenomena, can affect the short-term forecast performance, with consequent impacts on gas-to-power production and related negative imbalances. For operational application of the methodology in CCGT power plant, the benefits and limits have been successfully identified.

  3. Sharing wind power forecasts in electricity markets: A numerical analysis

    International Nuclear Information System (INIS)

    Exizidis, Lazaros; Kazempour, S. Jalal; Pinson, Pierre; Greve, Zacharie de; Vallée, François

    2016-01-01

    Highlights: • Information sharing among different agents can be beneficial for electricity markets. • System cost decreases by sharing wind power forecasts between different agents. • Market power of wind producer may increase by sharing forecasts with market operator. • Extensive out-of-sample analysis is employed to draw reliable conclusions. - Abstract: In an electricity pool with significant share of wind power, all generators including conventional and wind power units are generally scheduled in a day-ahead market based on wind power forecasts. Then, a real-time market is cleared given the updated wind power forecast and fixed day-ahead decisions to adjust power imbalances. This sequential market-clearing process may cope with serious operational challenges such as severe power shortage in real-time due to erroneous wind power forecasts in day-ahead market. To overcome such situations, several solutions can be considered such as adding flexible resources to the system. In this paper, we address another potential solution based on information sharing in which market players share their own wind power forecasts with others in day-ahead market. This solution may improve the functioning of sequential market-clearing process through making more informed day-ahead schedules, which reduces the need for balancing resources in real-time operation. This paper numerically evaluates the potential value of sharing forecasts for the whole system in terms of system cost reduction. Besides, its impact on each market player’s profit is analyzed. The framework of this study is based on a stochastic two-stage market setup and complementarity modeling, which allows us to gain further insights into information sharing impacts.

  4. Constraining primordial non-Gaussianity with bispectrum and power spectrum from upcoming optical and radio surveys

    Science.gov (United States)

    Karagiannis, Dionysios; Lazanu, Andrei; Liguori, Michele; Raccanelli, Alvise; Bartolo, Nicola; Verde, Licia

    2018-07-01

    We forecast constraints on primordial non-Gaussianity (PNG) and bias parameters from measurements of galaxy power spectrum and bispectrum in future radio continuum and optical surveys. In the galaxy bispectrum, we consider a comprehensive list of effects, including the bias expansion for non-Gaussian initial conditions up to second order, redshift space distortions, redshift uncertainties and theoretical errors. These effects are all combined in a single PNG forecast for the first time. Moreover, we improve the bispectrum modelling over previous forecasts, by accounting for trispectrum contributions. All effects have an impact on final predicted bounds, which varies with the type of survey. We find that the bispectrum can lead to improvements up to a factor ˜5 over bounds based on the power spectrum alone, leading to significantly better constraints for local-type PNG, with respect to current limits from Planck. Future radio and photometric surveys could obtain a measurement error of σ (f_{NL}^{loc}) ≈ 0.2. In the case of equilateral PNG, galaxy bispectrum can improve upon present bounds only if significant improvements in the redshift determinations of future, large volume, photometric or radio surveys could be achieved. For orthogonal non-Gaussianity, expected constraints are generally comparable to current ones.

  5. Probabilistic Wind Power Ramp Forecasting Based on a Scenario Generation Method: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Qin [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Florita, Anthony R [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Krishnan, Venkat K [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Hodge, Brian S [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Cui, Mingjian [Univ. of Texas-Dallas, Richardson, TX (United States); Feng, Cong [Univ. of Texas-Dallas, Richardson, TX (United States); Wang, Zhenke [Univ. of Texas-Dallas, Richardson, TX (United States); Zhang, Jie [Univ. of Texas-Dallas, Richardson, TX (United States)

    2017-08-31

    Wind power ramps (WPRs) are particularly important in the management and dispatch of wind power, and they are currently drawing the attention of balancing authorities. With the aim to reduce the impact of WPRs for power system operations, this paper develops a probabilistic ramp forecasting method based on a large number of simulated scenarios. An ensemble machine learning technique is first adopted to forecast the basic wind power forecasting scenario and calculate the historical forecasting errors. A continuous Gaussian mixture model (GMM) is used to fit the probability distribution function (PDF) of forecasting errors. The cumulative distribution function (CDF) is analytically deduced. The inverse transform method based on Monte Carlo sampling and the CDF is used to generate a massive number of forecasting error scenarios. An optimized swinging door algorithm is adopted to extract all the WPRs from the complete set of wind power forecasting scenarios. The probabilistic forecasting results of ramp duration and start time are generated based on all scenarios. Numerical simulations on publicly available wind power data show that within a predefined tolerance level, the developed probabilistic wind power ramp forecasting method is able to predict WPRs with a high level of sharpness and accuracy.

  6. Future wind power forecast errors, need for regulating power, and costs in the Swedish system

    Energy Technology Data Exchange (ETDEWEB)

    Carlsson, Fredrik [Vattenfall Research and Development AB, Stockholm (Sweden). Power Technology

    2011-07-01

    Wind power is one of the renewable energy sources in the electricity system that grows most rapid in Sweden. There are however two market challenges that need to be addressed with a higher proportion of wind power - that is variability and predictability. Predictability is important since the spot market Nord Pool Spot requires forecasts of production 12 - 36 hours ahead. The forecast errors must be regulated with regulating power, which is expensive for the actors causing the forecast errors. This paper has investigated a number of scenarios with 10 - 55 TWh of wind power installed in the Swedish system. The focus has been on a base scenario with 10 TWh new wind power consisting of 3,5 GW new wind power and 1,5 GW already installed power, which gives 5 GW. The results show that the costs for the forecast errors will increase as more intermittent production is installed. However, the increase can be limited by for instance trading on intraday market or increase quality of forecasts. (orig.)

  7. Skill forecasting from ensemble predictions of wind power

    DEFF Research Database (Denmark)

    Pinson, Pierre; Nielsen, Henrik Aalborg; Madsen, Henrik

    2009-01-01

    Optimal management and trading of wind generation calls for the providing of uncertainty estimates along with the commonly provided short-term wind power point predictions. Alternative approaches for the use of probabilistic forecasting are introduced. More precisely, focus is given to prediction...... risk indices aiming to give a comprehensive signal on the expected level of forecast uncertainty. Ensemble predictions of wind generation are used as input. A proposal for the definition of prediction risk indices is given. Such skill forecasts are based on the spread of ensemble forecasts (i.e. a set...... of alternative scenarios for the coming period) for a single prediction horizon or over a took-ahead period. It is shown on the test case of a Danish offshore wind farm how these prediction risk indices may be related to several levels of forecast uncertainty (and potential energy imbalances). Wind power...

  8. Comparison of two new short-term wind-power forecasting systems

    Energy Technology Data Exchange (ETDEWEB)

    Ramirez-Rosado, Ignacio J. [Department of Electrical Engineering, University of Zaragoza, Zaragoza (Spain); Fernandez-Jimenez, L. Alfredo [Department of Electrical Engineering, University of La Rioja, Logrono (Spain); Monteiro, Claudio; Sousa, Joao; Bessa, Ricardo [FEUP, Fac. Engenharia Univ. Porto (Portugal)]|[INESC - Instituto de Engenharia de Sistemas e Computadores do Porto, Porto (Portugal)

    2009-07-15

    This paper presents a comparison of two new advanced statistical short-term wind-power forecasting systems developed by two independent research teams. The input variables used in both systems were the same: forecasted meteorological variable values obtained from a numerical weather prediction model; and electric power-generation registers from the SCADA system of the wind farm. Both systems are described in detail and the forecasting results compared, revealing great similarities, although the proposed structures of the two systems are different. The forecast horizon for both systems is 72 h, allowing the use of the forecasted values in electric market operations, as diary and intra-diary power generation bid offers, and in wind-farm maintenance planning. (author)

  9. Device for forecasting reactor power-up routes

    International Nuclear Information System (INIS)

    Fukuzaki, Takaharu.

    1980-01-01

    Purpose: To improve the reliability and forecasting accuracy for a device forecasting the change of the state on line in BWR type reactors. Constitution: The present state in a nuclear reactor is estimated in a present state judging section based on measuring signals for thermal power, core flow rate, control rod density and the like from the nuclear reactor, and the estimated results are accumulated in an operation result collecting section. While on the other hand, a forecasting section forecasts the future state in the reactor based on the signals from the forecasting condition setting section. The actual result values from the collecting section and the forecasting results are compared to each other. If they are not equal, new setting signals are outputted from the setting section to perform the forecasting again. These procedures are repeated till the difference between the forecast results and the actual result values is minimized, by which accurate forecasting for the state of the reactor is made possible. (Furukawa, Y.)

  10. The effects of forecast errors on the merchandising of wind power

    International Nuclear Information System (INIS)

    Roon, Serafin von

    2012-01-01

    A permanent balance between consumption and generation is essential for a stable supply of electricity. In order to ensure this balance, all relevant load data have to be announced for the following day. Consequently, a day-ahead forecast of the wind power generation is required, which also forms the basis for the sale of the wind power at the wholesale market. The main subject of the study is the short-term power supply, which compensates errors in wind power forecasting for balancing the wind power forecast errors at short notice. These forecast errors effects the revenues and the expenses by selling and buying power in the day-ahead, intraday and balance energy market. These price effects resulting from the forecast errors are derived from an empirical analysis. In a scenario for the year 2020 the potential of conventional power plants to supply power at short notice is evaluated from a technical and economic point of view by a time series analysis and a unit commitment simulation.

  11. Short time ahead wind power production forecast

    International Nuclear Information System (INIS)

    Sapronova, Alla; Meissner, Catherine; Mana, Matteo

    2016-01-01

    An accurate prediction of wind power output is crucial for efficient coordination of cooperative energy production from different sources. Long-time ahead prediction (from 6 to 24 hours) of wind power for onshore parks can be achieved by using a coupled model that would bridge the mesoscale weather prediction data and computational fluid dynamics. When a forecast for shorter time horizon (less than one hour ahead) is anticipated, an accuracy of a predictive model that utilizes hourly weather data is decreasing. That is because the higher frequency fluctuations of the wind speed are lost when data is averaged over an hour. Since the wind speed can vary up to 50% in magnitude over a period of 5 minutes, the higher frequency variations of wind speed and direction have to be taken into account for an accurate short-term ahead energy production forecast. In this work a new model for wind power production forecast 5- to 30-minutes ahead is presented. The model is based on machine learning techniques and categorization approach and using the historical park production time series and hourly numerical weather forecast. (paper)

  12. Short time ahead wind power production forecast

    Science.gov (United States)

    Sapronova, Alla; Meissner, Catherine; Mana, Matteo

    2016-09-01

    An accurate prediction of wind power output is crucial for efficient coordination of cooperative energy production from different sources. Long-time ahead prediction (from 6 to 24 hours) of wind power for onshore parks can be achieved by using a coupled model that would bridge the mesoscale weather prediction data and computational fluid dynamics. When a forecast for shorter time horizon (less than one hour ahead) is anticipated, an accuracy of a predictive model that utilizes hourly weather data is decreasing. That is because the higher frequency fluctuations of the wind speed are lost when data is averaged over an hour. Since the wind speed can vary up to 50% in magnitude over a period of 5 minutes, the higher frequency variations of wind speed and direction have to be taken into account for an accurate short-term ahead energy production forecast. In this work a new model for wind power production forecast 5- to 30-minutes ahead is presented. The model is based on machine learning techniques and categorization approach and using the historical park production time series and hourly numerical weather forecast.

  13. Power plant asset market evaluations: Forecasting the costs of power production

    Energy Technology Data Exchange (ETDEWEB)

    Lefton, S A; Grunsrud, G P [Aptech Engineering Services, Inc., Sunnyvale, CA (United States)

    1999-12-31

    This presentation discusses the process of evaluating and valuing power plants for sale. It describes a method to forecast the future costs at a power plant using a portion of the past fixed costs, variable energy costs, and most importantly the variable cycling-related wear-and-tear costs. The presentation then discusses how to best determine market share, expected revenues, and then to forecast plant future costs based on future expected unit cycling operations. The presentation concludes with a section on recommendations to power plant buyers or sellers on how to manage the power plant asset and how to increase its market value. (orig.) 4 refs.

  14. Power plant asset market evaluations: Forecasting the costs of power production

    Energy Technology Data Exchange (ETDEWEB)

    Lefton, S.A.; Grunsrud, G.P. [Aptech Engineering Services, Inc., Sunnyvale, CA (United States)

    1998-12-31

    This presentation discusses the process of evaluating and valuing power plants for sale. It describes a method to forecast the future costs at a power plant using a portion of the past fixed costs, variable energy costs, and most importantly the variable cycling-related wear-and-tear costs. The presentation then discusses how to best determine market share, expected revenues, and then to forecast plant future costs based on future expected unit cycling operations. The presentation concludes with a section on recommendations to power plant buyers or sellers on how to manage the power plant asset and how to increase its market value. (orig.) 4 refs.

  15. Power plant asset market evaluations: Forecasting the costs of power production

    International Nuclear Information System (INIS)

    Lefton, S.A.; Grunsrud, G.P.

    1998-01-01

    This presentation discusses the process of evaluating and valuing power plants for sale. It describes a method to forecast the future costs at a power plant using a portion of the past fixed costs, variable energy costs, and most importantly the variable cycling-related wear-and-tear costs. The presentation then discusses how to best determine market share, expected revenues, and then to forecast plant future costs based on future expected unit cycling operations. The presentation concludes with a section on recommendations to power plant buyers or sellers on how to manage the power plant asset and how to increase its market value. (orig.) 4 refs

  16. The economic value of accurate wind power forecasting to utilities

    Energy Technology Data Exchange (ETDEWEB)

    Watson, S J [Rutherford Appleton Lab., Oxfordshire (United Kingdom); Giebel, G; Joensen, A [Risoe National Lab., Dept. of Wind Energy and Atmospheric Physics, Roskilde (Denmark)

    1999-03-01

    With increasing penetrations of wind power, the need for accurate forecasting is becoming ever more important. Wind power is by its very nature intermittent. For utility schedulers this presents its own problems particularly when the penetration of wind power capacity in a grid reaches a significant level (>20%). However, using accurate forecasts of wind power at wind farm sites, schedulers are able to plan the operation of conventional power capacity to accommodate the fluctuating demands of consumers and wind farm output. The results of a study to assess the value of forecasting at several potential wind farm sites in the UK and in the US state of Iowa using the Reading University/Rutherford Appleton Laboratory National Grid Model (NGM) are presented. The results are assessed for different types of wind power forecasting, namely: persistence, optimised numerical weather prediction or perfect forecasting. In particular, it will shown how the NGM has been used to assess the value of numerical weather prediction forecasts from the Danish Meteorological Institute model, HIRLAM, and the US Nested Grid Model, which have been `site tailored` by the use of the linearized flow model WA{sup s}P and by various Model output Statistics (MOS) and autoregressive techniques. (au)

  17. Scheduled Operation of PV Power Station Considering Solar Radiation Forecast Error

    Science.gov (United States)

    Takayama, Satoshi; Hara, Ryoichi; Kita, Hiroyuki; Ito, Takamitsu; Ueda, Yoshinobu; Saito, Yutaka; Takitani, Katsuyuki; Yamaguchi, Koji

    Massive penetration of photovoltaic generation (PV) power stations may cause some serious impacts on a power system operation due to their volatile and unpredictable output. Growth of uncertainty may require larger operating reserve capacity and regulating capacity. Therefore, in order to utilize a PV power station as an alternative for an existing power plant, improvement in controllability and adjustability of station output become very important factor. Purpose of this paper is to develop the scheduled operation technique using a battery system (NAS battery) and the meteorological forecast. The performance of scheduled operation strongly depends on the accuracy of solar radiation forecast. However, the solar radiation forecast contains error. This paper proposes scheduling method and rescheduling method considering the trend of forecast error. More specifically, the forecast error scenario is modeled by means of the clustering analysis of the past actual forecast error. Validity and effectiveness of the proposed method is ascertained through computational simulations using the actual PV generation data monitored at the Wakkanai PV power station and solar radiation forecast data provided by the Japan Weather Association.

  18. Short-term wind power forecasting: probabilistic and space-time aspects

    DEFF Research Database (Denmark)

    Tastu, Julija

    work deals with the proposal and evaluation of new mathematical models and forecasting methods for short-term wind power forecasting, accounting for space-time dynamics based on geographically distributed information. Different forms of power predictions are considered, starting from traditional point...... into the corresponding models are analysed. As a final step, emphasis is placed on generating space-time trajectories: this calls for the prediction of joint multivariate predictive densities describing wind power generation at a number of distributed locations and for a number of successive lead times. In addition......Optimal integration of wind energy into power systems calls for high quality wind power predictions. State-of-the-art forecasting systems typically provide forecasts for every location individually, without taking into account information coming from the neighbouring territories. It is however...

  19. Probabilistic Wind Power Forecasting with Hybrid Artificial Neural Networks

    DEFF Research Database (Denmark)

    Wan, Can; Song, Yonghua; Xu, Zhao

    2016-01-01

    probabilities of prediction errors provide an alternative yet effective solution. This article proposes a hybrid artificial neural network approach to generate prediction intervals of wind power. An extreme learning machine is applied to conduct point prediction of wind power and estimate model uncertainties...... via a bootstrap technique. Subsequently, the maximum likelihood estimation method is employed to construct a distinct neural network to estimate the noise variance of forecasting results. The proposed approach has been tested on multi-step forecasting of high-resolution (10-min) wind power using...... actual wind power data from Denmark. The numerical results demonstrate that the proposed hybrid artificial neural network approach is effective and efficient for probabilistic forecasting of wind power and has high potential in practical applications....

  20. Urban Saturated Power Load Analysis Based on a Novel Combined Forecasting Model

    Directory of Open Access Journals (Sweden)

    Huiru Zhao

    2015-03-01

    Full Text Available Analysis of urban saturated power loads is helpful to coordinate urban power grid construction and economic social development. There are two different kinds of forecasting models: the logistic curve model focuses on the growth law of the data itself, while the multi-dimensional forecasting model considers several influencing factors as the input variables. To improve forecasting performance, a novel combined forecasting model for saturated power load analysis was proposed in this paper, which combined the above two models. Meanwhile, the weights of these two models in the combined forecasting model were optimized by employing a fruit fly optimization algorithm. Using Hubei Province as the example, the effectiveness of the proposed combined forecasting model was verified, demonstrating a higher forecasting accuracy. The analysis result shows that the power load of Hubei Province will reach saturation in 2039, and the annual maximum power load will reach about 78,630 MW. The results obtained from this proposed hybrid urban saturated power load analysis model can serve as a reference for sustainable development for urban power grids, regional economies, and society at large.

  1. Developing a Local Neurofuzzy Model for Short-Term Wind Power Forecasting

    Directory of Open Access Journals (Sweden)

    E. Faghihnia

    2014-01-01

    Full Text Available Large scale integration of wind generation capacity into power systems introduces operational challenges due to wind power uncertainty and variability. Therefore, accurate wind power forecast is important for reliable and economic operation of the power systems. Complexities and nonlinearities exhibited by wind power time series necessitate use of elaborative and sophisticated approaches for wind power forecasting. In this paper, a local neurofuzzy (LNF approach, trained by the polynomial model tree (POLYMOT learning algorithm, is proposed for short-term wind power forecasting. The LNF approach is constructed based on the contribution of local polynomial models which can efficiently model wind power generation. Data from Sotavento wind farm in Spain was used to validate the proposed LNF approach. Comparison between performance of the proposed approach and several recently published approaches illustrates capability of the LNF model for accurate wind power forecasting.

  2. Short-Term Wind Power Interval Forecasting Based on an EEMD-RT-RVM Model

    Directory of Open Access Journals (Sweden)

    Haixiang Zang

    2016-01-01

    Full Text Available Accurate short-term wind power forecasting is important for improving the security and economic success of power grids. Existing wind power forecasting methods are mostly types of deterministic point forecasting. Deterministic point forecasting is vulnerable to forecasting errors and cannot effectively deal with the random nature of wind power. In order to solve the above problems, we propose a short-term wind power interval forecasting model based on ensemble empirical mode decomposition (EEMD, runs test (RT, and relevance vector machine (RVM. First, in order to reduce the complexity of data, the original wind power sequence is decomposed into a plurality of intrinsic mode function (IMF components and residual (RES component by using EEMD. Next, we use the RT method to reconstruct the components and obtain three new components characterized by the fine-to-coarse order. Finally, we obtain the overall forecasting results (with preestablished confidence levels by superimposing the forecasting results of each new component. Our results show that, compared with existing methods, our proposed short-term interval forecasting method has less forecasting errors, narrower interval widths, and larger interval coverage percentages. Ultimately, our forecasting model is more suitable for engineering applications and other forecasting methods for new energy.

  3. Aggregated wind power generation probabilistic forecasting based on particle filter

    International Nuclear Information System (INIS)

    Li, Pai; Guan, Xiaohong; Wu, Jiang

    2015-01-01

    Highlights: • A new method for probabilistic forecasting of aggregated wind power generation. • A dynamic system is established based on a numerical weather prediction model. • The new method handles the non-Gaussian and time-varying wind power uncertainties. • Particle filter is applied to forecast predictive densities of wind generation. - Abstract: Probability distribution of aggregated wind power generation in a region is one of important issues for power system daily operation. This paper presents a novel method to forecast the predictive densities of the aggregated wind power generation from several geographically distributed wind farms, considering the non-Gaussian and non-stationary characteristics in wind power uncertainties. Based on a mesoscale numerical weather prediction model, a dynamic system is established to formulate the relationship between the atmospheric and near-surface wind fields of geographically distributed wind farms. A recursively backtracking framework based on the particle filter is applied to estimate the atmospheric state with the near-surface wind power generation measurements, and to forecast the possible samples of the aggregated wind power generation. The predictive densities of the aggregated wind power generation are then estimated based on these predicted samples by a kernel density estimator. In case studies, the new method presented is tested on a 9 wind farms system in Midwestern United States. The testing results that the new method can provide competitive interval forecasts for the aggregated wind power generation with conventional statistical based models, which validates the effectiveness of the new method

  4. Hour-Ahead Wind Speed and Power Forecasting Using Empirical Mode Decomposition

    Directory of Open Access Journals (Sweden)

    Ying-Yi Hong

    2013-11-01

    Full Text Available Operation of wind power generation in a large farm is quite challenging in a smart grid owing to uncertain weather conditions. Consequently, operators must accurately forecast wind speed/power in the dispatch center to carry out unit commitment, real power scheduling and economic dispatch. This work presents a novel method based on the integration of empirical mode decomposition (EMD with artificial neural networks (ANN to forecast the short-term (1 h ahead wind speed/power. First, significant parameters for training the ANN are identified using the correlation coefficients. These significant parameters serve as inputs of the ANN. Owing to the volatile and intermittent wind speed/power, the historical time series of wind speed/power is decomposed into several intrinsic mode functions (IMFs and a residual function through EMD. Each IMF becomes less volatile and therefore increases the accuracy of the neural network. The final forecasting results are achieved by aggregating all individual forecasting results from all IMFs and their corresponding residual functions. Real data related to the wind speed and wind power measured at a wind-turbine generator in Taiwan are used for simulation. The wind speed forecasting and wind power forecasting for the four seasons are studied. Comparative studies between the proposed method and traditional methods (i.e., artificial neural network without EMD, autoregressive integrated moving average (ARIMA, and persistence method are also introduced.

  5. Canadian nuclear power plant construction cost forecast and analysis

    International Nuclear Information System (INIS)

    Keng, C.W.K.

    1985-01-01

    Because of the huge volume of capital required to construct a modern electric power generating station, investment decisions have to be made with as complete an understanding of the consequence of the decision as possible. This understanding must be provided by the evaluation of the situation to take place in the future. This paper attempts to use an econometric method to forecast the construction costs escalation of a standard Canadian nuclear generating station (NGS). A review of the history of Canadian nuclear electric power is provided. The major components of the construction costs of a Canadian NGS are studied and summarized. A data base is built and indexes are prepared. Based on these indexes an econometric forecasting model is constructed using an apparently new econometric methodology of forecasting modelling. Forecasts for a period of forty years are generated and applications of alternative scenario forecasts and range forecasts to uncertainty assessment are demonstrated. The indexes, the model, and the forecasts and their applications, to the best of the author's knowledge, are the very first ever done for Canadian NGS constructions

  6. Using ensemble forecasting for wind power

    Energy Technology Data Exchange (ETDEWEB)

    Giebel, G.; Landberg, L.; Badger, J. [Risoe National Lab., Roskilde (Denmark); Sattler, K.

    2003-07-01

    Short-term prediction of wind power has a long tradition in Denmark. It is an essential tool for the operators to keep the grid from becoming unstable in a region like Jutland, where more than 27% of the electricity consumption comes from wind power. This means that the minimum load is already lower than the maximum production from wind energy alone. Danish utilities have therefore used short-term prediction of wind energy since the mid-90ies. However, the accuracy is still far from being sufficient in the eyes of the utilities (used to have load forecasts accurate to within 5% on a one-week horizon). The Ensemble project tries to alleviate the dependency of the forecast quality on one model by using multiple models, and also will investigate the possibilities of using the model spread of multiple models or of dedicated ensemble runs for a prediction of the uncertainty of the forecast. Usually, short-term forecasting works (especially for the horizon beyond 6 hours) by gathering input from a Numerical Weather Prediction (NWP) model. This input data is used together with online data in statistical models (this is the case eg in Zephyr/WPPT) to yield the output of the wind farms or of a whole region for the next 48 hours (only limited by the NWP model horizon). For the accuracy of the final production forecast, the accuracy of the NWP prediction is paramount. While many efforts are underway to increase the accuracy of the NWP forecasts themselves (which ultimately are limited by the amount of computing power available, the lack of a tight observational network on the Atlantic and limited physics modelling), another approach is to use ensembles of different models or different model runs. This can be either an ensemble of different models output for the same area, using different data assimilation schemes and different model physics, or a dedicated ensemble run by a large institution, where the same model is run with slight variations in initial conditions and

  7. A methodology for Electric Power Load Forecasting

    Directory of Open Access Journals (Sweden)

    Eisa Almeshaiei

    2011-06-01

    Full Text Available Electricity demand forecasting is a central and integral process for planning periodical operations and facility expansion in the electricity sector. Demand pattern is almost very complex due to the deregulation of energy markets. Therefore, finding an appropriate forecasting model for a specific electricity network is not an easy task. Although many forecasting methods were developed, none can be generalized for all demand patterns. Therefore, this paper presents a pragmatic methodology that can be used as a guide to construct Electric Power Load Forecasting models. This methodology is mainly based on decomposition and segmentation of the load time series. Several statistical analyses are involved to study the load features and forecasting precision such as moving average and probability plots of load noise. Real daily load data from Kuwaiti electric network are used as a case study. Some results are reported to guide forecasting future needs of this network.

  8. Forecast Inaccuracies in Power Plant Projects From Project Managers' Perspectives

    Science.gov (United States)

    Sanabria, Orlando

    Guided by organizational theory, this phenomenological study explored the factors affecting forecast preparation and inaccuracies during the construction of fossil fuel-fired power plants in the United States. Forecast inaccuracies can create financial stress and uncertain profits during the project construction phase. A combination of purposeful and snowball sampling supported the selection of participants. Twenty project managers with over 15 years of experience in power generation and project experience across the United States were interviewed within a 2-month period. From the inductive codification and descriptive analysis, 5 themes emerged: (a) project monitoring, (b) cost control, (c) management review frequency, (d) factors to achieve a precise forecast, and (e) factors causing forecast inaccuracies. The findings of the study showed the factors necessary to achieve a precise forecast includes a detailed project schedule, accurate labor cost estimates, monthly project reviews and risk assessment, and proper utilization of accounting systems to monitor costs. The primary factors reported as causing forecast inaccuracies were cost overruns by subcontractors, scope gaps, labor cost and availability of labor, and equipment and material cost. Results of this study could improve planning accuracy and the effective use of resources during construction of power plants. The study results could contribute to social change by providing a framework to project managers to lessen forecast inaccuracies, and promote construction of power plants that will generate employment opportunities and economic development.

  9. Space-time wind speed forecasting for improved power system dispatch

    KAUST Repository

    Zhu, Xinxin

    2014-02-27

    To support large-scale integration of wind power into electric energy systems, state-of-the-art wind speed forecasting methods should be able to provide accurate and adequate information to enable efficient, reliable, and cost-effective scheduling of wind power. Here, we incorporate space-time wind forecasts into electric power system scheduling. First, we propose a modified regime-switching, space-time wind speed forecasting model that allows the forecast regimes to vary with the dominant wind direction and with the seasons, hence avoiding a subjective choice of regimes. Then, results from the wind forecasts are incorporated into a power system economic dispatch model, the cost of which is used as a loss measure of the quality of the forecast models. This, in turn, leads to cost-effective scheduling of system-wide wind generation. Potential economic benefits arise from the system-wide generation of cost savings and from the ancillary service cost savings. We illustrate the economic benefits using a test system in the northwest region of the United States. Compared with persistence and autoregressive models, our model suggests that cost savings from integration of wind power could be on the scale of tens of millions of dollars annually in regions with high wind penetration, such as Texas and the Pacific northwest. © 2014 Sociedad de Estadística e Investigación Operativa.

  10. Short-Term Forecasting of Loads and Wind Power for Latvian Power System: Accuracy and Capacity of the Developed Tools

    Directory of Open Access Journals (Sweden)

    Radziukynas V.

    2016-04-01

    Full Text Available The paper analyses the performance results of the recently developed short-term forecasting suit for the Latvian power system. The system load and wind power are forecasted using ANN and ARIMA models, respectively, and the forecasting accuracy is evaluated in terms of errors, mean absolute errors and mean absolute percentage errors. The investigation of influence of additional input variables on load forecasting errors is performed. The interplay of hourly loads and wind power forecasting errors is also evaluated for the Latvian power system with historical loads (the year 2011 and planned wind power capacities (the year 2023.

  11. Short-Term Forecasting of Loads and Wind Power for Latvian Power System: Accuracy and Capacity of the Developed Tools

    Science.gov (United States)

    Radziukynas, V.; Klementavičius, A.

    2016-04-01

    The paper analyses the performance results of the recently developed short-term forecasting suit for the Latvian power system. The system load and wind power are forecasted using ANN and ARIMA models, respectively, and the forecasting accuracy is evaluated in terms of errors, mean absolute errors and mean absolute percentage errors. The investigation of influence of additional input variables on load forecasting errors is performed. The interplay of hourly loads and wind power forecasting errors is also evaluated for the Latvian power system with historical loads (the year 2011) and planned wind power capacities (the year 2023).

  12. An overview of wind power forecast types and their use in large-scale integration of wind power

    Energy Technology Data Exchange (ETDEWEB)

    Nielsen, Henrik Aalborg; Nielsen, Torben Skov [ENFOR A/S, Horslholm (Denmark); Madsen, Henrik [Technical Univ. of Denmark, Lyngby (Denmark). Informatics and Mathematical Modelling

    2011-07-01

    Wind power forecast characteristics are described and it is shown how analyses of actual decision problems can be used to derive the forecast characteristics important in a given situation. Generally, characteristics related to resolution in space and time, together with the required maximal forecast horizon are easily identified. However, identification of forecast characteristics required for optimal decision support requires a more thorough investigation, which is illustrated by examples. Generally, quantile forecasts of the future wind power production are required, but the transformation of a quantile forecast into an actual decisions is highly dependent on the precise formulation of the decision problem. Furthermore, when consequences of neighbouring time steps interact, quantile forecasts are not sufficient. It is argued that a general solution in such cases is to base the decision on reliable scenarios of the future wind power production. (orig.)

  13. Short-Term Wind Power Forecasting Using the Enhanced Particle Swarm Optimization Based Hybrid Method

    Directory of Open Access Journals (Sweden)

    Wen-Yeau Chang

    2013-09-01

    Full Text Available High penetration of wind power in the electricity system provides many challenges to power system operators, mainly due to the unpredictability and variability of wind power generation. Although wind energy may not be dispatched, an accurate forecasting method of wind speed and power generation can help power system operators reduce the risk of an unreliable electricity supply. This paper proposes an enhanced particle swarm optimization (EPSO based hybrid forecasting method for short-term wind power forecasting. The hybrid forecasting method combines the persistence method, the back propagation neural network, and the radial basis function (RBF neural network. The EPSO algorithm is employed to optimize the weight coefficients in the hybrid forecasting method. To demonstrate the effectiveness of the proposed method, the method is tested on the practical information of wind power generation of a wind energy conversion system (WECS installed on the Taichung coast of Taiwan. Comparisons of forecasting performance are made with the individual forecasting methods. Good agreements between the realistic values and forecasting values are obtained; the test results show the proposed forecasting method is accurate and reliable.

  14. Electric power systems advanced forecasting techniques and optimal generation scheduling

    CERN Document Server

    Catalão, João P S

    2012-01-01

    Overview of Electric Power Generation SystemsCláudio MonteiroUncertainty and Risk in Generation SchedulingRabih A. JabrShort-Term Load ForecastingAlexandre P. Alves da Silva and Vitor H. FerreiraShort-Term Electricity Price ForecastingNima AmjadyShort-Term Wind Power ForecastingGregor Giebel and Michael DenhardPrice-Based Scheduling for GencosGovinda B. Shrestha and Songbo QiaoOptimal Self-Schedule of a Hydro Producer under UncertaintyF. Javier Díaz and Javie

  15. Applying of forecasting at decision making in power systems

    International Nuclear Information System (INIS)

    Sapundjiev, G.

    2007-01-01

    The problems concerning forecast and decision making are analyzed. The typical tasks arising in the forecasting process of the power systems with hierarchical structure formulated and brought to formal description

  16. An analog ensemble for short-term probabilistic solar power forecast

    International Nuclear Information System (INIS)

    Alessandrini, S.; Delle Monache, L.; Sperati, S.; Cervone, G.

    2015-01-01

    Highlights: • A novel method for solar power probabilistic forecasting is proposed. • The forecast accuracy does not depend on the nominal power. • The impact of climatology on forecast accuracy is evaluated. - Abstract: The energy produced by photovoltaic farms has a variable nature depending on astronomical and meteorological factors. The former are the solar elevation and the solar azimuth, which are easily predictable without any uncertainty. The amount of liquid water met by the solar radiation within the troposphere is the main meteorological factor influencing the solar power production, as a fraction of short wave solar radiation is reflected by the water particles and cannot reach the earth surface. The total cloud cover is a meteorological variable often used to indicate the presence of liquid water in the troposphere and has a limited predictability, which is also reflected on the global horizontal irradiance and, as a consequence, on solar photovoltaic power prediction. This lack of predictability makes the solar energy integration into the grid challenging. A cost-effective utilization of solar energy over a grid strongly depends on the accuracy and reliability of the power forecasts available to the Transmission System Operators (TSOs). Furthermore, several countries have in place legislation requiring solar power producers to pay penalties proportional to the errors of day-ahead energy forecasts, which makes the accuracy of such predictions a determining factor for producers to reduce their economic losses. Probabilistic predictions can provide accurate deterministic forecasts along with a quantification of their uncertainty, as well as a reliable estimate of the probability to overcome a certain production threshold. In this paper we propose the application of an analog ensemble (AnEn) method to generate probabilistic solar power forecasts (SPF). The AnEn is based on an historical set of deterministic numerical weather prediction (NWP) model

  17. Wind power forecast using wavelet neural network trained by improved Clonal selection algorithm

    International Nuclear Information System (INIS)

    Chitsaz, Hamed; Amjady, Nima; Zareipour, Hamidreza

    2015-01-01

    Highlights: • Presenting a Morlet wavelet neural network for wind power forecasting. • Proposing improved Clonal selection algorithm for training the model. • Applying Maximum Correntropy Criterion to evaluate the training performance. • Extensive testing of the proposed wind power forecast method on real-world data. - Abstract: With the integration of wind farms into electric power grids, an accurate wind power prediction is becoming increasingly important for the operation of these power plants. In this paper, a new forecasting engine for wind power prediction is proposed. The proposed engine has the structure of Wavelet Neural Network (WNN) with the activation functions of the hidden neurons constructed based on multi-dimensional Morlet wavelets. This forecast engine is trained by a new improved Clonal selection algorithm, which optimizes the free parameters of the WNN for wind power prediction. Furthermore, Maximum Correntropy Criterion (MCC) has been utilized instead of Mean Squared Error as the error measure in training phase of the forecasting model. The proposed wind power forecaster is tested with real-world hourly data of system level wind power generation in Alberta, Canada. In order to demonstrate the efficiency of the proposed method, it is compared with several other wind power forecast techniques. The obtained results confirm the validity of the developed approach

  18. Uncertainty Reduction in Power Generation Forecast Using Coupled Wavelet-ARIMA

    Energy Technology Data Exchange (ETDEWEB)

    Hou, Zhangshuan; Etingov, Pavel V.; Makarov, Yuri V.; Samaan, Nader A.

    2014-10-27

    In this paper, we introduce a new approach without implying normal distributions and stationarity of power generation forecast errors. In addition, it is desired to more accurately quantify the forecast uncertainty by reducing prediction intervals of forecasts. We use automatically coupled wavelet transform and autoregressive integrated moving-average (ARIMA) forecasting to reflect multi-scale variability of forecast errors. The proposed analysis reveals slow-changing “quasi-deterministic” components of forecast errors. This helps improve forecasts produced by other means, e.g., using weather-based models, and reduce forecast errors prediction intervals.

  19. Compressive spatio-temporal forecasting of meteorological quantities and photovoltaic power

    NARCIS (Netherlands)

    Tascikaraoglu, A.; Sanandaji, B.M.; Chicco, G.; Cocina, V.; Spertino, F.; Erdinç, O.; Paterakis, N.G.; Catalaõ, J.P.S.

    2016-01-01

    This paper presents a solar power forecasting scheme, which uses spatial and temporal time series data along with a photovoltaic (PV) power conversion model. The PV conversion model uses the forecast of three different variables, namely, irradiance on the tilted plane, ambient temperature, and wind

  20. A short-term spatio-temporal approach for Photovoltaic power forecasting

    NARCIS (Netherlands)

    Tascikaraoglu, A.; Sanandaji, B.M.; Chicco, G.; Cocina, V.; Spertino, F.; Erdinc, Ozan; Paterakis, N.G.; Catalão, J.P.S.

    2016-01-01

    This paper presents a Photovoltaic (PV) power conversion model and a forecasting approach which uses spatial dependency of variables along with their temporal information. The power produced by a PV plant is forecasted by a PV conversion model using the predictions of three weather variables,

  1. Multi-site solar power forecasting using gradient boosted regression trees

    DEFF Research Database (Denmark)

    Persson, Caroline Stougård; Bacher, Peder; Shiga, Takahiro

    2017-01-01

    The challenges to optimally utilize weather dependent renewable energy sources call for powerful tools for forecasting. This paper presents a non-parametric machine learning approach used for multi-site prediction of solar power generation on a forecast horizon of one to six hours. Historical pow...

  2. Probabilistic Forecast of Wind Power Generation by Stochastic Differential Equation Models

    KAUST Repository

    Elkantassi, Soumaya

    2017-04-01

    Reliable forecasting of wind power generation is crucial to optimal control of costs in generation of electricity with respect to the electricity demand. Here, we propose and analyze stochastic wind power forecast models described by parametrized stochastic differential equations, which introduce appropriate fluctuations in numerical forecast outputs. We use an approximate maximum likelihood method to infer the model parameters taking into account the time correlated sets of data. Furthermore, we study the validity and sensitivity of the parameters for each model. We applied our models to Uruguayan wind power production as determined by historical data and corresponding numerical forecasts for the period of March 1 to May 31, 2016.

  3. An application of ensemble/multi model approach for wind power production forecasting

    Science.gov (United States)

    Alessandrini, S.; Pinson, P.; Hagedorn, R.; Decimi, G.; Sperati, S.

    2011-02-01

    The wind power forecasts of the 3 days ahead period are becoming always more useful and important in reducing the problem of grid integration and energy price trading due to the increasing wind power penetration. Therefore it's clear that the accuracy of this forecast is one of the most important requirements for a successful application. The wind power forecast applied in this study is based on meteorological models that provide the 3 days ahead wind data. A Model Output Statistic correction is then performed to reduce systematic error caused, for instance, by a wrong representation of surface roughness or topography in the meteorological models. For this purpose a training of a Neural Network (NN) to link directly the forecasted meteorological data and the power data has been performed. One wind farm has been examined located in a mountain area in the south of Italy (Sicily). First we compare the performances of a prediction based on meteorological data coming from a single model with those obtained by the combination of models (RAMS, ECMWF deterministic, LAMI). It is shown that the multi models approach reduces the day-ahead normalized RMSE forecast error (normalized by nominal power) of at least 1% compared to the singles models approach. Finally we have focused on the possibility of using the ensemble model system (EPS by ECMWF) to estimate the hourly, three days ahead, power forecast accuracy. Contingency diagram between RMSE of the deterministic power forecast and the ensemble members spread of wind forecast have been produced. From this first analysis it seems that ensemble spread could be used as an indicator of the forecast's accuracy at least for the first three days ahead period.

  4. Forecasting wind power production from a wind farm using the RAMS model

    DEFF Research Database (Denmark)

    Tiriolo, L.; Torcasio, R. C.; Montesanti, S.

    2015-01-01

    of the ECMWF Integrated Forecasting System (IFS), whose horizontal resolution over Central Italy is about 25 km at the time considered in this paper. Because wind observations were not available for the site, the power curve for the whole wind farm was derived from the ECMWF wind operational analyses available......The importance of wind power forecast is commonly recognized because it represents a useful tool for grid integration and facilitates the energy trading. This work considers an example of power forecast for a wind farm in the Apennines in Central Italy. The orography around the site is complex...... and the horizontal resolution of the wind forecast has an important role. To explore this point we compared the performance of two 48 h wind power forecasts using the winds predicted by the Regional Atmospheric Modeling System (RAMS) for the year 2011. The two forecasts differ only for the horizontal resolution...

  5. Probabilistic wind power forecasting based on logarithmic transformation and boundary kernel

    International Nuclear Information System (INIS)

    Zhang, Yao; Wang, Jianxue; Luo, Xu

    2015-01-01

    Highlights: • Quantitative information on the uncertainty of wind power generation. • Kernel density estimator provides non-Gaussian predictive distributions. • Logarithmic transformation reduces the skewness of wind power density. • Boundary kernel method eliminates the density leakage near the boundary. - Abstracts: Probabilistic wind power forecasting not only produces the expectation of wind power output, but also gives quantitative information on the associated uncertainty, which is essential for making better decisions about power system and market operations with the increasing penetration of wind power generation. This paper presents a novel kernel density estimator for probabilistic wind power forecasting, addressing two characteristics of wind power which have adverse impacts on the forecast accuracy, namely, the heavily skewed and double-bounded nature of wind power density. Logarithmic transformation is used to reduce the skewness of wind power density, which improves the effectiveness of the kernel density estimator in a transformed scale. Transformations partially relieve the boundary effect problem of the kernel density estimator caused by the double-bounded nature of wind power density. However, the case study shows that there are still some serious problems of density leakage after the transformation. In order to solve this problem in the transformed scale, a boundary kernel method is employed to eliminate the density leak at the bounds of wind power distribution. The improvement of the proposed method over the standard kernel density estimator is demonstrated by short-term probabilistic forecasting results based on the data from an actual wind farm. Then, a detailed comparison is carried out of the proposed method and some existing probabilistic forecasting methods

  6. Probabilistic Forecast of Wind Power Generation by Stochastic Differential Equation Models

    KAUST Repository

    Elkantassi, Soumaya

    2017-01-01

    Reliable forecasting of wind power generation is crucial to optimal control of costs in generation of electricity with respect to the electricity demand. Here, we propose and analyze stochastic wind power forecast models described by parametrized

  7. An application and verification of ensemble forecasting on wind power to assess operational risk indicators in power grids

    Energy Technology Data Exchange (ETDEWEB)

    Alessandrini, S.; Ciapessoni, E.; Cirio, D.; Pitto, A.; Sperati, S. [Ricerca sul Sistema Energetico RSE S.p.A., Milan (Italy). Power System Development Dept. and Environment and Sustainable Development Dept.; Pinson, P. [Technical University of Denmark, Lyngby (Denmark). DTU Informatics

    2012-07-01

    Wind energy is part of the so-called not schedulable renewable sources, i.e. it must be exploited when it is available, otherwise it is lost. In European regulation it has priority of dispatch over conventional generation, to maximize green energy production. However, being variable and uncertain, wind (and solar) generation raises several issues for the security of the power grids operation. In particular, Transmission System Operators (TSOs) need as accurate as possible forecasts. Nowadays a deterministic approach in wind power forecasting (WPF) could easily be considered insufficient to face the uncertainty associated to wind energy. In order to obtain information about the accuracy of a forecast and a reliable estimation of its uncertainty, probabilistic forecasting is becoming increasingly widespread. In this paper we investigate the performances of the COnsortium for Small-scale MOdelling Limited area Ensemble Prediction System (COSMO-LEPS). First the ensemble application is followed by assessment of its properties (i.e. consistency, reliability) using different verification indices and diagrams calculated on wind power. Then we provide examples of how EPS based wind power forecast can be used in power system security analyses. Quantifying the forecast uncertainty allows to determine more accurately the regulation reserve requirements, hence improving security of operation and reducing system costs. In particular, the paper also presents a probabilistic power flow (PPF) technique developed at RSE and aimed to evaluate the impact of wind power forecast accuracy on the probability of security violations in power systems. (orig.)

  8. Leveraging stochastic differential equations for probabilistic forecasting of wind power using a dynamic power curve

    DEFF Research Database (Denmark)

    Iversen, Jan Emil Banning; Morales González, Juan Miguel; Møller, Jan Kloppenborg

    2017-01-01

    Short-term (hours to days) probabilistic forecasts of wind power generation provide useful information about the associated uncertainty of these forecasts. Standard probabilistic forecasts are usually issued on a per-horizon-basis, meaning that they lack information about the development of the u...

  9. Probing dark energy using convergence power spectrum and bi-spectrum

    Energy Technology Data Exchange (ETDEWEB)

    Dinda, Bikash R., E-mail: bikash@ctp-jamia.res.in [Centre for Theoretical Physics, Jamia Millia Islamia, New Delhi-110025 (India)

    2017-09-01

    Weak lensing convergence statistics is a powerful tool to probe dark energy. Dark energy plays an important role to the structure formation and the effects can be detected through the convergence power spectrum, bi-spectrum etc. One of the most promising and simplest dark energy model is the ΛCDM . However, it is worth investigating different dark energy models with evolving equation of state of the dark energy. In this work, detectability of different dark energy models from ΛCDM model has been explored through convergence power spectrum and bi-spectrum.

  10. An application of ensemble/multi model approach for wind power production forecast.

    Science.gov (United States)

    Alessandrini, S.; Decimi, G.; Hagedorn, R.; Sperati, S.

    2010-09-01

    The wind power forecast of the 3 days ahead period are becoming always more useful and important in reducing the problem of grid integration and energy price trading due to the increasing wind power penetration. Therefore it's clear that the accuracy of this forecast is one of the most important requirements for a successful application. The wind power forecast is based on a mesoscale meteorological models that provides the 3 days ahead wind data. A Model Output Statistic correction is then performed to reduce systematic error caused, for instance, by a wrong representation of surface roughness or topography in the meteorological models. The corrected wind data are then used as input in the wind farm power curve to obtain the power forecast. These computations require historical time series of wind measured data (by an anemometer located in the wind farm or on the nacelle) and power data in order to be able to perform the statistical analysis on the past. For this purpose a Neural Network (NN) is trained on the past data and then applied in the forecast task. Considering that the anemometer measurements are not always available in a wind farm a different approach has also been adopted. A training of the NN to link directly the forecasted meteorological data and the power data has also been performed. The normalized RMSE forecast error seems to be lower in most cases by following the second approach. We have examined two wind farms, one located in Denmark on flat terrain and one located in a mountain area in the south of Italy (Sicily). In both cases we compare the performances of a prediction based on meteorological data coming from a single model with those obtained by using two or more models (RAMS, ECMWF deterministic, LAMI, HIRLAM). It is shown that the multi models approach reduces the day-ahead normalized RMSE forecast error of at least 1% compared to the singles models approach. Moreover the use of a deterministic global model, (e.g. ECMWF deterministic

  11. Short-Term Wind Speed Forecasting for Power System Operations

    KAUST Repository

    Zhu, Xinxin

    2012-04-01

    The emphasis on renewable energy and concerns about the environment have led to large-scale wind energy penetration worldwide. However, there are also significant challenges associated with the use of wind energy due to the intermittent and unstable nature of wind. High-quality short-term wind speed forecasting is critical to reliable and secure power system operations. This article begins with an overview of the current status of worldwide wind power developments and future trends. It then reviews some statistical short-term wind speed forecasting models, including traditional time series approaches and more advanced space-time statistical models. It also discusses the evaluation of forecast accuracy, in particular, the need for realistic loss functions. New challenges in wind speed forecasting regarding ramp events and offshore wind farms are also presented. © 2012 The Authors. International Statistical Review © 2012 International Statistical Institute.

  12. System for forecasting a reactor power distribution

    International Nuclear Information System (INIS)

    Motoda, Hiroshi; Nishizawa, Yasuo.

    1976-01-01

    Purpose: To dispense with frequent running of detector in a BWR type reactor and permit calculation of the prevailing value and forecast value of power distribution in a specified region in an on-line basis. Constitution: The prevailing power distribution P sub(OZ) (where Z indicates a position in the axial direction) at a given position is estimated by prevailing power distribution estimating means, and the average prevailing power distribution Q sub(OZ) in the core is estimated while making correction of a primary neutron distribution model by core average characteristic measuring means. Then, the estimated core average power distribution Q sub(Z) after alteration of the core flow rate or alteration of Xe concentration is estimated by core average power distribution estimating means. At this time, a forecast power distribution P sub(Z) in a specified region after alteration of the flow rate or alteration of the Xe concentration is calculated on the basis of a relation P sub(Z) = (Q sub(Z)/Q sub(OZ)) by using P sub(OZ), Q sub(OZ) and Q sub(Z). The above calculations are carried out in a short period of time by using a process computer. (Ikeda, J.)

  13. Generation Mix Study Focusing on Nuclear Power by Practical Peak Forecast

    International Nuclear Information System (INIS)

    Shin, Jung Ho; Roh, Myung Sub

    2013-01-01

    The excessive underestimation can lead to a range of problem; expansion of LNG plant requiring short construction period, the following increase of electricity price, low reserve margin and inefficient configuration of power source. With regard to nuclear power, the share of the stable and economic base load plant, nuclear power, can reduce under the optimum level. Amongst varied factors which contribute to the underestimate, immoderate target for demand side management (DSM) including double deduction of the constraint amount by DSM from peak demand forecast is one of the causes. The hypothesis in this study is that the better optimum generation mix including the adequate share of nuclear power can be obtained under the condition of the peak demand forecast without deduction of DSM target because this forecast is closer to the actual peak demand. In this study, the hypothesis is verified with comparison between peak demand forecast before (or after) DSM target application and the actual peak demand in the 3 rd through 5 th BPE from 2006 to 2010. Furthermore, this research compares and analyzes several generation mix in 2027 focusing on the nuclear power by a few conditions using the WASP-IV program on the basis of the 6 th BPE in 2013. According to the comparative analysis on the peak demand forecast and actual peak demand from 2006 to 2010, the peak demand forecasts without the deduction of the DSM target is closer to the actual peak demand than the peak demand forecasts considering the DSM target in the 3 th , 4 th , 5 th entirely. In addition, the generation mix until 2027 is examined by the WASP-IV. As a result of the program run, when considering the peak demand forecast without DSM reflection, since the base load plants including nuclear power take up adequate proportion, stable and economic supply of electricity can be achieved. On the contrary, in case of planning based on the peak demand forecast with DSM reflected and then compensating the shortage by

  14. Accurate Medium-Term Wind Power Forecasting in a Censored Classification Framework

    DEFF Research Database (Denmark)

    Dahl, Christian M.; Croonenbroeck, Carsten

    2014-01-01

    We provide a wind power forecasting methodology that exploits many of the actual data's statistical features, in particular both-sided censoring. While other tools ignore many of the important “stylized facts” or provide forecasts for short-term horizons only, our approach focuses on medium......-term forecasts, which are especially necessary for practitioners in the forward electricity markets of many power trading places; for example, NASDAQ OMX Commodities (formerly Nord Pool OMX Commodities) in northern Europe. We show that our model produces turbine-specific forecasts that are significantly more...... accurate in comparison to established benchmark models and present an application that illustrates the financial impact of more accurate forecasts obtained using our methodology....

  15. Cash flow forecasting model for nuclear power projects

    International Nuclear Information System (INIS)

    Liu Wei; Guo Jilin

    2002-01-01

    Cash flow forecasting is very important for owners and contractors of nuclear power projects to arrange the capital and to decrease the capital cost. The factors related to contractor cash flow forecasting are analyzed and a cash flow forecasting model is presented which is suitable for both contractors and owners. The model is efficiently solved using a cost-schedule data integration scheme described. A program is developed based on the model and verified with real project data. The result indicates that the model is efficient and effective

  16. Primordial power spectrum features and consequences

    Science.gov (United States)

    Goswami, G.

    2014-03-01

    The present Cosmic Microwave Background (CMB) temperature and polarization anisotropy data is consistent with not only a power law scalar primordial power spectrum (PPS) with a small running but also with the scalar PPS having very sharp features. This has motivated inflationary models with such sharp features. Recently, even the possibility of having nulls in the power spectrum (at certain scales) has been considered. The existence of these nulls has been shown in linear perturbation theory. What shall be the effect of higher order corrections on such nulls? Inspired by this question, we have attempted to calculate quantum radiative corrections to the Fourier transform of the 2-point function in a toy field theory and address the issue of how these corrections to the power spectrum behave in models in which the tree-level power spectrum has a sharp dip (but not a null). In particular, we have considered the possibility of the relative enhancement of radiative corrections in a model in which the tree-level spectrum goes through a dip in power at a certain scale. The mode functions of the field (whose power spectrum is to be evaluated) are chosen such that they undergo the kind of dynamics that leads to a sharp dip in the tree level power spectrum. Next, we have considered the situation in which this field has quartic self interactions, and found one loop correction in a suitably chosen renormalization scheme. Thus, we have attempted to answer the following key question in the context of this toy model (which is as important in the realistic case): In the chosen renormalization scheme, can quantum radiative corrections be enhanced relative to tree-level power spectrum at scales, at which sharp dips appear in the tree-level spectrum?

  17. Skill forecasting from different wind power ensemble prediction methods

    International Nuclear Information System (INIS)

    Pinson, Pierre; Nielsen, Henrik A; Madsen, Henrik; Kariniotakis, George

    2007-01-01

    This paper presents an investigation on alternative approaches to the providing of uncertainty estimates associated to point predictions of wind generation. Focus is given to skill forecasts in the form of prediction risk indices, aiming at giving a comprehensive signal on the expected level of forecast uncertainty. Ensemble predictions of wind generation are used as input. A proposal for the definition of prediction risk indices is given. Such skill forecasts are based on the dispersion of ensemble members for a single prediction horizon, or over a set of successive look-ahead times. It is shown on the test case of a Danish offshore wind farm how prediction risk indices may be related to several levels of forecast uncertainty (and energy imbalances). Wind power ensemble predictions are derived from the transformation of ECMWF and NCEP ensembles of meteorological variables to power, as well as by a lagged average approach alternative. The ability of risk indices calculated from the various types of ensembles forecasts to resolve among situations with different levels of uncertainty is discussed

  18. Multi-step wind speed forecasting based on a hybrid forecasting architecture and an improved bat algorithm

    International Nuclear Information System (INIS)

    Xiao, Liye; Qian, Feng; Shao, Wei

    2017-01-01

    Highlights: • Propose a hybrid architecture based on a modified bat algorithm for multi-step wind speed forecasting. • Improve the accuracy of multi-step wind speed forecasting. • Modify bat algorithm with CG to improve optimized performance. - Abstract: As one of the most promising sustainable energy sources, wind energy plays an important role in energy development because of its cleanliness without causing pollution. Generally, wind speed forecasting, which has an essential influence on wind power systems, is regarded as a challenging task. Analyses based on single-step wind speed forecasting have been widely used, but their results are insufficient in ensuring the reliability and controllability of wind power systems. In this paper, a new forecasting architecture based on decomposing algorithms and modified neural networks is successfully developed for multi-step wind speed forecasting. Four different hybrid models are contained in this architecture, and to further improve the forecasting performance, a modified bat algorithm (BA) with the conjugate gradient (CG) method is developed to optimize the initial weights between layers and thresholds of the hidden layer of neural networks. To investigate the forecasting abilities of the four models, the wind speed data collected from four different wind power stations in Penglai, China, were used as a case study. The numerical experiments showed that the hybrid model including the singular spectrum analysis and general regression neural network with CG-BA (SSA-CG-BA-GRNN) achieved the most accurate forecasting results in one-step to three-step wind speed forecasting.

  19. Refined Diebold-Mariano Test Methods for the Evaluation of Wind Power Forecasting Models

    Directory of Open Access Journals (Sweden)

    Hao Chen

    2014-07-01

    Full Text Available The scientific evaluation methodology for the forecast accuracy of wind power forecasting models is an important issue in the domain of wind power forecasting. However, traditional forecast evaluation criteria, such as Mean Squared Error (MSE and Mean Absolute Error (MAE, have limitations in application to some degree. In this paper, a modern evaluation criterion, the Diebold-Mariano (DM test, is introduced. The DM test can discriminate the significant differences of forecasting accuracy between different models based on the scheme of quantitative analysis. Furthermore, the augmented DM test with rolling windows approach is proposed to give a more strict forecasting evaluation. By extending the loss function to an asymmetric structure, the asymmetric DM test is proposed. Case study indicates that the evaluation criteria based on DM test can relieve the influence of random sample disturbance. Moreover, the proposed augmented DM test can provide more evidence when the cost of changing models is expensive, and the proposed asymmetric DM test can add in the asymmetric factor, and provide practical evaluation of wind power forecasting models. It is concluded that the two refined DM tests can provide reference to the comprehensive evaluation for wind power forecasting models.

  20. The Spectrum of Wind Power Fluctuations

    Science.gov (United States)

    Bandi, Mahesh

    2016-11-01

    Wind is a variable energy source whose fluctuations threaten electrical grid stability and complicate dynamical load balancing. The power generated by a wind turbine fluctuates due to the variable wind speed that blows past the turbine. Indeed, the spectrum of wind power fluctuations is widely believed to reflect the Kolmogorov spectrum; both vary with frequency f as f - 5 / 3. This variability decreases when aggregate power fluctuations from geographically distributed wind farms are averaged at the grid via a mechanism known as geographic smoothing. Neither the f - 5 / 3 wind power fluctuation spectrum nor the mechanism of geographic smoothing are understood. In this work, we explain the wind power fluctuation spectrum from the turbine through grid scales. The f - 5 / 3 wind power fluctuation spectrum results from the largest length scales of atmospheric turbulence of order 200 km influencing the small scales where individual turbines operate. This long-range influence spatially couples geographically distributed wind farms and synchronizes farm outputs over a range of frequencies and decreases with increasing inter-farm distance. Consequently, aggregate grid-scale power fluctuations remain correlated, and are smoothed until they reach a limiting f - 7 / 3 spectrum. This work was funded by the Collective Interactions Unit, OIST Graduate University, Japan.

  1. Generation Mix Study Focusing on Nuclear Power by Practical Peak Forecast

    Energy Technology Data Exchange (ETDEWEB)

    Shin, Jung Ho; Roh, Myung Sub [KEPCO International Nuclear Graduate School, Ulsan (Korea, Republic of)

    2013-10-15

    The excessive underestimation can lead to a range of problem; expansion of LNG plant requiring short construction period, the following increase of electricity price, low reserve margin and inefficient configuration of power source. With regard to nuclear power, the share of the stable and economic base load plant, nuclear power, can reduce under the optimum level. Amongst varied factors which contribute to the underestimate, immoderate target for demand side management (DSM) including double deduction of the constraint amount by DSM from peak demand forecast is one of the causes. The hypothesis in this study is that the better optimum generation mix including the adequate share of nuclear power can be obtained under the condition of the peak demand forecast without deduction of DSM target because this forecast is closer to the actual peak demand. In this study, the hypothesis is verified with comparison between peak demand forecast before (or after) DSM target application and the actual peak demand in the 3{sup rd} through 5{sup th} BPE from 2006 to 2010. Furthermore, this research compares and analyzes several generation mix in 2027 focusing on the nuclear power by a few conditions using the WASP-IV program on the basis of the 6{sup th} BPE in 2013. According to the comparative analysis on the peak demand forecast and actual peak demand from 2006 to 2010, the peak demand forecasts without the deduction of the DSM target is closer to the actual peak demand than the peak demand forecasts considering the DSM target in the 3{sup th}, 4{sup th}, 5{sup th} entirely. In addition, the generation mix until 2027 is examined by the WASP-IV. As a result of the program run, when considering the peak demand forecast without DSM reflection, since the base load plants including nuclear power take up adequate proportion, stable and economic supply of electricity can be achieved. On the contrary, in case of planning based on the peak demand forecast with DSM reflected and then

  2. Forecast of wind energy production and ensuring required balancing power

    International Nuclear Information System (INIS)

    Merkulov, M.

    2010-01-01

    The wind energy is gaining larger part of the energy mix around the world as well as in Bulgaria. Having in mind the irregularity of the wind, we are in front of a challenge for management of the power grid in new unknown conditions. The world's experience has proven that there could be no effective management of the grid without forecasting tools, even with small scale of wind power penetration. Application of such tools promotes simple management of large wind energy production and reduction of the quantities of required balancing powers. The share of the expenses and efforts for forecasting of the wind energy is incomparably small in comparison with expenses for keeping additional powers in readiness. The recent computers potential allow simple and rapid processing of large quantities of data from different sources, which provides required conditions for modeling the world's climate and producing sophisticated forecast. (author)

  3. Very-short-term wind power probabilistic forecasts by sparse vector autoregression

    DEFF Research Database (Denmark)

    Dowell, Jethro; Pinson, Pierre

    2016-01-01

    A spatio-temporal method for producing very-shortterm parametric probabilistic wind power forecasts at a large number of locations is presented. Smart grids containing tens, or hundreds, of wind generators require skilled very-short-term forecasts to operate effectively, and spatial information...... is highly desirable. In addition, probabilistic forecasts are widely regarded as necessary for optimal power system management as they quantify the uncertainty associated with point forecasts. Here we work within a parametric framework based on the logit-normal distribution and forecast its parameters....... The location parameter for multiple wind farms is modelled as a vector-valued spatiotemporal process, and the scale parameter is tracked by modified exponential smoothing. A state-of-the-art technique for fitting sparse vector autoregressive models is employed to model the location parameter and demonstrates...

  4. Forecasting loads and prices in competitive power markets

    International Nuclear Information System (INIS)

    Bunn, D.W.

    2000-01-01

    This paper provides a review of some of the main methodological issues and techniques which have become innovative in addressing the problem of forecasting daily loads and prices in the new competitive power markets. Particular emphasis is placed upon computationally intensive methods, including variable segmentation, multiple modeling, combinations, and neural networks for forecasting the demand side, and strategic simulation using artificial agents for the supply side

  5. New tool for integration of wind power forecasting into power system operation

    DEFF Research Database (Denmark)

    Gubina, Andrej F.; Keane, Andrew; Meibom, Peter

    2009-01-01

    The paper describes the methodology that has been developed for transmission system operators (TSOs) of Republic of Ireland, Eirgrid, and Northern Ireland, SONI the TSO in Northern Ireland, to study the effects of advanced wind power forecasting on optimal short-term power system scheduling....... The resulting schedules take into account the electricity market conditions and feature optimal reserve scheduling. The short-term wind power prediction is provided by the Anemos tool, and the scheduling function, including the reserve optimisation, by the Wilmar tool. The proposed methodology allows...... for evaluation of the impacts that different types of wind energy forecasts (stochastic vs. deterministic vs. perfect) have on the schedules, and how the new incoming information via in-day scheduling impacts the quality of the schedules. Within the methodology, metrics to assess the quality of the schedules...

  6. Combination of Deterministic and Probabilistic Meteorological Models to enhance Wind Farm Power Forecasts

    International Nuclear Information System (INIS)

    Bremen, Lueder von

    2007-01-01

    Large-scale wind farms will play an important role in the future worldwide energy supply. However, with increasing wind power penetration all stakeholders on the electricity market will ask for more skilful wind power predictions regarding save grid integration and to increase the economic value of wind power. A Neural Network is used to calculate Model Output Statistics (MOS) for each individual forecast model (ECMWF and HIRLAM) and to model the aggregated power curve of the Middelgrunden offshore wind farm. We showed that the combination of two NWP models clearly outperforms the better single model. The normalized day-ahead RMSE forecast error for Middelgrunden can be reduced by 1% compared to single ECMWF. This is a relative improvement of 6%. For lead times >24h it is worthwhile to use a more sophisticated model combination approach than simple linear weighting. The investigated principle component regression is able to extract the uncorrelated information from two NWP forecasts. The spread of Ensemble Predictions is related to the skill of wind power forecasts. Simple contingency diagrams show that low spread corresponds is more often related to low forecast errors and high spread to large forecast errors

  7. A multiscale forecasting method for power plant fleet management

    Science.gov (United States)

    Chen, Hongmei

    In recent years the electric power industry has been challenged by a high level of uncertainty and volatility brought on by deregulation and globalization. A power producer must minimize the life cycle cost while meeting stringent safety and regulatory requirements and fulfilling customer demand for high reliability. Therefore, to achieve true system excellence, a more sophisticated system-level decision-making process with a more accurate forecasting support system to manage diverse and often widely dispersed generation units as a single, easily scaled and deployed fleet system in order to fully utilize the critical assets of a power producer has been created as a response. The process takes into account the time horizon for each of the major decision actions taken in a power plant and develops methods for information sharing between them. These decisions are highly interrelated and no optimal operation can be achieved without sharing information in the overall process. The process includes a forecasting system to provide information for planning for uncertainty. A new forecasting method is proposed, which utilizes a synergy of several modeling techniques properly combined at different time-scales of the forecasting objects. It can not only take advantages of the abundant historical data but also take into account the impact of pertinent driving forces from the external business environment to achieve more accurate forecasting results. Then block bootstrap is utilized to measure the bias in the estimate of the expected life cycle cost which will actually be needed to drive the business for a power plant in the long run. Finally, scenario analysis is used to provide a composite picture of future developments for decision making or strategic planning. The decision-making process is applied to a typical power producer chosen to represent challenging customer demand during high-demand periods. The process enhances system excellence by providing more accurate market

  8. The Increase of Power Efficiency of Underground Coal Mining by the Forecasting of Electric Power Consumption

    Science.gov (United States)

    Efremenko, Vladimir; Belyaevsky, Roman; Skrebneva, Evgeniya

    2017-11-01

    In article the analysis of electric power consumption and problems of power saving on coal mines are considered. Nowadays the share of conditionally constant costs of electric power for providing safe working conditions underground on coal mines is big. Therefore, the power efficiency of underground coal mining depends on electric power expense of the main technological processes and size of conditionally constant costs. The important direction of increase of power efficiency of coal mining is forecasting of a power consumption and monitoring of electric power expense. One of the main approaches to reducing of electric power costs is increase in accuracy of the enterprise demand in the wholesale electric power market. It is offered to use artificial neural networks to forecasting of day-ahead power consumption with hourly breakdown. At the same time use of neural and indistinct (hybrid) systems on the principles of fuzzy logic, neural networks and genetic algorithms is more preferable. This model allows to do exact short-term forecasts at a small array of input data. A set of the input parameters characterizing mining-and-geological and technological features of the enterprise is offered.

  9. Spatial-temporal analysis of wind power forecast errors for West-Coast Norway

    Energy Technology Data Exchange (ETDEWEB)

    Revheim, Paal Preede; Beyer, Hans Georg [Agder Univ. (UiA), Grimstad (Norway). Dept. of Engineering Sciences

    2012-07-01

    In this paper the spatial-temporal structure of forecast errors for wind power in West-Coast Norway is analyzed. Starting on the qualitative analysis of the forecast error reduction, with respect to single site data, for the lumped conditions of groups of sites the spatial and temporal correlations of the wind power forecast errors within and between the same groups are studied in detail. Based on this, time-series regression models to be used to analytically describe the error reduction are set up. The models give an expected reduction in forecast error between 48.4% and 49%. (orig.)

  10. Adaptive robust polynomial regression for power curve modeling with application to wind power forecasting

    DEFF Research Database (Denmark)

    Xu, Man; Pinson, Pierre; Lu, Zongxiang

    2016-01-01

    of the lack of time adaptivity. In this paper, a refined local polynomial regression algorithm is proposed to yield an adaptive robust model of the time-varying scattered power curve for forecasting applications. The time adaptivity of the algorithm is considered with a new data-driven bandwidth selection......Wind farm power curve modeling, which characterizes the relationship between meteorological variables and power production, is a crucial procedure for wind power forecasting. In many cases, power curve modeling is more impacted by the limited quality of input data rather than the stochastic nature...... of the energy conversion process. Such nature may be due the varying wind conditions, aging and state of the turbines, etc. And, an equivalent steady-state power curve, estimated under normal operating conditions with the intention to filter abnormal data, is not sufficient to solve the problem because...

  11. Enhanced short-term wind power forecasting and value to grid operations. The wind forecasting improvement project

    Energy Technology Data Exchange (ETDEWEB)

    Orwig, Kirsten D. [National Renewable Energy Laboratory (NREL), Golden, CO (United States). Transmission Grid Integration; Benjamin, Stan; Wilczak, James; Marquis, Melinda [National Oceanic and Atmospheric Administration, Boulder, CO (United States). Earth System Research Lab.; Stern, Andrew [National Oceanic and Atmospheric Administration, Silver Spring, MD (United States); Clark, Charlton; Cline, Joel [U.S. Department of Energy, Washington, DC (United States). Wind and Water Power Program; Finley, Catherine [WindLogics, Grand Rapids, MN (United States); Freedman, Jeffrey [AWS Truepower, Albany, NY (United States)

    2012-07-01

    The current state-of-the-art wind power forecasting in the 0- to 6-h timeframe has levels of uncertainty that are adding increased costs and risks to the U.S. electrical grid. It is widely recognized within the electrical grid community that improvements to these forecasts could greatly reduce the costs and risks associated with integrating higher penetrations of wind energy. The U.S. Department of Energy has sponsored a research campaign in partnership with the National Oceanic and Atmospheric Administration (NOAA) and private industry to foster improvements in wind power forecasting. The research campaign involves a three-pronged approach: (1) a one-year field measurement campaign within two regions; (2) enhancement of NOAA's experimental 3-km High-Resolution Rapid Refresh (HRRR) model by assimilating the data from the field campaign; and (3) evaluation of the economic and reliability benefits of improved forecasts to grid operators. This paper and presentation provide an overview of the regions selected, instrumentation deployed, data quality and control, assimilation of data into HRRR, and preliminary results of HRRR performance analysis. (orig.)

  12. Space-time wind speed forecasting for improved power system dispatch

    KAUST Repository

    Zhu, Xinxin; Genton, Marc G.; Gu, Yingzhong; Xie, Le

    2014-01-01

    direction and with the seasons, hence avoiding a subjective choice of regimes. Then, results from the wind forecasts are incorporated into a power system economic dispatch model, the cost of which is used as a loss measure of the quality of the forecast

  13. Modeling of spatial dependence in wind power forecast uncertainty

    DEFF Research Database (Denmark)

    Papaefthymiou, George; Pinson, Pierre

    2008-01-01

    It is recognized today that short-term (up to 2-3 days ahead) probabilistic forecasts of wind power provide forecast users with a paramount information on the uncertainty of expected wind generation. When considering different areas covering a region, they are produced independently, and thus...... neglect the interdependence structure of prediction errors, induced by movement of meteorological fronts, or more generally by inertia of meteorological systems. This issue is addressed here by describing a method that permits to generate interdependent scenarios of wind generation for spatially...... distributed wind power production for specific look-ahead times. The approach is applied to the case of western Denmark split in 5 zones, for a total capacity of more than 2.1 GW. The interest of the methodology for improving the resolution of probabilistic forecasts, for a range of decision-making problems...

  14. Using Analog Ensemble to generate spatially downscaled probabilistic wind power forecasts

    Science.gov (United States)

    Delle Monache, L.; Shahriari, M.; Cervone, G.

    2017-12-01

    We use the Analog Ensemble (AnEn) method to generate probabilistic 80-m wind power forecasts. We use data from the NCEP GFS ( 28 km resolution) and NCEP NAM (12 km resolution). We use forecasts data from NAM and GFS, and analysis data from NAM which enables us to: 1) use a lower-resolution model to create higher-resolution forecasts, and 2) use a higher-resolution model to create higher-resolution forecasts. The former essentially increases computing speed and the latter increases forecast accuracy. An aggregated model of the former can be compared against the latter to measure the accuracy of the AnEn spatial downscaling. The AnEn works by taking a deterministic future forecast and comparing it with past forecasts. The model searches for the best matching estimates within the past forecasts and selects the predictand value corresponding to these past forecasts as the ensemble prediction for the future forecast. Our study is based on predicting wind speed and air density at more than 13,000 grid points in the continental US. We run the AnEn model twice: 1) estimating 80-m wind speed by using predictor variables such as temperature, pressure, geopotential height, U-component and V-component of wind, 2) estimating air density by using predictors such as temperature, pressure, and relative humidity. We use the air density values to correct the standard wind power curves for different values of air density. The standard deviation of the ensemble members (i.e. ensemble spread) will be used as the degree of difficulty to predict wind power at different locations. The value of the correlation coefficient between the ensemble spread and the forecast error determines the appropriateness of this measure. This measure is prominent for wind farm developers as building wind farms in regions with higher predictability will reduce the real-time risks of operating in the electricity markets.

  15. A Novel Hybrid Model for Short-Term Forecasting in PV Power Generation

    Directory of Open Access Journals (Sweden)

    Yuan-Kang Wu

    2014-01-01

    Full Text Available The increasing use of solar power as a source of electricity has led to increased interest in forecasting its power output over short-time horizons. Short-term forecasts are needed for operational planning, switching sources, programming backup, reserve usage, and peak load matching. However, the output of a photovoltaic (PV system is influenced by irradiation, cloud cover, and other weather conditions. These factors make it difficult to conduct short-term PV output forecasting. In this paper, an experimental database of solar power output, solar irradiance, air, and module temperature data has been utilized. It includes data from the Green Energy Office Building in Malaysia, the Taichung Thermal Plant of Taipower, and National Penghu University. Based on the historical PV power and weather data provided in the experiment, all factors that influence photovoltaic-generated energy are discussed. Moreover, five types of forecasting modules were developed and utilized to predict the one-hour-ahead PV output. They include the ARIMA, SVM, ANN, ANFIS, and the combination models using GA algorithm. Forecasting results show the high precision and efficiency of this combination model. Therefore, the proposed model is suitable for ensuring the stable operation of a photovoltaic generation system.

  16. Short-term Wind Forecasting to Support Virtual Power Player Operation

    OpenAIRE

    Ramos, Sérgio; Soares, João; Pinto, Tiago; Vale, Zita

    2013-01-01

    This paper proposes a wind speed forecasting model that contributes to the development and implementation of adequate methodologies for Energy Resource Man-agement in a distribution power network, with intensive use of wind based power generation. The proposed fore-casting methodology aims to support the operation in the scope of the intraday resources scheduling model, name-ly with a time horizon of 10 minutes. A case study using a real database from the meteoro-logical station installed ...

  17. A General Probabilistic Forecasting Framework for Offshore Wind Power Fluctuations

    DEFF Research Database (Denmark)

    Trombe, Pierre-Julien; Pinson, Pierre; Madsen, Henrik

    2012-01-01

    Accurate wind power forecasts highly contribute to the integration of wind power into power systems. The focus of the present study is on large-scale offshore wind farms and the complexity of generating accurate probabilistic forecasts of wind power fluctuations at time-scales of a few minutes...... fluctuations are characterized by highly volatile dynamics which are difficult to capture and predict. Due to the lack of adequate on-site meteorological observations to relate these dynamics to meteorological phenomena, we propose a general model formulation based on a statistical approach and historical wind...... power measurements only. We introduce an advanced Markov Chain Monte Carlo (MCMC) estimation method to account for the different features observed in an empirical time series of wind power: autocorrelation, heteroscedasticity and regime-switching. The model we propose is an extension of Markov...

  18. The Brazilian electric power market: historic and forecasting

    International Nuclear Information System (INIS)

    Carvalho Afonso, C.A. de; Azevedo, J.B.L. de

    1992-01-01

    A historical analysis of electric power market evolution in Brazil and in their regions during 1950 to 1990, is described, showing the forecasting for the next ten years. Some considerations about population, energy conservation and industrial consumers are also presented, including statistical data of the electrical power market. (C.G.C.)

  19. Solar photovoltaic power forecasting using optimized modified extreme learning machine technique

    Directory of Open Access Journals (Sweden)

    Manoja Kumar Behera

    2018-06-01

    Full Text Available Prediction of photovoltaic power is a significant research area using different forecasting techniques mitigating the effects of the uncertainty of the photovoltaic generation. Increasingly high penetration level of photovoltaic (PV generation arises in smart grid and microgrid concept. Solar source is irregular in nature as a result PV power is intermittent and is highly dependent on irradiance, temperature level and other atmospheric parameters. Large scale photovoltaic generation and penetration to the conventional power system introduces the significant challenges to microgrid a smart grid energy management. It is very critical to do exact forecasting of solar power/irradiance in order to secure the economic operation of the microgrid and smart grid. In this paper an extreme learning machine (ELM technique is used for PV power forecasting of a real time model whose location is given in the Table 1. Here the model is associated with the incremental conductance (IC maximum power point tracking (MPPT technique that is based on proportional integral (PI controller which is simulated in MATLAB/SIMULINK software. To train single layer feed-forward network (SLFN, ELM algorithm is implemented whose weights are updated by different particle swarm optimization (PSO techniques and their performance are compared with existing models like back propagation (BP forecasting model. Keywords: PV array, Extreme learning machine, Maximum power point tracking, Particle swarm optimization, Craziness particle swarm optimization, Accelerate particle swarm optimization, Single layer feed-forward network

  20. Scaling forecast models for wind turbulence and wind turbine power intermittency

    Science.gov (United States)

    Duran Medina, Olmo; Schmitt, Francois G.; Calif, Rudy

    2017-04-01

    The intermittency of the wind turbine power remains an important issue for the massive development of this renewable energy. The energy peaks injected in the electric grid produce difficulties in the energy distribution management. Hence, a correct forecast of the wind power in the short and middle term is needed due to the high unpredictability of the intermittency phenomenon. We consider a statistical approach through the analysis and characterization of stochastic fluctuations. The theoretical framework is the multifractal modelisation of wind velocity fluctuations. Here, we consider three wind turbine data where two possess a direct drive technology. Those turbines are producing energy in real exploitation conditions and allow to test our forecast models of power production at a different time horizons. Two forecast models were developed based on two physical principles observed in the wind and the power time series: the scaling properties on the one hand and the intermittency in the wind power increments on the other. The first tool is related to the intermittency through a multifractal lognormal fit of the power fluctuations. The second tool is based on an analogy of the power scaling properties with a fractional brownian motion. Indeed, an inner long-term memory is found in both time series. Both models show encouraging results since a correct tendency of the signal is respected over different time scales. Those tools are first steps to a search of efficient forecasting approaches for grid adaptation facing the wind energy fluctuations.

  1. Stochastic Dynamic AC Optimal Power Flow Based on a Multivariate Short-Term Wind Power Scenario Forecasting Model

    Directory of Open Access Journals (Sweden)

    Wenlei Bai

    2017-12-01

    Full Text Available The deterministic methods generally used to solve DC optimal power flow (OPF do not fully capture the uncertainty information in wind power, and thus their solutions could be suboptimal. However, the stochastic dynamic AC OPF problem can be used to find an optimal solution by fully capturing the uncertainty information of wind power. That uncertainty information of future wind power can be well represented by the short-term future wind power scenarios that are forecasted using the generalized dynamic factor model (GDFM—a novel multivariate statistical wind power forecasting model. Furthermore, the GDFM can accurately represent the spatial and temporal correlations among wind farms through the multivariate stochastic process. Fully capturing the uncertainty information in the spatially and temporally correlated GDFM scenarios can lead to a better AC OPF solution under a high penetration level of wind power. Since the GDFM is a factor analysis based model, the computational time can also be reduced. In order to further reduce the computational time, a modified artificial bee colony (ABC algorithm is used to solve the AC OPF problem based on the GDFM forecasting scenarios. Using the modified ABC algorithm based on the GDFM forecasting scenarios has resulted in better AC OPF’ solutions on an IEEE 118-bus system at every hour for 24 h.

  2. A Public-Private-Acadmic Partnership to Advance Solar Power Forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Haupt, Sue Ellen [National Center for Atmospheric Research, Boulder, CO (United States)

    2016-04-19

    The National Center for Atmospheric Research (NCAR) is pleased to have led a partnership to advance the state-of-the-science of solar power forecasting by designing, developing, building, deploying, testing, and assessing the SunCast™ Solar Power Forecasting System. The project has included cutting edge research, testing in several geographically- and climatologically-diverse high penetration solar utilities and Independent System Operators (ISOs), and wide dissemination of the research results to raise the bar on solar power forecasting technology. The partners include three other national laboratories, six universities, and industry partners. This public-private-academic team has worked in concert to perform use-inspired research to advance solar power forecasting through cutting-edge research to advance both the necessary forecasting technologies and the metrics for evaluating them. The project has culminated in a year-long, full-scale demonstration of provide irradiance and power forecasts to utilities and ISOs to use in their operations. The project focused on providing elements of a value chain, beginning with the weather that causes a deviation from clear sky irradiance and progresses through monitoring and observations, modeling, forecasting, dissemination and communication of the forecasts, interpretation of the forecast, and through decision-making, which produces outcomes that have an economic value. The system has been evaluated using metrics developed specifically for this project, which has provided rich information on model and system performance. Research was accomplished on the very short range (0-6 hours) Nowcasting system as well as on the longer term (6-72 hour) forecasting system. The shortest range forecasts are based on observations in the field. The shortest range system, built by Brookhaven National Laboratory (BNL) and based on Total Sky Imagers (TSIs) is TSICast, which operates on the shortest time scale with a latency of only a few

  3. Economic evaluation of short-term wind power forecast in ERCOT. Preliminary results

    Energy Technology Data Exchange (ETDEWEB)

    Orwig, Kirsten D.; Hodge, Bri-Mathias; Brinkman, Greg; Ela, Erik; Milligan, Michael [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Banunarayanan, Venkat; Nasir, Saleh [ICF International, Fairfax, VA (United States); Freedman, Jeff [AWS Truepower, Albany, NY (United States)

    2012-07-01

    A number of wind energy integration studies have investigated the monetary value of using day-ahead wind power forecasts for grid operation decisions. Historically, these studies have shown that large cost savings could be gained by grid operators implementing the forecasts in their system operations. To date, none of these studies have investigated the value of shorter term (0- to 6-h ahead) wind power forecasts. In 2010, the Department of Energy and the National Oceanic and Atmospheric Administration partnered to form the Wind Forecasting Improvement Project (WFIP) to fund improvements in short-term wind forecasts and determine the economic value of these improvements to grid operators. In this work, we discuss the preliminary results of the economic benefit analysis portion of the WFIP for the Electric Reliability Council of Texas. The improvements seen in the wind forecasts are examined and the economic results of a production cost model simulation are analyzed. (orig.)

  4. Space power needs and forecasted technologies for the 1990s and beyond

    International Nuclear Information System (INIS)

    Buden, D.; Albert, T.

    1987-01-01

    A new generation of reactors for electric power will be available for space missions to satisfy military and civilian needs in the 1990s and beyond. To ensure a useful product, nuclear power plant development must be cognizant of other space power technologies. Major advances in solar and chemical technologies need to be considered in establishing the goals of future nuclear power plants. In addition, the mission needs are evolving into new regimes. Civilian and military power needs are forecasted to exceed anything used in space to date. Technology trend forecasts have been mapped as a function of time for solar, nuclear, chemical, and storage systems to illustrate areas where each technology provides minimum mass. Other system characteristics may dominate the usefulness of a technology on a given mission. This paper will discuss some of these factors, as well as forecast future military and civilian power needs and the status of technologies for the 1990s and 2000s. 6 references

  5. Subsampling for graph power spectrum estimation

    KAUST Repository

    Chepuri, Sundeep Prabhakar; Leus, Geert

    2016-01-01

    In this paper we focus on subsampling stationary random signals that reside on the vertices of undirected graphs. Second-order stationary graph signals are obtained by filtering white noise and they admit a well-defined power spectrum. Estimating the graph power spectrum forms a central component of stationary graph signal processing and related inference tasks. We show that by sampling a significantly smaller subset of vertices and using simple least squares, we can reconstruct the power spectrum of the graph signal from the subsampled observations, without any spectral priors. In addition, a near-optimal greedy algorithm is developed to design the subsampling scheme.

  6. Subsampling for graph power spectrum estimation

    KAUST Repository

    Chepuri, Sundeep Prabhakar

    2016-10-06

    In this paper we focus on subsampling stationary random signals that reside on the vertices of undirected graphs. Second-order stationary graph signals are obtained by filtering white noise and they admit a well-defined power spectrum. Estimating the graph power spectrum forms a central component of stationary graph signal processing and related inference tasks. We show that by sampling a significantly smaller subset of vertices and using simple least squares, we can reconstruct the power spectrum of the graph signal from the subsampled observations, without any spectral priors. In addition, a near-optimal greedy algorithm is developed to design the subsampling scheme.

  7. From probabilistic forecasts to statistical scenarios of short-term wind power production

    DEFF Research Database (Denmark)

    Pinson, Pierre; Papaefthymiou, George; Klockl, Bernd

    2009-01-01

    on the development of the forecast uncertainty through forecast series. However, this additional information may be paramount for a large class of time-dependent and multistage decision-making problems, e.g. optimal operation of combined wind-storage systems or multiple-market trading with different gate closures......Short-term (up to 2-3 days ahead) probabilistic forecasts of wind power provide forecast users with highly valuable information on the uncertainty of expected wind generation. Whatever the type of these probabilistic forecasts, they are produced on a per horizon basis, and hence do not inform....... This issue is addressed here by describing a method that permits the generation of statistical scenarios of short-term wind generation that accounts for both the interdependence structure of prediction errors and the predictive distributions of wind power production. The method is based on the conversion...

  8. Daily rainfall forecasting for one year in a single run using Singular Spectrum Analysis

    Science.gov (United States)

    Unnikrishnan, Poornima; Jothiprakash, V.

    2018-06-01

    Effective modelling and prediction of smaller time step rainfall is reported to be very difficult owing to its highly erratic nature. Accurate forecast of daily rainfall for longer duration (multi time step) may be exceptionally helpful in the efficient planning and management of water resources systems. Identification of inherent patterns in a rainfall time series is also important for an effective water resources planning and management system. In the present study, Singular Spectrum Analysis (SSA) is utilized to forecast the daily rainfall time series pertaining to Koyna watershed in Maharashtra, India, for 365 days after extracting various components of the rainfall time series such as trend, periodic component, noise and cyclic component. In order to forecast the time series for longer time step (365 days-one window length), the signal and noise components of the time series are forecasted separately and then added together. The results of the study show that the method of SSA could extract the various components of the time series effectively and could also forecast the daily rainfall time series for longer duration such as one year in a single run with reasonable accuracy.

  9. A hybrid wind power forecasting model based on data mining and wavelets analysis

    International Nuclear Information System (INIS)

    Azimi, R.; Ghofrani, M.; Ghayekhloo, M.

    2016-01-01

    Highlights: • An improved version of K-means algorithm is proposed for clustering wind data. • A persistence based method is applied to select the best cluster for NN training. • A combination of DWT and HANTS methods is used to provide a deep learning for NN. • A hybrid of T.S.B K-means, DWT and HANTS and NN is developed for wind forecasting. - Abstract: Accurate forecasting of wind power plays a key role in energy balancing and wind power integration into the grid. This paper proposes a novel time-series based K-means clustering method, named T.S.B K-means, and a cluster selection algorithm to better extract features of wind time-series data. A hybrid of T.S.B K-means, discrete wavelet transform (DWT) and harmonic analysis time series (HANTS) methods, and a multilayer perceptron neural network (MLPNN) is developed for wind power forecasting. The proposed T.S.B K-means classifies data into separate groups and leads to more appropriate learning for neural networks by identifying anomalies and irregular patterns. This improves the accuracy of the forecast results. A cluster selection method is developed to determine the cluster that provides the best training for the MLPNN. This significantly accelerates the forecast process as the most appropriate portion of the data rather than the whole data is used for the NN training. The wind power data is decomposed by the Daubechies D4 wavelet transform, filtered by the HANTS, and pre-processed to provide the most appropriate inputs for the MLPNN. Time-series analysis is used to pre-process the historical wind-power generation data and structure it into input-output series. Wind power datasets with diverse characteristics, from different wind farms located in the United States, are used to evaluate the accuracy of the hybrid forecasting method through various performance measures and different experiments. A comparative analysis with well-established forecasting models shows the superior performance of the proposed

  10. Accurate Short-Term Power Forecasting of Wind Turbines: The Case of Jeju Island’s Wind Farm

    OpenAIRE

    BeomJun Park; Jin Hur

    2017-01-01

    Short-term wind power forecasting is a technique which tells system operators how much wind power can be expected at a specific time. Due to the increasing penetration of wind generating resources into the power grids, short-term wind power forecasting is becoming an important issue for grid integration analysis. The high reliability of wind power forecasting can contribute to the successful integration of wind generating resources into the power grids. To guarantee the reliability of forecas...

  11. Fitting and forecasting coupled dark energy in the non-linear regime

    Energy Technology Data Exchange (ETDEWEB)

    Casas, Santiago; Amendola, Luca; Pettorino, Valeria; Vollmer, Adrian [Institut für Theoretische Physik, Ruprecht-Karls-Universität Heidelberg, Philosophenweg 16, Heidelberg, 69120 Germany (Germany); Baldi, Marco, E-mail: casas@thphys.uni-heidelberg.de, E-mail: l.amendola@thphys.uni-heidelberg.de, E-mail: mail@marcobaldi.it, E-mail: v.pettorino@thphys.uni-heidelberg.de, E-mail: vollmer@thphys.uni-heidelberg.de [Dipartimento di Fisica e Astronomia, Alma Mater Studiorum Università di Bologna, viale Berti Pichat, 6/2, Bologna, I-40127 Italy (Italy)

    2016-01-01

    We consider cosmological models in which dark matter feels a fifth force mediated by the dark energy scalar field, also known as coupled dark energy. Our interest resides in estimating forecasts for future surveys like Euclid when we take into account non-linear effects, relying on new fitting functions that reproduce the non-linear matter power spectrum obtained from N-body simulations. We obtain fitting functions for models in which the dark matter-dark energy coupling is constant. Their validity is demonstrated for all available simulations in the redshift range 0z=–1.6 and wave modes below 0k=1 h/Mpc. These fitting formulas can be used to test the predictions of the model in the non-linear regime without the need for additional computing-intensive N-body simulations. We then use these fitting functions to perform forecasts on the constraining power that future galaxy-redshift surveys like Euclid will have on the coupling parameter, using the Fisher matrix method for galaxy clustering (GC) and weak lensing (WL). We find that by using information in the non-linear power spectrum, and combining the GC and WL probes, we can constrain the dark matter-dark energy coupling constant squared, β{sup 2}, with precision smaller than 4% and all other cosmological parameters better than 1%, which is a considerable improvement of more than an order of magnitude compared to corresponding linear power spectrum forecasts with the same survey specifications.

  12. Fitting and forecasting coupled dark energy in the non-linear regime

    International Nuclear Information System (INIS)

    Casas, Santiago; Amendola, Luca; Pettorino, Valeria; Vollmer, Adrian; Baldi, Marco

    2016-01-01

    We consider cosmological models in which dark matter feels a fifth force mediated by the dark energy scalar field, also known as coupled dark energy. Our interest resides in estimating forecasts for future surveys like Euclid when we take into account non-linear effects, relying on new fitting functions that reproduce the non-linear matter power spectrum obtained from N-body simulations. We obtain fitting functions for models in which the dark matter-dark energy coupling is constant. Their validity is demonstrated for all available simulations in the redshift range 0z=–1.6 and wave modes below 0k=1 h/Mpc. These fitting formulas can be used to test the predictions of the model in the non-linear regime without the need for additional computing-intensive N-body simulations. We then use these fitting functions to perform forecasts on the constraining power that future galaxy-redshift surveys like Euclid will have on the coupling parameter, using the Fisher matrix method for galaxy clustering (GC) and weak lensing (WL). We find that by using information in the non-linear power spectrum, and combining the GC and WL probes, we can constrain the dark matter-dark energy coupling constant squared, β 2 , with precision smaller than 4% and all other cosmological parameters better than 1%, which is a considerable improvement of more than an order of magnitude compared to corresponding linear power spectrum forecasts with the same survey specifications

  13. Forecasting Fossil Fuel Energy Consumption for Power Generation Using QHSA-Based LSSVM Model

    Directory of Open Access Journals (Sweden)

    Wei Sun

    2015-01-01

    Full Text Available Accurate forecasting of fossil fuel energy consumption for power generation is important and fundamental for rational power energy planning in the electricity industry. The least squares support vector machine (LSSVM is a powerful methodology for solving nonlinear forecasting issues with small samples. The key point is how to determine the appropriate parameters which have great effect on the performance of LSSVM model. In this paper, a novel hybrid quantum harmony search algorithm-based LSSVM (QHSA-LSSVM energy forecasting model is proposed. The QHSA which combines the quantum computation theory and harmony search algorithm is applied to searching the optimal values of and C in LSSVM model to enhance the learning and generalization ability. The case study on annual fossil fuel energy consumption for power generation in China shows that the proposed model outperforms other four comparative models, namely regression, grey model (1, 1 (GM (1, 1, back propagation (BP and LSSVM, in terms of prediction accuracy and forecasting risk.

  14. Wind speed power spectrum analysis for Bushland, Texas

    Energy Technology Data Exchange (ETDEWEB)

    Eggleston, E.D. [USDA-Agricultural Research Service, Bushland, TX (United States)

    1996-12-31

    Numerous papers and publications on wind turbulence have referenced the wind speed spectrum presented by Isaac Van der Hoven in his article entitled Power Spectrum of Horizontal Wind Speed Spectrum in the Frequency Range from 0.0007 to 900 Cycles per Hour. Van der Hoven used data measured at different heights between 91 and 125 meters above the ground, and represented the high frequency end of the spectrum with data from the peak hour of hurricane Connie. These facts suggest we should question the use of his power spectrum in the wind industry. During the USDA - Agricultural Research Service`s investigation of wind/diesel system power storage, using the appropriate wind speed power spectrum became a significant issue. We developed a power spectrum from 13 years of hourly average data, 1 year of 5 minute average data, and 2 particularly gusty day`s 1 second average data all collected at a height of 10 meters. While the general shape is similar to the Van der Hoven spectrum, few of his peaks were found in the Bushland spectrum. While higher average wind speeds tend to suggest higher amplitudes in the high frequency end of the spectrum, this is not always true. Also, the high frequency end of the spectrum is not accurately described by simple wind statistics such as standard deviation and turbulence intensity. 2 refs., 5 figs., 1 tab.

  15. A Simple and Effective Approach for the Prediction of Turbine Power Production From Wind Speed Forecast

    Directory of Open Access Journals (Sweden)

    Marino Marrocu

    2017-11-01

    Full Text Available An accurate forecast of the power generated by a wind turbine is of paramount importance for its optimal exploitation. Several forecasting methods have been proposed either based on a physical modeling or using a statistical approach. All of them rely on the availability of high quality measures of local wind speed, corresponding generated power and on numerical weather forecasts. In this paper, a simple and effective wind power forecast technique, based on the probability distribution mapping of wind speed forecast and observed power data, is presented and it is applied to two turbines located on the island of Borkum (Germany in the North Sea. The wind speed forecast of the ECMWF model at 100 m from the ground is used as the prognostic meteorological parameter. Training procedures are based entirely on relatively short time series of power measurements. Results show that our approach has skills that are similar or better than those obtained using more standard methods when measured with mean absolute error.

  16. Determining the bounds of skilful forecast range for probabilistic prediction of system-wide wind power generation

    Directory of Open Access Journals (Sweden)

    Dirk Cannon

    2017-06-01

    Full Text Available State-of-the-art wind power forecasts beyond a few hours ahead rely on global numerical weather prediction models to forecast the future large-scale atmospheric state. Often they provide initial and boundary conditions for nested high resolution simulations. In this paper, both upper and lower bounds on forecast range are identified within which global ensemble forecasts provide skilful information for system-wide wind power applications. An upper bound on forecast range is associated with the limit of predictability, beyond which forecasts have no more skill than predictions based on climatological statistics. A lower bound is defined at the lead time beyond which the resolved uncertainty associated with estimating the future large-scale atmospheric state is larger than the unresolved uncertainty associated with estimating the system-wide wind power response to a given large-scale state.The bounds of skilful ensemble forecast range are quantified for three leading global forecast systems. The power system of Great Britain (GB is used as an example because independent verifying data is available from National Grid. The upper bound defined by forecasts of GB-total wind power generation at a specific point in time is found to be 6–8 days. The lower bound is found to be 1.4–2.4 days. Both bounds depend on the global forecast system and vary seasonally. In addition, forecasts of the probability of an extreme power ramp event were found to possess a shorter limit of predictability (4.5–5.5 days. The upper bound on this forecast range can only be extended by improving the global forecast system (outside the control of most users or by changing the metric used in the probability forecast. Improved downscaling and microscale modelling of the wind farm response may act to decrease the lower bound. The potential gain from such improvements have diminishing returns beyond the short-range (out to around 2 days.

  17. Solar PV Power Forecasting Using Extreme Learning Machine and Information Fusion

    OpenAIRE

    Le Cadre , Hélène; Aravena , Ignacio; Papavasiliou , Anthony

    2015-01-01

    International audience; We provide a learning algorithm combining distributed Extreme Learning Machine and an information fusion rule based on the ag-gregation of experts advice, to build day ahead probabilistic solar PV power production forecasts. These forecasts use, apart from the current day solar PV power production, local meteorological inputs, the most valuable of which is shown to be precipitation. Experiments are then run in one French region, Provence-Alpes-Côte d'Azur, to evaluate ...

  18. Probabilistic Forecasts of Wind Power Generation by Stochastic Differential Equation Models

    DEFF Research Database (Denmark)

    Møller, Jan Kloppenborg; Zugno, Marco; Madsen, Henrik

    2016-01-01

    The increasing penetration of wind power has resulted in larger shares of volatile sources of supply in power systems worldwide. In order to operate such systems efficiently, methods for reliable probabilistic forecasts of future wind power production are essential. It is well known...... that the conditional density of wind power production is highly dependent on the level of predicted wind power and prediction horizon. This paper describes a new approach for wind power forecasting based on logistic-type stochastic differential equations (SDEs). The SDE formulation allows us to calculate both state......-dependent conditional uncertainties as well as correlation structures. Model estimation is performed by maximizing the likelihood of a multidimensional random vector while accounting for the correlation structure defined by the SDE formulation. We use non-parametric modelling to explore conditional correlation...

  19. TradeWind Deliverable 2.2: Forecast error of aggregated wind power

    DEFF Research Database (Denmark)

    Giebel, Gregor; Sørensen, Poul Ejnar; Holttinen, Hannele

    2007-01-01

    Estimates of forecast error of aggregated production for time horizons of intraday and dayahead markets in future will be produced. This will be done by reference to published studies of forecasting for wind generation, and from internal knowledge of WP2 participants. Modelling of wind power fluctuations...

  20. SINGULAR SPECTRUM ANALYSIS: METHODOLOGY AND APPLICATION TO ECONOMICS DATA

    Institute of Scientific and Technical Information of China (English)

    Hossein HASSANI; Anatoly ZHIGLJAVSKY

    2009-01-01

    This paper describes the methodology of singular spectrum analysis (SSA) and demonstrate that it is a powerful method of time series analysis and forecasting, particulary for economic time series. The authors consider the application of SSA to the analysis and forecasting of the Iranian national accounts data as provided by the Central Bank of the Islamic Republic of lran.

  1. Response approach to the squeezed-limit bispectrum: application to the correlation of quasar and Lyman-α forest power spectrum

    Energy Technology Data Exchange (ETDEWEB)

    Chiang, Chi-Ting [C.N. Yang Institute for Theoretical Physics, Stony Brook University, Stony Brook, NY 11794 (United States); Cieplak, Agnieszka M.; Slosar, Anže [Brookhaven National Laboratory, Blgd 510, Upton, NY 11375 (United States); Schmidt, Fabian, E-mail: chi-ting.chiang@stonybrook.edu, E-mail: acieplak@bnl.gov, E-mail: fabians@mpa-garching.mpg.de, E-mail: anze@bnl.gov [Max-Planck-Institut für Astrophysik, Karl-Schwarzschild-Str. 1, 85741 Garching (Germany)

    2017-06-01

    The squeezed-limit bispectrum, which is generated by nonlinear gravitational evolution as well as inflationary physics, measures the correlation of three wavenumbers, in the configuration where one wavenumber is much smaller than the other two. Since the squeezed-limit bispectrum encodes the impact of a large-scale fluctuation on the small-scale power spectrum, it can be understood as how the small-scale power spectrum ''responds'' to the large-scale fluctuation. Viewed in this way, the squeezed-limit bispectrum can be calculated using the response approach even in the cases which do not submit to perturbative treatment. To illustrate this point, we apply this approach to the cross-correlation between the large-scale quasar density field and small-scale Lyman-α forest flux power spectrum. In particular, using separate universe simulations which implement changes in the large-scale density, velocity gradient, and primordial power spectrum amplitude, we measure how the Lyman-α forest flux power spectrum responds to the local, long-wavelength quasar overdensity, and equivalently their squeezed-limit bispectrum. We perform a Fisher forecast for the ability of future experiments to constrain local non-Gaussianity using the bispectrum of quasars and the Lyman-α forest. Combining with quasar and Lyman-α forest power spectra to constrain the biases, we find that for DESI the expected 1−σ constraint is err[ f {sub NL}]∼60. Ability for DESI to measure f {sub NL} through this channel is limited primarily by the aliasing and instrumental noise of the Lyman-α forest flux power spectrum. The combination of response approach and separate universe simulations provides a novel technique to explore the constraints from the squeezed-limit bispectrum between different observables.

  2. Short-Term Wind Power Forecasting Using the Enhanced Particle Swarm Optimization Based Hybrid Method

    OpenAIRE

    Wen-Yeau Chang

    2013-01-01

    High penetration of wind power in the electricity system provides many challenges to power system operators, mainly due to the unpredictability and variability of wind power generation. Although wind energy may not be dispatched, an accurate forecasting method of wind speed and power generation can help power system operators reduce the risk of an unreliable electricity supply. This paper proposes an enhanced particle swarm optimization (EPSO) based hybrid forecasting method for short-term wi...

  3. A General Probabilistic Forecasting Framework for Offshore Wind Power Fluctuations

    Directory of Open Access Journals (Sweden)

    Henrik Madsen

    2012-03-01

    Full Text Available Accurate wind power forecasts highly contribute to the integration of wind power into power systems. The focus of the present study is on large-scale offshore wind farms and the complexity of generating accurate probabilistic forecasts of wind power fluctuations at time-scales of a few minutes. Such complexity is addressed from three perspectives: (i the modeling of a nonlinear and non-stationary stochastic process; (ii the practical implementation of the model we proposed; (iii the gap between working on synthetic data and real world observations. At time-scales of a few minutes, offshore fluctuations are characterized by highly volatile dynamics which are difficult to capture and predict. Due to the lack of adequate on-site meteorological observations to relate these dynamics to meteorological phenomena, we propose a general model formulation based on a statistical approach and historical wind power measurements only. We introduce an advanced Markov Chain Monte Carlo (MCMC estimation method to account for the different features observed in an empirical time series of wind power: autocorrelation, heteroscedasticity and regime-switching. The model we propose is an extension of Markov-Switching Autoregressive (MSAR models with Generalized AutoRegressive Conditional Heteroscedastic (GARCH errors in each regime to cope with the heteroscedasticity. Then, we analyze the predictive power of our model on a one-step ahead exercise of time series sampled over 10 min intervals. Its performances are compared to state-of-the-art models and highlight the interest of including a GARCH specification for density forecasts.

  4. Mid-term load forecasting of power systems by a new prediction method

    International Nuclear Information System (INIS)

    Amjady, Nima; Keynia, Farshid

    2008-01-01

    Mid-term load forecasting (MTLF) becomes an essential tool for today power systems, mainly in those countries whose power systems operate in a deregulated environment. Among different kinds of MTLF, this paper focuses on the prediction of daily peak load for one month ahead. This kind of load forecast has many applications like maintenance scheduling, mid-term hydro thermal coordination, adequacy assessment, management of limited energy units, negotiation of forward contracts, and development of cost efficient fuel purchasing strategies. However, daily peak load is a nonlinear, volatile, and nonstationary signal. Besides, lack of sufficient data usually further complicates this problem. The paper proposes a new methodology to solve it, composed of an efficient data model, preforecast mechanism and combination of neural network and evolutionary algorithm as the hybrid forecast technique. The proposed methodology is examined on the EUropean Network on Intelligent TEchnologies (EUNITE) test data and Iran's power system. We will also compare our strategy with the other MTLF methods revealing its capability to solve this load forecast problem

  5. Short-term load and wind power forecasting using neural network-based prediction intervals.

    Science.gov (United States)

    Quan, Hao; Srinivasan, Dipti; Khosravi, Abbas

    2014-02-01

    Electrical power systems are evolving from today's centralized bulk systems to more decentralized systems. Penetrations of renewable energies, such as wind and solar power, significantly increase the level of uncertainty in power systems. Accurate load forecasting becomes more complex, yet more important for management of power systems. Traditional methods for generating point forecasts of load demands cannot properly handle uncertainties in system operations. To quantify potential uncertainties associated with forecasts, this paper implements a neural network (NN)-based method for the construction of prediction intervals (PIs). A newly introduced method, called lower upper bound estimation (LUBE), is applied and extended to develop PIs using NN models. A new problem formulation is proposed, which translates the primary multiobjective problem into a constrained single-objective problem. Compared with the cost function, this new formulation is closer to the primary problem and has fewer parameters. Particle swarm optimization (PSO) integrated with the mutation operator is used to solve the problem. Electrical demands from Singapore and New South Wales (Australia), as well as wind power generation from Capital Wind Farm, are used to validate the PSO-based LUBE method. Comparative results show that the proposed method can construct higher quality PIs for load and wind power generation forecasts in a short time.

  6. Short-term Power Load Forecasting Based on Balanced KNN

    Science.gov (United States)

    Lv, Xianlong; Cheng, Xingong; YanShuang; Tang, Yan-mei

    2018-03-01

    To improve the accuracy of load forecasting, a short-term load forecasting model based on balanced KNN algorithm is proposed; According to the load characteristics, the historical data of massive power load are divided into scenes by the K-means algorithm; In view of unbalanced load scenes, the balanced KNN algorithm is proposed to classify the scene accurately; The local weighted linear regression algorithm is used to fitting and predict the load; Adopting the Apache Hadoop programming framework of cloud computing, the proposed algorithm model is parallelized and improved to enhance its ability of dealing with massive and high-dimension data. The analysis of the household electricity consumption data for a residential district is done by 23-nodes cloud computing cluster, and experimental results show that the load forecasting accuracy and execution time by the proposed model are the better than those of traditional forecasting algorithm.

  7. Modelling the TSZ power spectrum

    Energy Technology Data Exchange (ETDEWEB)

    Bhattacharya, Suman [Los Alamos National Laboratory; Shaw, Laurie D [YALE; Nagai, Daisuke [YALE

    2010-01-01

    The structure formation in university is a hierarchical process. As universe evolves, tiny density fluctuations that existed in the early universe grows under gravitational instability to form massive large scale structures. The galaxy clusters are the massive viralized objects that forms by accreting smaller clumps of mass until they collapse under their self-gravity. As such galaxy clusters are the youngest objects in the universe which makes their abundance as a function of mass and redshift, very sensitive to dark energy. Galaxy clusters can be detected by measuring the richness in optical waveband, by measuring the X-ray flux, and in the microwave sky using Sunyaev-Zel'dovich (SZ) effect. The Sunyaev-Zel'dovich (SZ) effect has long been recognized as a powerful tool for detecting clusters and probing the physics of the intra-cluster medium. Ongoing and future experiments like Atacama Cosmology Telescope, the South Pole Telescope and Planck survey are currently surveying the microwave sky to develop large catalogs of galaxy clusters that are uniformly selected by the SZ flux. However one major systematic uncertainties that cluster abundance is prone to is the connection between the cluster mass and the SZ flux. As shown by several simulation studies, the scatter and bias in the SZ flux-mass relation can be a potential source of systematic error to using clusters as a cosmology probe. In this study they take a semi-analytic approach for modeling the intra-cluster medium in order to predict the tSZ power spectrum. The advantage of this approach is, being analytic, one can vary the parameters describing gas physics and cosmology simultaneously. The model can be calibrated against X-ray observations of massive, low-z clusters, and using the SZ power spectrum which is sourced by high-z lower mass galaxy groups. This approach allows us to include the uncertainty in gas physics, as dictated by the current observational uncertainties, while measuring the

  8. Evaluation of Wind Power Forecasts from the Vermont Weather Analytics Center and Identification of Improvements

    Energy Technology Data Exchange (ETDEWEB)

    Optis, Michael [National Renewable Energy Lab. (NREL), Golden, CO (United States); Scott, George N. [National Renewable Energy Lab. (NREL), Golden, CO (United States); Draxl, Caroline [National Renewable Energy Lab. (NREL), Golden, CO (United States)

    2018-02-02

    The goal of this analysis was to assess the wind power forecast accuracy of the Vermont Weather Analytics Center (VTWAC) forecast system and to identify potential improvements to the forecasts. Based on the analysis at Georgia Mountain, the following recommendations for improving forecast performance were made: 1. Resolve the significant negative forecast bias in February-March 2017 (50% underprediction on average) 2. Improve the ability of the forecast model to capture the strong diurnal cycle of wind power 3. Add ability for forecast model to assess internal wake loss, particularly at sites where strong diurnal shifts in wind direction are present. Data availability and quality limited the robustness of this forecast assessment. A more thorough analysis would be possible given a longer period of record for the data (at least one full year), detailed supervisory control and data acquisition data for each wind plant, and more detailed information on the forecast system input data and methodologies.

  9. Using Quantile Regression to Extend an Existing Wind Power Forecasting System with Probabilistic Forecasts

    DEFF Research Database (Denmark)

    Nielsen, Henrik Aalborg; Madsen, Henrik; Nielsen, Torben Skov

    2006-01-01

    speed (due to the non-linearity of the power curve) and the forecast horizon. With respect to the predictability of the actual meteorological situation a number of explanatory variables are considered, some inspired by the literature. The article contains an overview of related work within the field...

  10. Electric power demand forecasting using interval time series. A comparison between VAR and iMLP

    International Nuclear Information System (INIS)

    Garcia-Ascanio, Carolina; Mate, Carlos

    2010-01-01

    Electric power demand forecasts play an essential role in the electric industry, as they provide the basis for making decisions in power system planning and operation. A great variety of mathematical methods have been used for demand forecasting. The development and improvement of appropriate mathematical tools will lead to more accurate demand forecasting techniques. In order to forecast the monthly electric power demand per hour in Spain for 2 years, this paper presents a comparison between a new forecasting approach considering vector autoregressive (VAR) forecasting models applied to interval time series (ITS) and the iMLP, the multi-layer perceptron model adapted to interval data. In the proposed comparison, for the VAR approach two models are fitted per every hour, one composed of the centre (mid-point) and radius (half-range), and another one of the lower and upper bounds according to the interval representation assumed by the ITS in the learning set. In the case of the iMLP, only the model composed of the centre and radius is fitted. The other interval representation composed of the lower and upper bounds is obtained from the linear combination of the two. This novel approach, obtaining two bivariate models each hour, makes possible to establish, for different periods in the day, which interval representation is more accurate. Furthermore, the comparison between two different techniques adapted to interval time series allows us to determine the efficiency of these models in forecasting electric power demand. It is important to note that the iMLP technique has been selected for the comparison, as it has shown its accuracy in forecasting daily electricity price intervals. This work shows the ITS forecasting methods as a potential tool that will lead to a reduction in risk when making power system planning and operational decisions. (author)

  11. Theory Study and Application of the BP-ANN Method for Power Grid Short-Term Load Forecasting

    Institute of Scientific and Technical Information of China (English)

    Xia Hua; Gang Zhang; Jiawei Yang; Zhengyuan Li

    2015-01-01

    Aiming at the low accuracy problem of power system short⁃term load forecasting by traditional methods, a back⁃propagation artifi⁃cial neural network (BP⁃ANN) based method for short⁃term load forecasting is presented in this paper. The forecast points are re⁃lated to prophase adjacent data as well as the periodical long⁃term historical load data. Then the short⁃term load forecasting model of Shanxi Power Grid (China) based on BP⁃ANN method and correlation analysis is established. The simulation model matches well with practical power system load, indicating the BP⁃ANN method is simple and with higher precision and practicality.

  12. Maximizing Statistical Power When Verifying Probabilistic Forecasts of Hydrometeorological Events

    Science.gov (United States)

    DeChant, C. M.; Moradkhani, H.

    2014-12-01

    Hydrometeorological events (i.e. floods, droughts, precipitation) are increasingly being forecasted probabilistically, owing to the uncertainties in the underlying causes of the phenomenon. In these forecasts, the probability of the event, over some lead time, is estimated based on some model simulations or predictive indicators. By issuing probabilistic forecasts, agencies may communicate the uncertainty in the event occurring. Assuming that the assigned probability of the event is correct, which is referred to as a reliable forecast, the end user may perform some risk management based on the potential damages resulting from the event. Alternatively, an unreliable forecast may give false impressions of the actual risk, leading to improper decision making when protecting resources from extreme events. Due to this requisite for reliable forecasts to perform effective risk management, this study takes a renewed look at reliability assessment in event forecasts. Illustrative experiments will be presented, showing deficiencies in the commonly available approaches (Brier Score, Reliability Diagram). Overall, it is shown that the conventional reliability assessment techniques do not maximize the ability to distinguish between a reliable and unreliable forecast. In this regard, a theoretical formulation of the probabilistic event forecast verification framework will be presented. From this analysis, hypothesis testing with the Poisson-Binomial distribution is the most exact model available for the verification framework, and therefore maximizes one's ability to distinguish between a reliable and unreliable forecast. Application of this verification system was also examined within a real forecasting case study, highlighting the additional statistical power provided with the use of the Poisson-Binomial distribution.

  13. Solar PV power forecasting using extreme machine learning and experts advice fusion

    OpenAIRE

    Le Cadre, Hélène; Aravena Solís, Ignacio Andrés; Papavasiliou, Anthony; European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

    2015-01-01

    We provide a learning algorithm combining distributed Extreme Learning Machine and an information fusion rule based on the aggregation of experts advice, to build day ahead probabilistic solar PV power production forecasts. These forecasts use, apart from the current day solar PV power production, local meteorological inputs, the most valuable of which is shown to be precipitation. Experiments are then run in one French region, Provence-Alpes-Côte d’Azur, to evaluate the algorithm performance...

  14. Powering Up With Space-Time Wind Forecasting

    KAUST Repository

    Hering, Amanda S.

    2010-03-01

    The technology to harvest electricity from wind energy is now advanced enough to make entire cities powered by it a reality. High-quality, short-term forecasts of wind speed are vital to making this a more reliable energy source. Gneiting et al. (2006) have introduced a model for the average wind speed two hours ahead based on both spatial and temporal information. The forecasts produced by this model are accurate, and subject to accuracy, the predictive distribution is sharp, that is, highly concentrated around its center. However, this model is split into nonunique regimes based on the wind direction at an offsite location. This paper both generalizes and improves upon this model by treating wind direction as a circular variable and including it in the model. It is robust in many experiments, such as predicting wind at other locations. We compare this with the more common approach of modeling wind speeds and directions in the Cartesian space and use a skew-t distribution for the errors. The quality of the predictions from all of these models can be more realistically assessed with a loss measure that depends upon the power curve relating wind speed to power output. This proposed loss measure yields more insight into the true value of each models predictions. © 2010 American Statistical Association.

  15. Powering Up With Space-Time Wind Forecasting

    KAUST Repository

    Hering, Amanda S.; Genton, Marc G.

    2010-01-01

    The technology to harvest electricity from wind energy is now advanced enough to make entire cities powered by it a reality. High-quality, short-term forecasts of wind speed are vital to making this a more reliable energy source. Gneiting et al. (2006) have introduced a model for the average wind speed two hours ahead based on both spatial and temporal information. The forecasts produced by this model are accurate, and subject to accuracy, the predictive distribution is sharp, that is, highly concentrated around its center. However, this model is split into nonunique regimes based on the wind direction at an offsite location. This paper both generalizes and improves upon this model by treating wind direction as a circular variable and including it in the model. It is robust in many experiments, such as predicting wind at other locations. We compare this with the more common approach of modeling wind speeds and directions in the Cartesian space and use a skew-t distribution for the errors. The quality of the predictions from all of these models can be more realistically assessed with a loss measure that depends upon the power curve relating wind speed to power output. This proposed loss measure yields more insight into the true value of each models predictions. © 2010 American Statistical Association.

  16. Benefits of spatiotemporal modeling for short-term wind power forecasting at both individual and aggregated levels

    DEFF Research Database (Denmark)

    Lenzi, Amanda; Steinsland, Ingelin; Pinson, Pierre

    2018-01-01

    The share of wind energy in total installed power capacity has grown rapidly in recent years. Producing accurate and reliable forecasts of wind power production, together with a quantification of the uncertainty, is essential to optimally integrate wind energy into power systems. We build...... spatiotemporal models for wind power generation and obtain full probabilistic forecasts from 15 min to 5 h ahead. Detailed analyses of forecast performances on individual wind farms and aggregated wind power are provided. The predictions from our models are evaluated on a data set from wind farms in western...... Denmark using a sliding window approach, for which estimation is performed using only the last available measurements. The case study shows that it is important to have a spatiotemporal model instead of a temporal one to achieve calibrated aggregated forecasts. Furthermore, spatiotemporal models have...

  17. Day-Ahead Wind Power Forecasting Using a Two-Stage Hybrid Modeling Approach Based on SCADA and Meteorological Information, and Evaluating the Impact of Input-Data Dependency on Forecasting Accuracy

    Directory of Open Access Journals (Sweden)

    Dehua Zheng

    2017-12-01

    Full Text Available The power generated by wind generators is usually associated with uncertainties, due to the intermittency of wind speed and other weather variables. This creates a big challenge for transmission system operators (TSOs and distribution system operators (DSOs in terms of connecting, controlling and managing power networks with high-penetration wind energy. Hence, in these power networks, accurate wind power forecasts are essential for their reliable and efficient operation. They support TSOs and DSOs in enhancing the control and management of the power network. In this paper, a novel two-stage hybrid approach based on the combination of the Hilbert-Huang transform (HHT, genetic algorithm (GA and artificial neural network (ANN is proposed for day-ahead wind power forecasting. The approach is composed of two stages. The first stage utilizes numerical weather prediction (NWP meteorological information to predict wind speed at the exact site of the wind farm. The second stage maps actual wind speed vs. power characteristics recorded by SCADA. Then, the wind speed forecast in the first stage for the future day is fed to the second stage to predict the future day’s wind power. Comparative selection of input-data parameter sets for the forecasting model and impact analysis of input-data dependency on forecasting accuracy have also been studied. The proposed approach achieves significant forecasting accuracy improvement compared with three other artificial intelligence-based forecasting approaches and a benchmark model using the smart persistence method.

  18. Investigating the Correlation Between Wind and Solar Power Forecast Errors in the Western Interconnection: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, J.; Hodge, B. M.; Florita, A.

    2013-05-01

    Wind and solar power generations differ from conventional energy generation because of the variable and uncertain nature of their power output. This variability and uncertainty can have significant impacts on grid operations. Thus, short-term forecasting of wind and solar generation is uniquely helpful for power system operations to balance supply and demand in an electricity system. This paper investigates the correlation between wind and solar power forecasting errors.

  19. Data on Support Vector Machines (SVM model to forecast photovoltaic power

    Directory of Open Access Journals (Sweden)

    M. Malvoni

    2016-12-01

    Full Text Available The data concern the photovoltaic (PV power, forecasted by a hybrid model that considers weather variations and applies a technique to reduce the input data size, as presented in the paper entitled “Photovoltaic forecast based on hybrid pca-lssvm using dimensionality reducted data” (M. Malvoni, M.G. De Giorgi, P.M. Congedo, 2015 [1]. The quadratic Renyi entropy criteria together with the principal component analysis (PCA are applied to the Least Squares Support Vector Machines (LS-SVM to predict the PV power in the day-ahead time frame. The data here shared represent the proposed approach results. Hourly PV power predictions for 1,3,6,12, 24 ahead hours and for different data reduction sizes are provided in Supplementary material.

  20. Probabilistic forecasting of wind power at the minute time-scale with Markov-switching autoregressive models

    DEFF Research Database (Denmark)

    Pinson, Pierre; Madsen, Henrik

    2008-01-01

    Better modelling and forecasting of very short-term power fluctuations at large offshore wind farms may significantly enhance control and management strategies of their power output. The paper introduces a new methodology for modelling and forecasting such very short-term fluctuations. The proposed...... consists in 1-step ahead forecasting exercise on time-series of wind generation with a time resolution of 10 minute. The quality of the introduced forecasting methodology and its interest for better understanding power fluctuations are finally discussed....... methodology is based on a Markov-switching autoregressive model with time-varying coefficients. An advantage of the method is that one can easily derive full predictive densities. The quality of this methodology is demonstrated from the test case of 2 large offshore wind farms in Denmark. The exercise...

  1. Detecting, categorizing and forecasting large romps in wind farm power output using meteorological observations and WPPT

    DEFF Research Database (Denmark)

    Cutler, N.; Kay, M.; Jacka, K.

    2007-01-01

    The Wind Power Prediction Tool (WPPT) has been installed in Australia for the first time, to forecast the power output from the 65MW Roaring 40s Renewable Energy P/L Woolnorth Bluff Point wind form. This article analyses the general performance of WPPT as well as its performance during large romps...... (swings) in power output. In addition to this, detected large ramps are studied in detail and categorized. WPPT combines wind speed and direction forecasts from the Australian Bureau of Meteorology regional numerical weather prediction model, MesoLAPS, with real-time wind power observations to make hourly...... forecasts of the wind farm power output. The general performances of MesoLAPS and WPPTore evaluated over I year using the root mean square error (RMSE). The errors are significantly lower than for basic benchmark forecasts but higher than for many other WPPT installations, where the site conditions...

  2. Mathematic simulation of mining company’s power demand forecast (by example of “Neryungri” coal strip mine)

    Science.gov (United States)

    Antonenkov, D. V.; Solovev, D. B.

    2017-10-01

    The article covers the aspects of forecasting and consideration of the wholesale market environment in generating the power demand forecast. Major mining companies that operate in conditions of the present day power market have to provide a reliable energy demand request for a certain time period ahead, thus ensuring sufficient reduction of financial losses associated with deviations of the actual power demand from the expected figures. Normally, under the power supply agreement, the consumer is bound to provide a per-month and per-hour request annually. It means that the consumer has to generate one-month-ahead short-term and medium-term hourly forecasts. The authors discovered that empiric distributions of “Yakutugol”, Holding Joint Stock Company, power demand belong to the sustainable rank parameter H-distribution type used for generating forecasts based on extrapolation of such distribution parameters. For this reason they justify the need to apply the mathematic rank analysis in short-term forecasting of the contracted power demand of “Neryungri” coil strip mine being a component of the technocenosis-type system of the mining company “Yakutugol”, Holding JSC.

  3. The Atacama Cosmology Telescope: A Measurement of the Primordial Power Spectrum

    Science.gov (United States)

    Hlozek, Renee; Dunkley, Joanna; Addison, Graeme; Appel, John William; Bond, J. Richard; Carvalho, C. Sofia; Das, Sudeep; Devlin, Mark J.; Duenner, Rolando; Essinger-Hileman, Thomas; hide

    2011-01-01

    We present constraints on the primordial power spectrum of adiabatic fluctuations using data from the 2008 Southern Survey of the Atacama Cosmology Telescope (ACT). The angular resolution of ACT provides sensitivity to scales beyond l = 1000 for resolution of multiple peaks in the primordial temperature power spectrum, which enables us to probe the primordial power spectrum of adiabatic scalar perturbations with wavenumbers up to k approx. = 0.2 Mp/c. We find no evidence for deviation from power-law fluctuations over two decades in scale. Matter fluctuations inferred from the primordial temperature power spectrum evolve over cosmic time and can be used to predict the matter power spectrum at late times; we illustrate the overlap of the matter power inferred from CMB measurements (which probe the power spectrum in thc linear regime) with existing probes of galaxy clustering, cluster abundances and weak lensing constraints on the primordial power. This highlights the range of scales probed by current measurement.s of the matter power spectrum.

  4. Combined time-varying forecast based on the proper scoring approach for wind power generation

    DEFF Research Database (Denmark)

    Chen, Xingying; Jiang, Yu; Yu, Kun

    2017-01-01

    Compared with traditional point forecasts, combined forecast have been proposed as an effective method to provide more accurate forecasts than individual model. However, the literature and research focus on wind-power combined forecasts are relatively limited. Here, based on forecasting error...... distribution, a proper scoring approach is applied to combine plausible models to form an overall time-varying model for the next day forecasts, rather than weights-based combination. To validate the effectiveness of the proposed method, real data of 3 years were used for testing. Simulation results...... demonstrate that the proposed method improves the accuracy of overall forecasts, even compared with a numerical weather prediction....

  5. Probabilistic wind power forecasting with online model selection and warped gaussian process

    International Nuclear Information System (INIS)

    Kou, Peng; Liang, Deliang; Gao, Feng; Gao, Lin

    2014-01-01

    Highlights: • A new online ensemble model for the probabilistic wind power forecasting. • Quantifying the non-Gaussian uncertainties in wind power. • Online model selection that tracks the time-varying characteristic of wind generation. • Dynamically altering the input features. • Recursive update of base models. - Abstract: Based on the online model selection and the warped Gaussian process (WGP), this paper presents an ensemble model for the probabilistic wind power forecasting. This model provides the non-Gaussian predictive distributions, which quantify the non-Gaussian uncertainties associated with wind power. In order to follow the time-varying characteristics of wind generation, multiple time dependent base forecasting models and an online model selection strategy are established, thus adaptively selecting the most probable base model for each prediction. WGP is employed as the base model, which handles the non-Gaussian uncertainties in wind power series. Furthermore, a regime switch strategy is designed to modify the input feature set dynamically, thereby enhancing the adaptiveness of the model. In an online learning framework, the base models should also be time adaptive. To achieve this, a recursive algorithm is introduced, thus permitting the online updating of WGP base models. The proposed model has been tested on the actual data collected from both single and aggregated wind farms

  6. A short-term ensemble wind speed forecasting system for wind power applications

    Science.gov (United States)

    Baidya Roy, S.; Traiteur, J. J.; Callicutt, D.; Smith, M.

    2011-12-01

    This study develops an adaptive, blended forecasting system to provide accurate wind speed forecasts 1 hour ahead of time for wind power applications. The system consists of an ensemble of 21 forecasts with different configurations of the Weather Research and Forecasting Single Column Model (WRFSCM) and a persistence model. The ensemble is calibrated against observations for a 2 month period (June-July, 2008) at a potential wind farm site in Illinois using the Bayesian Model Averaging (BMA) technique. The forecasting system is evaluated against observations for August 2008 at the same site. The calibrated ensemble forecasts significantly outperform the forecasts from the uncalibrated ensemble while significantly reducing forecast uncertainty under all environmental stability conditions. The system also generates significantly better forecasts than persistence, autoregressive (AR) and autoregressive moving average (ARMA) models during the morning transition and the diurnal convective regimes. This forecasting system is computationally more efficient than traditional numerical weather prediction models and can generate a calibrated forecast, including model runs and calibration, in approximately 1 minute. Currently, hour-ahead wind speed forecasts are almost exclusively produced using statistical models. However, numerical models have several distinct advantages over statistical models including the potential to provide turbulence forecasts. Hence, there is an urgent need to explore the role of numerical models in short-term wind speed forecasting. This work is a step in that direction and is likely to trigger a debate within the wind speed forecasting community.

  7. A Hybrid Forecasting Model Based on Empirical Mode Decomposition and the Cuckoo Search Algorithm: A Case Study for Power Load

    Directory of Open Access Journals (Sweden)

    Jiani Heng

    2016-01-01

    Full Text Available Power load forecasting always plays a considerable role in the management of a power system, as accurate forecasting provides a guarantee for the daily operation of the power grid. It has been widely demonstrated in forecasting that hybrid forecasts can improve forecast performance compared with individual forecasts. In this paper, a hybrid forecasting approach, comprising Empirical Mode Decomposition, CSA (Cuckoo Search Algorithm, and WNN (Wavelet Neural Network, is proposed. This approach constructs a more valid forecasting structure and more stable results than traditional ANN (Artificial Neural Network models such as BPNN (Back Propagation Neural Network, GABPNN (Back Propagation Neural Network Optimized by Genetic Algorithm, and WNN. To evaluate the forecasting performance of the proposed model, a half-hourly power load in New South Wales of Australia is used as a case study in this paper. The experimental results demonstrate that the proposed hybrid model is not only simple but also able to satisfactorily approximate the actual power load and can be an effective tool in planning and dispatch for smart grids.

  8. Incorporating Wind Power Forecast Uncertainties Into Stochastic Unit Commitment Using Neural Network-Based Prediction Intervals.

    Science.gov (United States)

    Quan, Hao; Srinivasan, Dipti; Khosravi, Abbas

    2015-09-01

    Penetration of renewable energy resources, such as wind and solar power, into power systems significantly increases the uncertainties on system operation, stability, and reliability in smart grids. In this paper, the nonparametric neural network-based prediction intervals (PIs) are implemented for forecast uncertainty quantification. Instead of a single level PI, wind power forecast uncertainties are represented in a list of PIs. These PIs are then decomposed into quantiles of wind power. A new scenario generation method is proposed to handle wind power forecast uncertainties. For each hour, an empirical cumulative distribution function (ECDF) is fitted to these quantile points. The Monte Carlo simulation method is used to generate scenarios from the ECDF. Then the wind power scenarios are incorporated into a stochastic security-constrained unit commitment (SCUC) model. The heuristic genetic algorithm is utilized to solve the stochastic SCUC problem. Five deterministic and four stochastic case studies incorporated with interval forecasts of wind power are implemented. The results of these cases are presented and discussed together. Generation costs, and the scheduled and real-time economic dispatch reserves of different unit commitment strategies are compared. The experimental results show that the stochastic model is more robust than deterministic ones and, thus, decreases the risk in system operations of smart grids.

  9. SOLAR PHOTOVOLTAIC OUTPUT POWER FORECASTING USING BACK PROPAGATION NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    B. Jency Paulin

    2016-01-01

    Full Text Available Solar Energy is an important renewable and unlimited source of energy. Solar photovoltaic power forecasting, is an estimation of the expected power production, that help the grid operators to better manage the electric balance between power demand and supply. Neural network is a computational model that can predict new outcomes from past trends. The artificial neural network is used for photovoltaic plant energy forecasting. The output power for solar photovoltaic cell is predicted on hourly basis. In historical dataset collection process, two dataset was collected and used for analysis. The dataset was provided with three independent attributes and one dependent attributes. The implementation of Artificial Neural Network structure is done by Multilayer Perceptron (MLP and training procedure for neural network is done by error Back Propagation (BP. In order to train and test the neural network, the datasets are divided in the ratio 70:30. The accuracy of prediction can be done by using various error measurement criteria and the performance of neural network is to be noted.

  10. Spatio‐temporal analysis and modeling of short‐term wind power forecast errors

    DEFF Research Database (Denmark)

    Tastu, Julija; Pinson, Pierre; Kotwa, Ewelina

    2011-01-01

    of small size like western Denmark, significant correlation between the various zones is observed for time delays up to 5 h. Wind direction is shown to play a crucial role, while the effect of wind speed is more complex. Nonlinear models permitting capture of the interdependence structure of wind power......Forecasts of wind power production are increasingly being used in various management tasks. So far, such forecasts and related uncertainty information have usually been generated individually for a given site of interest (either a wind farm or a group of wind farms), without properly accounting...

  11. The effects of forecast errors on the merchandising of wind power; Auswirkungen von Prognosefehlern auf die Vermarktung von Windstrom

    Energy Technology Data Exchange (ETDEWEB)

    Roon, Serafin von

    2012-02-28

    A permanent balance between consumption and generation is essential for a stable supply of electricity. In order to ensure this balance, all relevant load data have to be announced for the following day. Consequently, a day-ahead forecast of the wind power generation is required, which also forms the basis for the sale of the wind power at the wholesale market. The main subject of the study is the short-term power supply, which compensates errors in wind power forecasting for balancing the wind power forecast errors at short notice. These forecast errors effects the revenues and the expenses by selling and buying power in the day-ahead, intraday and balance energy market. These price effects resulting from the forecast errors are derived from an empirical analysis. In a scenario for the year 2020 the potential of conventional power plants to supply power at short notice is evaluated from a technical and economic point of view by a time series analysis and a unit commitment simulation.

  12. Imprint of spatial curvature on inflation power spectrum

    International Nuclear Information System (INIS)

    Masso, Eduard; Zsembinszki, Gabriel; Mohanty, Subhendra; Nautiyal, Akhilesh

    2008-01-01

    If the Universe had a large curvature before inflation there is a deviation from the scale invariant perturbations of the inflaton at the beginning of inflation. This may have some effect on the cosmic microwave background anisotropy at large angular scales. We calculate the density perturbations for both open and closed universe cases using the Bunch-Davies vacuum condition on the initial state. We use our power spectrum to calculate the temperature anisotropy spectrum and compare the results with the Wilkinson microwave anisotropy map five year data. We find that our power spectrum gives a lower quadrupole anisotropy when Ω-1>0, but matches the temperature anisotropy calculated from the standard Ratra-Peebles power spectrum at large l. The determination of spatial curvature from temperature anisotropy data is not much affected by the different power spectra which arise from the choice of different boundary conditions for the inflaton perturbation.

  13. All-sky analysis of the general relativistic galaxy power spectrum

    Science.gov (United States)

    Yoo, Jaiyul; Desjacques, Vincent

    2013-07-01

    We perform an all-sky analysis of the general relativistic galaxy power spectrum using the well-developed spherical Fourier decomposition. Spherical Fourier analysis expresses the observed galaxy fluctuation in terms of the spherical harmonics and spherical Bessel functions that are angular and radial eigenfunctions of the Helmholtz equation, providing a natural orthogonal basis for all-sky analysis of the large-scale mode measurements. Accounting for all the relativistic effects in galaxy clustering, we compute the spherical power spectrum and its covariance matrix and compare it to the standard three-dimensional power spectrum to establish a connection. The spherical power spectrum recovers the three-dimensional power spectrum at each wave number k with its angular dependence μk encoded in angular multipole l, and the contributions of the line-of-sight projection to galaxy clustering such as the gravitational lensing effect can be readily accommodated in the spherical Fourier analysis. A complete list of formulas for computing the relativistic spherical galaxy power spectrum is also presented.

  14. An Advanced Bayesian Method for Short-Term Probabilistic Forecasting of the Generation of Wind Power

    Directory of Open Access Journals (Sweden)

    Antonio Bracale

    2015-09-01

    Full Text Available Currently, among renewable distributed generation systems, wind generators are receiving a great deal of interest due to the great economic, technological, and environmental incentives they involve. However, the uncertainties due to the intermittent nature of wind energy make it difficult to operate electrical power systems optimally and make decisions that satisfy the needs of all the stakeholders of the electricity energy market. Thus, there is increasing interest determining how to forecast wind power production accurately. Most the methods that have been published in the relevant literature provided deterministic forecasts even though great interest has been focused recently on probabilistic forecast methods. In this paper, an advanced probabilistic method is proposed for short-term forecasting of wind power production. A mixture of two Weibull distributions was used as a probability function to model the uncertainties associated with wind speed. Then, a Bayesian inference approach with a particularly-effective, autoregressive, integrated, moving-average model was used to determine the parameters of the mixture Weibull distribution. Numerical applications also are presented to provide evidence of the forecasting performance of the Bayesian-based approach.

  15. The Possibility Using the Power Production Function of Complex Variable for Economic Forecasting

    Directory of Open Access Journals (Sweden)

    Sergey Gennadyevich Svetunkov

    2016-09-01

    Full Text Available The possibility of dynamic analysis and forecasting production results using the power production functions of complex variables with real coefficients is considered. This model expands the arsenal of instrumental methods and allows multivariate production forecasts which are unattainable by other methods of real variables as the functions of complex variables simulate the production differently in comparison with the models of real variables. The values of coefficients of the power production functions of complex variables can be calculated for each statistical observation. This allows to consider the change of the coefficients over time, to analyze this trend and predict the values of the coefficients for a given term, thereby to predict the form of the production function, which forecasts the operating results. Thus, the model of the production function with variable coefficients is introduced into the scientific circulation. With this model, the inverse problem of forecasting might be solved, such as the determination of the necessary quantities of labor and capital to achieve the desired operational results. The study is based on the principles of the modern methodology of complex-valued economy, one of its sections is the complex-valued patterns of production functions. In the article, the possibility of economic forecasting is tested on the example of the UK economy. The results of this prediction are compared with the forecasts obtained by other methods, which have led to the conclusion about the effectiveness of the proposed approach and the method of forecasting at the macro levels of production systems. A complex-valued power model of the production function is recommended for the multivariate prediction of sustainable production systems — the global economy, the economies of individual countries, major industries and regions.

  16. Optimal Power Flow for Distribution Systems under Uncertain Forecasts: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Dall' Anese, Emiliano; Baker, Kyri; Summers, Tyler

    2016-12-01

    The paper focuses on distribution systems featuring renewable energy sources and energy storage devices, and develops an optimal power flow (OPF) approach to optimize the system operation in spite of forecasting errors. The proposed method builds on a chance-constrained multi-period AC OPF formulation, where probabilistic constraints are utilized to enforce voltage regulation with a prescribed probability. To enable a computationally affordable solution approach, a convex reformulation of the OPF task is obtained by resorting to i) pertinent linear approximations of the power flow equations, and ii) convex approximations of the chance constraints. Particularly, the approximate chance constraints provide conservative bounds that hold for arbitrary distributions of the forecasting errors. An adaptive optimization strategy is then obtained by embedding the proposed OPF task into a model predictive control framework.

  17. Impact of wind power uncertainty forecasting on the market integration of wind energy in Spain

    International Nuclear Information System (INIS)

    González-Aparicio, I.; Zucker, A.

    2015-01-01

    Highlights: • Reduction wind power forecasting uncertainty for day ahead and intraday markets. • Statistical relationship between total load and wind power generation. • Accurately forecast expected revenues from wind producer’s perspective. - Abstract: The growing share of electricity production from variable renewable energy sources increases the stochastic nature of the power system. This has repercussions on the markets for electricity. Deviations from forecasted production schedules require balancing of a generator’s position within a day. Short term products that are traded on power and/or reserve markets have been developed for this purpose, providing opportunities to actors who can offer flexibility in the short term. The value of flexibility is typically modelled using stochastic scenario extensions of dispatch models which requires, as a first step, understanding the nature of forecast uncertainties. This study provides a new approach for determining the forecast errors of wind power generation in the time period between the closure of the day ahead and the opening of the first intraday session using Spain as an example. The methodology has been developed using time series analysis for the years 2010–2013 to find the explanatory variables of the wind error variability by applying clustering techniques to reduce the range of uncertainty, and regressive techniques to forecast the probability density functions of the intra-day price. This methodology has been tested considering different system actions showing its suitability for developing intra-day bidding strategies and also for the generation of electricity generated from Renewable Energy Sources scenarios. This methodology could help a wind power producer to optimally bid into the intraday market based on more accurate scenarios, increasing their revenues and the system value of wind.

  18. Evaluation of Nonparametric Probabilistic Forecasts of Wind Power

    DEFF Research Database (Denmark)

    Pinson, Pierre; Møller, Jan Kloppenborg; Nielsen, Henrik Aalborg, orlov 31.07.2008

    Predictions of wind power production for horizons up to 48-72 hour ahead comprise a highly valuable input to the methods for the daily management or trading of wind generation. Today, users of wind power predictions are not only provided with point predictions, which are estimates of the most...... likely outcome for each look-ahead time, but also with uncertainty estimates given by probabilistic forecasts. In order to avoid assumptions on the shape of predictive distributions, these probabilistic predictions are produced from nonparametric methods, and then take the form of a single or a set...

  19. THE ATACAMA COSMOLOGY TELESCOPE: A MEASUREMENT OF THE PRIMORDIAL POWER SPECTRUM

    Energy Technology Data Exchange (ETDEWEB)

    Hlozek, Renee; Dunkley, Joanna; Addison, Graeme [Department of Astrophysics, Oxford University, Oxford OX1 3RH (United Kingdom); Appel, John William; Das, Sudeep; Essinger-Hileman, Thomas; Fowler, Joseph W.; Hajian, Amir; Hincks, Adam D. [Joseph Henry Laboratories of Physics, Jadwin Hall, Princeton University, Princeton, NJ 08544 (United States); Bond, J. Richard [Canadian Institute for Theoretical Astrophysics, University of Toronto, Toronto, ON M5S 3H8 (Canada); Carvalho, C. Sofia [IPFN, IST, Av. RoviscoPais, 1049-001Lisboa, Portugal and RCAAM, Academy of Athens, Soranou Efessiou 4, 11-527 Athens (Greece); Devlin, Mark J.; Klein, Jeff [Department of Physics and Astronomy, University of Pennsylvania, 209 South 33rd Street, Philadelphia, PA 19104 (United States); Duenner, Rolando; Gallardo, Patricio [Departamento de Astronomia y Astrofisica, Facultad de Fisica, Pontificia Universidad Catolica de Chile, Casilla 306, Santiago 22 (Chile); Halpern, Mark; Hasselfield, Matthew [Department of Physics and Astronomy, University of British Columbia, Vancouver, BC V6T 1Z4 (Canada); Hilton, Matt [School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD (United Kingdom); Hughes, John P. [Department of Physics and Astronomy, Rutgers, The State University of New Jersey, Piscataway, NJ 08854-8019 (United States); Irwin, Kent D. [NIST Quantum Devices Group, 325 Broadway Mailcode 817.03, Boulder, CO 80305 (United States); and others

    2012-04-10

    We present constraints on the primordial power spectrum of adiabatic fluctuations using data from the 2008 Southern Survey of the Atacama Cosmology Telescope (ACT) in combination with measurements from the Wilkinson Microwave Anisotropy Probe and a prior on the Hubble constant. The angular resolution of ACT provides sensitivity to scales beyond l = 1000 for resolution of multiple peaks in the primordial temperature power spectrum, which enables us to probe the primordial power spectrum of adiabatic scalar perturbations with wavenumbers up to k {approx_equal} 0.2 Mpc{sup -1}. We find no evidence for deviation from power-law fluctuations over two decades in scale. Matter fluctuations inferred from the primordial temperature power spectrum evolve over cosmic time and can be used to predict the matter power spectrum at late times; we illustrate the overlap of the matter power inferred from cosmic microwave background measurements (which probe the power spectrum in the linear regime) with existing probes of galaxy clustering, cluster abundances, and weak-lensing constraints on the primordial power. This highlights the range of scales probed by current measurements of the matter power spectrum.

  20. A cosmology forecast toolkit — CosmoLib

    Energy Technology Data Exchange (ETDEWEB)

    Huang, Zhiqi, E-mail: zqhuang@cita.utoronto.ca [CEA, Institut de Physique Théorique, Orme des Merisiers, Saint-Aubin, 91191 Gif-sur-Yvette Cédex (France)

    2012-06-01

    The package CosmoLib is a combination of a cosmological Boltzmann code and a simulation toolkit to forecast the constraints on cosmological parameters from future observations. In this paper we describe the released linear-order part of the package. We discuss the stability and performance of the Boltzmann code. This is written in Newtonian gauge and including dark energy perturbations. In CosmoLib the integrator that computes the CMB angular power spectrum is optimized for a l-by-l brute-force integration, which is useful for studying inflationary models predicting sharp features in the primordial power spectrum of metric fluctuations. As an application, CosmoLib is used to study the axion monodromy inflation model that predicts cosine oscillations in the primordial power spectrum. In contrast to the previous studies by Aich et al. and Meerburg et al., we found no detection or hint of the osicllations. We pointed out that the CAMB code modified by Aich et al. does not have sufficient numerical accuracy. CosmoLib and its documentation are available at http://www.cita.utoronto.ca/∼zqhuang/CosmoLib.

  1. Forecasting Electricity Spot Prices Accounting for Wind Power Predictions

    DEFF Research Database (Denmark)

    Jónsson, Tryggvi; Pinson, Pierre; Nielsen, Henrik Aalborg

    2013-01-01

    A two-step methodology for forecasting of electricity spot prices is introduced, with focus on the impact of predicted system load and wind power generation. The nonlinear and nonstationary influence of these explanatory variables is accommodated in a first step based on a nonparametric and time...

  2. Day ahead forecast of wind power through optimal application of multivariate analyzing methods

    Energy Technology Data Exchange (ETDEWEB)

    Arnoldt, Alexander; Bretschneider, Peter [Fraunhofer Institute for Optronics, System Technology, and Image Exploitation - Application Centre System Technology (IOSB-AST), Ilmenau (Germany). Energy Systems Group

    2011-07-01

    This paper presents two algorithms in identifying input models for artificial neural networks. The algorithms are based on an entropy analysis and an eigenvalue analysis of the correlation matrix. The resulting input models are used for investigating a feed forward and a recurrent artificial neural network structure to simulate a 24 hour forecast of wind power production. The limitation of the forecast error distribution is investigated through successful implementation of hybridization of single forecast models. Errors of the best forecast model stay between a normalized root mean square error from 3.5% to 6.1%. (orig.)

  3. Power/response spectrum transformations in equipment qualification

    International Nuclear Information System (INIS)

    Unruh, J.F.; Kana, D.D.

    1985-01-01

    Since its introduction a few years ago the use of the power/response spectrum transformation has gained considerable interest and acceptance, and a number of new applications of the transformation have been developed in the equipment qualification area. A brief review of the power/response spectrum transformation is given with a discussion of the input/output relationships for linear systems required for elevated power spectrum generation. Frequency content of earthquakelike signals is discussed with emphasis on the resolution given by the PSD. The problem of excessive ZPA due to inconsistent spectra enveloping and mechanical nonlinearities is also discussed. The PSD/RS transformation is applied to the problems of combining various dynamic load events, developing bounding spectra, and developing damping consistent test spectra. Development of elevated component spectra corrected for base overtest and generation from in-situ measurements is reviewed

  4. Forecasting the daily power output of a grid-connected photovoltaic system based on multivariate adaptive regression splines

    International Nuclear Information System (INIS)

    Li, Yanting; He, Yong; Su, Yan; Shu, Lianjie

    2016-01-01

    Highlights: • Suggests a nonparametric model based on MARS for output power prediction. • Compare the MARS model with a wide variety of prediction models. • Show that the MARS model is able to provide an overall good performance in both the training and testing stages. - Abstract: Both linear and nonlinear models have been proposed for forecasting the power output of photovoltaic systems. Linear models are simple to implement but less flexible. Due to the stochastic nature of the power output of PV systems, nonlinear models tend to provide better forecast than linear models. Motivated by this, this paper suggests a fairly simple nonlinear regression model known as multivariate adaptive regression splines (MARS), as an alternative to forecasting of solar power output. The MARS model is a data-driven modeling approach without any assumption about the relationship between the power output and predictors. It maintains simplicity of the classical multiple linear regression (MLR) model while possessing the capability of handling nonlinearity. It is simpler in format than other nonlinear models such as ANN, k-nearest neighbors (KNN), classification and regression tree (CART), and support vector machine (SVM). The MARS model was applied on the daily output of a grid-connected 2.1 kW PV system to provide the 1-day-ahead mean daily forecast of the power output. The comparisons with a wide variety of forecast models show that the MARS model is able to provide reliable forecast performance.

  5. Short-term wind power forecasting in Portugal by neural networks and wavelet transform

    Energy Technology Data Exchange (ETDEWEB)

    Catalao, J.P.S. [Department of Electromechanical Engineering, University of Beira Interior, R. Fonte do Lameiro, 6201-001 Covilha (Portugal); Center for Innovation in Electrical and Energy Engineering, Instituto Superior Tecnico, Technical University of Lisbon, Av. Rovisco Pais, 1049-001 Lisbon (Portugal); Pousinho, H.M.I. [Department of Electromechanical Engineering, University of Beira Interior, R. Fonte do Lameiro, 6201-001 Covilha (Portugal); Mendes, V.M.F. [Department of Electrical Engineering and Automation, Instituto Superior de Engenharia de Lisboa, R. Conselheiro Emidio Navarro, 1950-062 Lisbon (Portugal)

    2011-04-15

    This paper proposes artificial neural networks in combination with wavelet transform for short-term wind power forecasting in Portugal. The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Hence, good forecasting tools play a key role in tackling these challenges. Results from a real-world case study are presented. A comparison is carried out, taking into account the results obtained with other approaches. Finally, conclusions are duly drawn. (author)

  6. Modeling and forecasting of electrical power demands for capacity planning

    International Nuclear Information System (INIS)

    Al-Shobaki, S.; Mohsen, M.

    2007-01-01

    Jordan imports oil from neighboring countries for use in power production. As such, the cost of electricity production is high compared to oil producing countries. It is anticipated that Jordan will face major challenges in trying to meet the growing energy and electricity demands while also developing the energy sector in a way that reduces any adverse impacts on the economy, the environment and social life. This paper described the development of forecasting models to predict future generation and sales loads of electrical power in Jordan. Two models that could be used for the prediction of electrical energy demand in Amman, Jordan were developed and validated. An analysis of the data was also presented. The first model was based on the levels of energy generated by the National Electric Power Company (NEPCO) and the other was based on the levels of energy sold by the company in the same area. The models were compared and the percent error was presented. Energy demand was also forecasted across the next 60 months for both models. Results were then compared with the output of the in-house forecast model used by NEPCO to predict the levels of generated energy needed across the 60 months time period. It was concluded that the NEPCO model predicted energy demand higher than the validated generated data model by an average of 5.25 per cent. 8 refs., 5 tabs., 15 figs

  7. Modeling and forecasting of electrical power demands for capacity planning

    Energy Technology Data Exchange (ETDEWEB)

    Al-Shobaki, S. [Hashemite Univ., Zarka (Jordan). Dept. of Industrial Engineering; Mohsen, M. [Hashemite Univ., Zarka (Jordan). Dept. of Mechanical Engineering

    2007-07-01

    Jordan imports oil from neighboring countries for use in power production. As such, the cost of electricity production is high compared to oil producing countries. It is anticipated that Jordan will face major challenges in trying to meet the growing energy and electricity demands while also developing the energy sector in a way that reduces any adverse impacts on the economy, the environment and social life. This paper described the development of forecasting models to predict future generation and sales loads of electrical power in Jordan. Two models that could be used for the prediction of electrical energy demand in Amman, Jordan were developed and validated. An analysis of the data was also presented. The first model was based on the levels of energy generated by the National Electric Power Company (NEPCO) and the other was based on the levels of energy sold by the company in the same area. The models were compared and the percent error was presented. Energy demand was also forecasted across the next 60 months for both models. Results were then compared with the output of the in-house forecast model used by NEPCO to predict the levels of generated energy needed across the 60 months time period. It was concluded that the NEPCO model predicted energy demand higher than the validated generated data model by an average of 5.25 per cent. 8 refs., 5 tabs., 15 figs.

  8. Application of data mining methods for power forecast of wind power plants

    Energy Technology Data Exchange (ETDEWEB)

    Arnoldt, Alexander; Koenig, Stefan; Bretschneider, Peter [Fraunhofer Institute for Optronics, System Technology, and Image Exploitation - Application Centre System Technology (IOSB-AST), Ilmenau (Germany). Energy Systems Group; Mikut, Ralf [Karlsruhe Institute of Technology (KIT), Eggenstein-Leopoldshafen (DE). Inst. for Applied Computer Science (IAI)

    2010-07-01

    Since the last decade power systems underlie a drastic change due to increased exploitation of renewable energy resources (RES) such as wind and photovoltaic power plants. A result of this process is a significant increase of fluctuating generation in low, middle and high voltage grids. Consequently, impacts on short and middle term capacity planning of power plants occur and must be handled to avoid imbalances between generation and demand at any time. Therefore, forecasts of wind and photovoltaic generation play a very important role. Quality improvements potentially ease planning and lead to cost reductions. This work investigated the dependencies of input parameters. The optimal parameter selection was achieved through application of data mining methods. Finally, the wind power prediction was demonstrated with Artificial Neural Networks and Physical Models. (orig.)

  9. Power spectrum analysis for defect screening in integrated circuit devices

    Science.gov (United States)

    Tangyunyong, Paiboon; Cole Jr., Edward I.; Stein, David J.

    2011-12-01

    A device sample is screened for defects using its power spectrum in response to a dynamic stimulus. The device sample receives a time-varying electrical signal. The power spectrum of the device sample is measured at one of the pins of the device sample. A defect in the device sample can be identified based on results of comparing the power spectrum with one or more power spectra of the device that have a known defect status.

  10. The “Weather Intelligence for Renewable Energies” Benchmarking Exercise on Short-Term Forecasting of Wind and Solar Power Generation

    Directory of Open Access Journals (Sweden)

    Simone Sperati

    2015-09-01

    Full Text Available A benchmarking exercise was organized within the framework of the European Action Weather Intelligence for Renewable Energies (“WIRE” with the purpose of evaluating the performance of state of the art models for short-term renewable energy forecasting. The exercise consisted in forecasting the power output of two wind farms and two photovoltaic power plants, in order to compare the merits of forecasts based on different modeling approaches and input data. It was thus possible to obtain a better knowledge of the state of the art in both wind and solar power forecasting, with an overview and comparison of the principal and the novel approaches that are used today in the field, and to assess the evolution of forecast performance with respect to previous benchmarking exercises. The outcome of this exercise consisted then in proposing new challenges in the renewable power forecasting field and identifying the main areas for improving accuracy in the future.

  11. Ensemble Nonlinear Autoregressive Exogenous Artificial Neural Networks for Short-Term Wind Speed and Power Forecasting.

    Science.gov (United States)

    Men, Zhongxian; Yee, Eugene; Lien, Fue-Sang; Yang, Zhiling; Liu, Yongqian

    2014-01-01

    Short-term wind speed and wind power forecasts (for a 72 h period) are obtained using a nonlinear autoregressive exogenous artificial neural network (ANN) methodology which incorporates either numerical weather prediction or high-resolution computational fluid dynamics wind field information as an exogenous input. An ensemble approach is used to combine the predictions from many candidate ANNs in order to provide improved forecasts for wind speed and power, along with the associated uncertainties in these forecasts. More specifically, the ensemble ANN is used to quantify the uncertainties arising from the network weight initialization and from the unknown structure of the ANN. All members forming the ensemble of neural networks were trained using an efficient particle swarm optimization algorithm. The results of the proposed methodology are validated using wind speed and wind power data obtained from an operational wind farm located in Northern China. The assessment demonstrates that this methodology for wind speed and power forecasting generally provides an improvement in predictive skills when compared to the practice of using an "optimal" weight vector from a single ANN while providing additional information in the form of prediction uncertainty bounds.

  12. Hybrid Power Forecasting Model for Photovoltaic Plants Based on Neural Network with Air Quality Index

    Directory of Open Access Journals (Sweden)

    Idris Khan

    2017-01-01

    Full Text Available High concentration of greenhouse gases in the atmosphere has increased dependency on photovoltaic (PV power, but its random nature poses a challenge for system operators to precisely predict and forecast PV power. The conventional forecasting methods were accurate for clean weather. But when the PV plants worked under heavy haze, the radiation is negatively impacted and thus reducing PV power; therefore, to deal with haze weather, Air Quality Index (AQI is introduced as a parameter to predict PV power. AQI, which is an indication of how polluted the air is, has been known to have a strong correlation with power generated by the PV panels. In this paper, a hybrid method based on the model of conventional back propagation (BP neural network for clear weather and BP AQI model for haze weather is used to forecast PV power with conventional parameters like temperature, wind speed, humidity, solar radiation, and an extra parameter of AQI as input. The results show that the proposed method has less error under haze condition as compared to conventional model of neural network.

  13. Estimating the Crustal Power Spectrum From Vector Magsat Data: Crustal Power Spectrum

    Science.gov (United States)

    Lowe, David A. J.; Parker, Robert L.; Purucker, Michael E.; Constable, Catherine G.

    2000-01-01

    The Earth's magnetic field can be subdivided into core and crustal components and we seek to characterize the crustal part through its spatial power spectrum (R(sub l)). We process vector Magsat data to isolate the crustal field and then invert power spectral densities of flight-local components along-track for R(sub l) following O'Brien et al. [1999]. Our model (LPPC) is accurate up to approximately degree 45 (lambda=900 km) - this is the resolution limit of our data and suggests that global crustal anomaly maps constructed from vector Magsat data should not contain features with wavelengths less than 900 km. We find continental power spectra to be greater than oceanic ones and attribute this to the relative thicknesses of continental and oceanic crust.

  14. Short-Term Wind Electric Power Forecasting Using a Novel Multi-Stage Intelligent Algorithm

    Directory of Open Access Journals (Sweden)

    Haoran Zhao

    2018-03-01

    Full Text Available As the most efficient renewable energy source for generating electricity in a modern electricity network, wind power has the potential to realize sustainable energy supply. However, owing to its random and intermittent instincts, a high permeability of wind power into a power network demands accurate and effective wind energy prediction models. This study proposes a multi-stage intelligent algorithm for wind electric power prediction, which combines the Beveridge–Nelson (B-N decomposition approach, the Least Square Support Vector Machine (LSSVM, and a newly proposed intelligent optimization approach called the Grasshopper Optimization Algorithm (GOA. For data preprocessing, the B-N decomposition approach was employed to disintegrate the hourly wind electric power data into a deterministic trend, a cyclic term, and a random component. Then, the LSSVM optimized by the GOA (denoted GOA-LSSVM was applied to forecast the future 168 h of the deterministic trend, the cyclic term, and the stochastic component, respectively. Finally, the future hourly wind electric power values can be obtained by multiplying the forecasted values of these three trends. Through comparing the forecasting performance of this proposed method with the LSSVM, the LSSVM optimized by the Fruit-fly Optimization Algorithm (FOA-LSSVM, and the LSSVM optimized by Particle Swarm Optimization (PSO-LSSVM, it is verified that the established multi-stage approach is superior to other models and can increase the precision of wind electric power prediction effectively.

  15. Entropy method combined with extreme learning machine method for the short-term photovoltaic power generation forecasting

    International Nuclear Information System (INIS)

    Tang, Pingzhou; Chen, Di; Hou, Yushuo

    2016-01-01

    As the world’s energy problem becomes more severe day by day, photovoltaic power generation has opened a new door for us with no doubt. It will provide an effective solution for this severe energy problem and meet human’s needs for energy if we can apply photovoltaic power generation in real life, Similar to wind power generation, photovoltaic power generation is uncertain. Therefore, the forecast of photovoltaic power generation is very crucial. In this paper, entropy method and extreme learning machine (ELM) method were combined to forecast a short-term photovoltaic power generation. First, entropy method is used to process initial data, train the network through the data after unification, and then forecast electricity generation. Finally, the data results obtained through the entropy method with ELM were compared with that generated through generalized regression neural network (GRNN) and radial basis function neural network (RBF) method. We found that entropy method combining with ELM method possesses higher accuracy and the calculation is faster.

  16. Forecasting manpower requirements for nuclear power plant construction

    International Nuclear Information System (INIS)

    Seltzer, N.; Schriver, W.R.

    1978-01-01

    This paper presents both the methodology and results of a segment of a comprehensive construction manpower demand forecasting system aimed at forecasting virtually all construction manpower requirements in the United States of America. The part of the system dealing with the demand for construction workers needed to build nuclear powered electricity generating plants is discussed here. The object of the system is to forecast manpower construction needs for each of 29 construction crafts on a monthly basis in each of 10 geographical regions of the United States. The method used is to establish profiles of the types of workers and time phasing required in the past. Profiling was done for different types of plants, different capacity classes, and different geographical locations. An appropriate worker profile matrix cannot simply be multiplied by the capacity of the proposed plant if the number of man-hours required per kilowatt of generating capacity is not constant. The value of this latter variable has changed considerably recently - presumably because of an increased awareness of environmental and safety considerations. Econometric techniques are used to forecast values for man-hours per kilowatt which are then multiplied by projected new capacity to be put in place. The resulting total man-hour requirement is then allocated over time and by craft through use of a worker profile matrix. The summary results indicate that 20 percent increases in man-hours required per kilowatt of capacity can be expected between 1977 and 1981. Total construction labour demand will rise from 65,700 work-years in 1977 to nearly 96,600 work-years in 1981. Forecasts of the actual number of different types of workers to be demanded in each month and in each region are available from the system. (author)

  17. Lifetime and economic analyses of lithium-ion batteries for balancing wind power forecast error

    DEFF Research Database (Denmark)

    Swierczynski, Maciej Jozef; Stroe, Daniel Ioan; Stroe, Ana-Irina

    2015-01-01

    is considered. In this paper, the economic feasibility of lithium-ion batteries for balancing the wind power forecast error is analysed. In order to perform a reliable assessment, an ageing model of lithium-ion battery was developed considering both cycling and calendar life. The economic analysis considers two......, it was found that for total elimination of the wind power forecast error, it is required to have a 25-MWh Li-ion battery energy storage system for the considered 2 MW WT....

  18. Trading wind generation from short-term probabilistic forecasts of wind power

    DEFF Research Database (Denmark)

    Pinson, Pierre; Chevallier, Christophe; Kariniotakis, Georges

    2007-01-01

    Due to the fluctuating nature of the wind resource, a wind power producer participating in a liberalized electricity market is subject to penalties related to regulation costs. Accurate forecasts of wind generation are therefore paramount for reducing such penalties and thus maximizing revenue......, as well as on modeling of the sensitivity a wind power producer may have to regulation costs. The benefits resulting from the application of these strategies are clearly demonstrated on the test case of the participation of a multi-MW wind farm in the Dutch electricity market over a year....... participation. Such strategies permit to further increase revenues and thus enhance competitiveness of wind generation compared to other forms of dispatchable generation. This paper formulates a general methodology for deriving optimal bidding strategies based on probabilistic forecasts of wind generation...

  19. Unit commitment with wind power generation: integrating wind forecast uncertainty and stochastic programming.

    Energy Technology Data Exchange (ETDEWEB)

    Constantinescu, E. M.; Zavala, V. M.; Rocklin, M.; Lee, S.; Anitescu, M. (Mathematics and Computer Science); (Univ. of Chicago); (New York Univ.)

    2009-10-09

    We present a computational framework for integrating the state-of-the-art Weather Research and Forecasting (WRF) model in stochastic unit commitment/energy dispatch formulations that account for wind power uncertainty. We first enhance the WRF model with adjoint sensitivity analysis capabilities and a sampling technique implemented in a distributed-memory parallel computing architecture. We use these capabilities through an ensemble approach to model the uncertainty of the forecast errors. The wind power realizations are exploited through a closed-loop stochastic unit commitment/energy dispatch formulation. We discuss computational issues arising in the implementation of the framework. In addition, we validate the framework using real wind speed data obtained from a set of meteorological stations. We also build a simulated power system to demonstrate the developments.

  20. The accuracy comparison between ARFIMA and singular spectrum analysis for forecasting the sales volume of motorcycle in Indonesia

    Science.gov (United States)

    Sitohang, Yosep Oktavianus; Darmawan, Gumgum

    2017-08-01

    This research attempts to compare between two forecasting models in time series analysis for predicting the sales volume of motorcycle in Indonesia. The first forecasting model used in this paper is Autoregressive Fractionally Integrated Moving Average (ARFIMA). ARFIMA can handle non-stationary data and has a better performance than ARIMA in forecasting accuracy on long memory data. This is because the fractional difference parameter can explain correlation structure in data that has short memory, long memory, and even both structures simultaneously. The second forecasting model is Singular spectrum analysis (SSA). The advantage of the technique is that it is able to decompose time series data into the classic components i.e. trend, cyclical, seasonal and noise components. This makes the forecasting accuracy of this technique significantly better. Furthermore, SSA is a model-free technique, so it is likely to have a very wide range in its application. Selection of the best model is based on the value of the lowest MAPE. Based on the calculation, it is obtained the best model for ARFIMA is ARFIMA (3, d = 0, 63, 0) with MAPE value of 22.95 percent. For SSA with a window length of 53 and 4 group of reconstructed data, resulting MAPE value of 13.57 percent. Based on these results it is concluded that SSA produces better forecasting accuracy.

  1. Price Forecasting of Electricity Markets in the Presence of a High Penetration of Wind Power Generators

    OpenAIRE

    Saber Talari; Miadreza Shafie-khah; Gerardo J. Osório; Fei Wang; Alireza Heidari; João P. S. Catalão

    2017-01-01

    Price forecasting plays a vital role in the day-ahead markets. Once sellers and buyers access an accurate price forecasting, managing the economic risk can be conducted appropriately through offering or bidding suitable prices. In networks with high wind power penetration, the electricity price is influenced by wind energy; therefore, price forecasting can be more complicated. This paper proposes a novel hybrid approach for price forecasting of day-ahead markets, with high penetration of wind...

  2. Power spectrum of dark matter substructure in strong gravitational lenses

    Science.gov (United States)

    Diaz Rivero, Ana; Cyr-Racine, Francis-Yan; Dvorkin, Cora

    2018-01-01

    Studying the smallest self-bound dark matter structure in our Universe can yield important clues about the fundamental particle nature of dark matter. Galaxy-scale strong gravitational lensing provides a unique way to detect and characterize dark matter substructures at cosmological distances from the Milky Way. Within the cold dark matter (CDM) paradigm, the number of low-mass subhalos within lens galaxies is expected to be large, implying that their contribution to the lensing convergence field is approximately Gaussian and could thus be described by their power spectrum. We develop here a general formalism to compute from first principles the substructure convergence power spectrum for different populations of dark matter subhalos. As an example, we apply our framework to two distinct subhalo populations: a truncated Navarro-Frenk-White subhalo population motivated by standard CDM, and a truncated cored subhalo population motivated by self-interacting dark matter (SIDM). We study in detail how the subhalo abundance, mass function, internal density profile, and concentration affect the amplitude and shape of the substructure power spectrum. We determine that the power spectrum is mostly sensitive to a specific combination of the subhalo abundance and moments of the mass function, as well as to the average tidal truncation scale of the largest subhalos included in the analysis. Interestingly, we show that the asymptotic slope of the substructure power spectrum at large wave number reflects the internal density profile of the subhalos. In particular, the SIDM power spectrum exhibits a characteristic steepening at large wave number absent in the CDM power spectrum, opening the possibility of using this observable, if at all measurable, to discern between these two scenarios.

  3. THE MURCHISON WIDEFIELD ARRAY 21 cm POWER SPECTRUM ANALYSIS METHODOLOGY

    Energy Technology Data Exchange (ETDEWEB)

    Jacobs, Daniel C.; Beardsley, A. P.; Bowman, Judd D. [Arizona State University, School of Earth and Space Exploration, Tempe, AZ 85287 (United States); Hazelton, B. J.; Sullivan, I. S.; Barry, N.; Carroll, P. [University of Washington, Department of Physics, Seattle, WA 98195 (United States); Trott, C. M.; Pindor, B.; Briggs, F.; Gaensler, B. M. [ARC Centre of Excellence for All-sky Astrophysics (CAASTRO) (Australia); Dillon, Joshua S.; Oliveira-Costa, A. de; Ewall-Wice, A.; Feng, L. [MIT Kavli Institute for Astrophysics and Space Research, Cambridge, MA 02139 (United States); Pober, J. C. [Brown University, Department of Physics, Providence, RI 02912 (United States); Bernardi, G. [Department of Physics and Electronics, Rhodes University, Grahamstown 6140 (South Africa); Cappallo, R. J.; Corey, B. E. [MIT Haystack Observatory, Westford, MA 01886 (United States); Emrich, D., E-mail: daniel.c.jacobs@asu.edu [International Centre for Radio Astronomy Research, Curtin University, Perth, WA 6845 (Australia); and others

    2016-07-10

    We present the 21 cm power spectrum analysis approach of the Murchison Widefield Array Epoch of Reionization project. In this paper, we compare the outputs of multiple pipelines for the purpose of validating statistical limits cosmological hydrogen at redshifts between 6 and 12. Multiple independent data calibration and reduction pipelines are used to make power spectrum limits on a fiducial night of data. Comparing the outputs of imaging and power spectrum stages highlights differences in calibration, foreground subtraction, and power spectrum calculation. The power spectra found using these different methods span a space defined by the various tradeoffs between speed, accuracy, and systematic control. Lessons learned from comparing the pipelines range from the algorithmic to the prosaically mundane; all demonstrate the many pitfalls of neglecting reproducibility. We briefly discuss the way these different methods attempt to handle the question of evaluating a significant detection in the presence of foregrounds.

  4. Short-term Forecast of Automatic Frequency Restoration Reserve from a Renewable Energy Based Virtual Power Plant

    OpenAIRE

    Camal , Simon; Michiorri , Andrea; Kariniotakis , Georges; Liebelt , Andreas

    2017-01-01

    International audience; This paper presents the initial findings on a new forecast approach for ancillary services delivered by aggregated renewable power plants. The increasing penetration of distributed variable generators challenges grid reliability. Wind and photovoltaic power plants are technically able to provide ancillary services, but their stochastic behavior currently impedes their integration into reserve mechanisms. A methodology is developed to forecast the flexibility that a win...

  5. A robust power spectrum split cancellation-based spectrum sensing method for cognitive radio systems

    International Nuclear Information System (INIS)

    Qi Pei-Han; Li Zan; Si Jiang-Bo; Gao Rui

    2014-01-01

    Spectrum sensing is an essential component to realize the cognitive radio, and the requirement for real-time spectrum sensing in the case of lacking prior information, fading channel, and noise uncertainty, indeed poses a major challenge to the classical spectrum sensing algorithms. Based on the stochastic properties of scalar transformation of power spectral density (PSD), a novel spectrum sensing algorithm, referred to as the power spectral density split cancellation method (PSC), is proposed in this paper. The PSC makes use of a scalar value as a test statistic, which is the ratio of each subband power to the full band power. Besides, by exploiting the asymptotic normality and independence of Fourier transform, the distribution of the ratio and the mathematical expressions for the probabilities of false alarm and detection in different channel models are derived. Further, the exact closed-form expression of decision threshold is calculated in accordance with Neyman—Pearson criterion. Analytical and simulation results show that the PSC is invulnerable to noise uncertainty, and can achive excellent detection performance without prior knowledge in additive white Gaussian noise and flat slow fading channels. In addition, the PSC benefits from a low computational cost, which can be completed in microseconds. (interdisciplinary physics and related areas of science and technology)

  6. A robust power spectrum split cancellation-based spectrum sensing method for cognitive radio systems

    Science.gov (United States)

    Qi, Pei-Han; Li, Zan; Si, Jiang-Bo; Gao, Rui

    2014-12-01

    Spectrum sensing is an essential component to realize the cognitive radio, and the requirement for real-time spectrum sensing in the case of lacking prior information, fading channel, and noise uncertainty, indeed poses a major challenge to the classical spectrum sensing algorithms. Based on the stochastic properties of scalar transformation of power spectral density (PSD), a novel spectrum sensing algorithm, referred to as the power spectral density split cancellation method (PSC), is proposed in this paper. The PSC makes use of a scalar value as a test statistic, which is the ratio of each subband power to the full band power. Besides, by exploiting the asymptotic normality and independence of Fourier transform, the distribution of the ratio and the mathematical expressions for the probabilities of false alarm and detection in different channel models are derived. Further, the exact closed-form expression of decision threshold is calculated in accordance with Neyman—Pearson criterion. Analytical and simulation results show that the PSC is invulnerable to noise uncertainty, and can achive excellent detection performance without prior knowledge in additive white Gaussian noise and flat slow fading channels. In addition, the PSC benefits from a low computational cost, which can be completed in microseconds.

  7. Multifractal signal reconstruction based on singularity power spectrum

    International Nuclear Information System (INIS)

    Xiong, Gang; Yu, Wenxian; Xia, Wenxiang; Zhang, Shuning

    2016-01-01

    Highlights: • We propose a novel multifractal reconstruction method based on singularity power spectrum analysis (MFR-SPS). • The proposed MFR-SPS method has better power characteristic than the algorithm in Fraclab. • Further, the SPS-ISE algorithm performs better than the SPS-MFS algorithm. • Based on the proposed MFR-SPS method, we can restructure singularity white fractal noise (SWFN) and linear singularity modulation (LSM) multifractal signal, in equivalent sense, similar with the linear frequency modulation(LFM) signal and WGN in the Fourier domain. - Abstract: Fractal reconstruction (FR) and multifractal reconstruction (MFR) can be considered as the inverse problem of singularity spectrum analysis, and it is challenging to reconstruct fractal signal in accord with multifractal spectrum (MFS). Due to the multiple solutions of fractal reconstruction, the traditional methods of FR/MFR, such as FBM based method, wavelet based method, random wavelet series, fail to reconstruct fractal signal deterministically, and besides, those methods neglect the power spectral distribution in the singular domain. In this paper, we propose a novel MFR method based singularity power spectrum (SPS). Supposing the consistent uniform covering of multifractal measurement, we control the traditional power law of each scale of wavelet coefficients based on the instantaneous singularity exponents (ISE) or MFS, simultaneously control the singularity power law based on the SPS, and deduce the principle and algorithm of MFR based on SPS. Reconstruction simulation and error analysis of estimated ISE, MFS and SPS show the effectiveness and the improvement of the proposed methods compared to those obtained by the Fraclab package.

  8. Implementation of 252Cf-source-driven power spectrum density measurement system

    International Nuclear Information System (INIS)

    Ren Yong; Wei Biao; Feng Peng; Li Jiansheng; Ye Cenming

    2012-01-01

    The principle of 252 Cf-source-driven power spectrum density measurement method is introduced. A measurement system and platform is realized accordingly, which is a combination of hardware and software, for measuring nuclear parameters. The detection method of neutron pulses based on an ultra-high-speed data acquisition card (three channels, 1 GHz sampling rate, 1 ns synchronization) is described, and the data processing process and the power spectrum density algorithm on PC are designed. This 252 Cf-source-driven power spectrum density measurement system can effectively obtain the nuclear tag parameters of nuclear random processes, such as correlation function and power spectrum density. (authors)

  9. Angular power spectrum of galaxies in the 2MASS Redshift Survey

    Science.gov (United States)

    Ando, Shin'ichiro; Benoit-Lévy, Aurélien; Komatsu, Eiichiro

    2018-02-01

    We present the measurement and interpretation of the angular power spectrum of nearby galaxies in the 2MASS Redshift Survey catalogue with spectroscopic redshifts up to z ≈ 0.1. We detect the angular power spectrum up to a multipole of ℓ ≈ 1000. We find that the measured power spectrum is dominated by galaxies living inside nearby galaxy clusters and groups. We use the halo occupation distribution (HOD) formalism to model the power spectrum, obtaining a fit with reasonable parameters. These HOD parameters are in agreement with the 2MASS galaxy distribution we measure towards the known nearby galaxy clusters, confirming validity of our analysis.

  10. Predictor-weighting strategies for probabilistic wind power forecasting with an analog ensemble

    Directory of Open Access Journals (Sweden)

    Constantin Junk

    2015-04-01

    Full Text Available Unlike deterministic forecasts, probabilistic predictions provide estimates of uncertainty, which is an additional value for decision-making. Previous studies have proposed the analog ensemble (AnEn, which is a technique to generate uncertainty information from a purely deterministic forecast. The objective of this study is to improve the AnEn performance for wind power forecasts by developing static and dynamic weighting strategies, which optimize the predictor combination with a brute-force continuous ranked probability score (CRPS minimization and a principal component analysis (PCA of the predictors. Predictors are taken from the high-resolution deterministic forecasts of the European Centre for Medium-Range Weather Forecasts (ECMWF, including forecasts of wind at several heights, geopotential height, pressure, and temperature, among others. The weighting strategies are compared at five wind farms in Europe and the U.S. situated in regions with different terrain complexity, both on and offshore, and significantly improve the deterministic and probabilistic AnEn forecast performance compared to the AnEn with 10‑m wind speed and direction as predictors and compared to PCA-based approaches. The AnEn methodology also provides reliable estimation of the forecast uncertainty. The optimized predictor combinations are strongly dependent on terrain complexity, local wind regimes, and atmospheric stratification. Since the proposed predictor-weighting strategies can accomplish both the selection of relevant predictors as well as finding their optimal weights, the AnEn performance is improved by up to 20 % at on and offshore sites.

  11. Implementation of a Model Output Statistics based on meteorological variable screening for short‐term wind power forecast

    DEFF Research Database (Denmark)

    Ranaboldo, Matteo; Giebel, Gregor; Codina, Bernat

    2013-01-01

    A combination of physical and statistical treatments to post‐process numerical weather predictions (NWP) outputs is needed for successful short‐term wind power forecasts. One of the most promising and effective approaches for statistical treatment is the Model Output Statistics (MOS) technique....... The proposed MOS performed well in both wind farms, and its forecasts compare positively with an actual operative model in use at Risø DTU and other MOS types, showing minimum BIAS and improving NWP power forecast of around 15% in terms of root mean square error. Further improvements could be obtained...

  12. The “Weather Intelligence for Renewable Energies” Benchmarking Exercise on Short-Term Forecasting of Wind and Solar Power Generation

    DEFF Research Database (Denmark)

    Sperati, Simone; Alessandrini, Stefano; Pinson, Pierre

    2015-01-01

    the power output of two wind farms and two photovoltaic power plants, in order to compare the merits of forecasts based on different modeling approaches and input data. It was thus possible to obtain a better knowledge of the state of the art in both wind and solar power forecasting, with an overview...

  13. Advancing satellite-based solar power forecasting through integration of infrared channels for automatic detection of coastal marine inversion layer

    Energy Technology Data Exchange (ETDEWEB)

    Kostylev, Vladimir; Kostylev, Andrey; Carter, Chris; Mahoney, Chad; Pavlovski, Alexandre; Daye, Tony [Green Power Labs Inc., Dartmouth, NS (Canada); Cormier, Dallas Eugene; Fotland, Lena [San Diego Gas and Electric Co., San Diego, CA (United States)

    2012-07-01

    The marine atmospheric boundary layer is a layer or cool, moist maritime air with the thickness of a few thousand feet immediately below a temperature inversion. In coastal areas as moist air rises from the ocean surface, it becomes trapped and is often compressed into fog above which a layer of stratus clouds often forms. This phenomenon is common for satellite-based solar radiation monitoring and forecasting. Hour ahead satellite-based solar radiation forecasts are commonly using visible spectrum satellite images, from which it is difficult to automatically differentiate low stratus clouds and fog from high altitude clouds. This provides a challenge for cloud motion tyracking and cloud cover forecasting. San Diego Gas and Electric {sup registered} (SDG and E {sup registered}) Marine Layer Project was undertaken to obtain information for integration with PV forecasts, and to develop a detailed understanding of long-term benefits from forecasting Marine Layer (ML) events and their effects on PV production. In order to establish climatological ML patterns, spatial extent and distribution of marine layer, we analyzed visible and IR spectrum satellite images (GOES WEST) archive for the period of eleven years (2000 - 2010). Historical boundaries of marine layers impact were established based on the cross-classification of visible spectrum (VIS) and infrared (IR) images. This approach is successfully used by us and elsewhere for evaluating cloud albedo in common satellite-based techniques for solar radiation monitoring and forecasting. The approach allows differentiation of cloud cover and helps distinguish low laying fog which is the main consequence of marine layer formation. ML occurrence probability and maximum extent inland was established for each hour and day of the analyzed period and seasonal/patterns were described. SDG and E service area is the most affected region by ML events with highest extent and probability of ML occurrence. Influence of ML was the

  14. REJUVENATING THE MATTER POWER SPECTRUM: RESTORING INFORMATION WITH A LOGARITHMIC DENSITY MAPPING

    International Nuclear Information System (INIS)

    Neyrinck, Mark C.; Szalay, Alexander S.; Szapudi, Istvan

    2009-01-01

    We find that nonlinearities in the dark matter power spectrum are dramatically smaller if the density field first undergoes a logarithmic mapping. In the Millennium simulation, this procedure gives a power spectrum with a shape hardly departing from the linear power spectrum for k ∼ -1 at all redshifts. Also, this procedure unveils pristine Fisher information on a range of scales reaching a factor of 2-3 smaller than in the standard power spectrum, yielding 10 times more cumulative signal to noise at z = 0.

  15. [Restoration filtering based on projection power spectrum for single-photon emission computed tomography].

    Science.gov (United States)

    Kubo, N

    1995-04-01

    To improve the quality of single-photon emission computed tomographic (SPECT) images, a restoration filter has been developed. This filter was designed according to practical "least squares filter" theory. It is necessary to know the object power spectrum and the noise power spectrum. The power spectrum is estimated from the power spectrum of a projection, when the high-frequency power spectrum of a projection is adequately approximated as a polynomial exponential expression. A study of the restoration with the filter based on a projection power spectrum was conducted, and compared with that of the "Butterworth" filtering method (cut-off frequency of 0.15 cycles/pixel), and "Wiener" filtering (signal-to-noise power spectrum ratio was a constant). Normalized mean-squared errors (NMSE) of the phantom, two line sources located in a 99mTc filled cylinder, were used. NMSE of the "Butterworth" filter, "Wiener" filter, and filtering based on a power spectrum were 0.77, 0.83, and 0.76 respectively. Clinically, brain SPECT images utilizing this new restoration filter improved the contrast. Thus, this filter may be useful in diagnosis of SPECT images.

  16. Restoration filtering based on projection power spectrum for single-photon emission computed tomography

    International Nuclear Information System (INIS)

    Kubo, Naoki

    1995-01-01

    To improve the quality of single-photon emission computed tomographic (SPECT) images, a restoration filter has been developed. This filter was designed according to practical 'least squares filter' theory. It is necessary to know the object power spectrum and the noise power spectrum. The power spectrum is estimated from the power spectrum of a projection, when the high-frequency power spectrum of a projection is adequately approximated as a polynomial exponential expression. A study of the restoration with the filter based on a projection power spectrum was conducted, and compared with that of the 'Butterworth' filtering method (cut-off frequency of 0.15 cycles/pixel), and 'Wiener' filtering (signal-to-noise power spectrum ratio was a constant). Normalized mean-squared errors (NMSE) of the phantom, two line sources located in a 99m Tc filled cylinder, were used. NMSE of the 'Butterworth' filter, 'Wiener' filter, and filtering based on a power spectrum were 0.77, 0.83, and 0.76 respectively. Clinically, brain SPECT images utilizing this new restoration filter improved the contrast. Thus, this filter may be useful in diagnosis of SPECT images. (author)

  17. Wet snow hazard for power lines: a forecast and alert system applied in Italy

    Directory of Open Access Journals (Sweden)

    P. Bonelli

    2011-09-01

    Full Text Available Wet snow icing accretion on power lines is a real problem in Italy, causing failures on high and medium voltage power supplies during the cold season. The phenomenon is a process in which many large and local scale variables contribute in a complex way and not completely understood. A numerical weather forecast can be used to select areas where wet snow accretion has an high probability of occurring, but a specific accretion model must also be used to estimate the load of an ice sleeve and its hazard. All the information must be carefully selected and shown to the electric grid operator in order to warn him promptly.

    The authors describe a prototype of forecast and alert system, WOLF (Wet snow Overload aLert and Forecast, developed and applied in Italy. The prototype elaborates the output of a numerical weather prediction model, as temperature, precipitation, wind intensity and direction, to determine the areas of potential risk for the power lines. Then an accretion model computes the ice sleeves' load for different conductor diameters. The highest values are selected and displayed on a WEB-GIS application principally devoted to the electric operator, but also to more expert users. Some experimental field campaigns have been conducted to better parameterize the accretion model. Comparisons between real accidents and forecasted icing conditions are presented and discussed.

  18. Wet snow hazard for power lines: a forecast and alert system applied in Italy

    Science.gov (United States)

    Bonelli, P.; Lacavalla, M.; Marcacci, P.; Mariani, G.; Stella, G.

    2011-09-01

    Wet snow icing accretion on power lines is a real problem in Italy, causing failures on high and medium voltage power supplies during the cold season. The phenomenon is a process in which many large and local scale variables contribute in a complex way and not completely understood. A numerical weather forecast can be used to select areas where wet snow accretion has an high probability of occurring, but a specific accretion model must also be used to estimate the load of an ice sleeve and its hazard. All the information must be carefully selected and shown to the electric grid operator in order to warn him promptly. The authors describe a prototype of forecast and alert system, WOLF (Wet snow Overload aLert and Forecast), developed and applied in Italy. The prototype elaborates the output of a numerical weather prediction model, as temperature, precipitation, wind intensity and direction, to determine the areas of potential risk for the power lines. Then an accretion model computes the ice sleeves' load for different conductor diameters. The highest values are selected and displayed on a WEB-GIS application principally devoted to the electric operator, but also to more expert users. Some experimental field campaigns have been conducted to better parameterize the accretion model. Comparisons between real accidents and forecasted icing conditions are presented and discussed.

  19. Electric peak power forecasting by year 2025

    International Nuclear Information System (INIS)

    Alsayegh, O.A.; Al-Matar, O.A.; Fairouz, F.A.; Al-Mulla Ali, A.

    2005-01-01

    Peak power demand in Kuwait up to the year 2025 was predicted using an artificial neural network (ANN) model. The aim of the study was to investigate the effect of air conditioning (A/C) units on long-term power demand. Five socio-economic factors were selected as inputs for the simulation: (1) gross national product, (2) population, (3) number of buildings, (4) imports of A/C units, and (5) index of industrial production. The study used socio-economic data from 1978 to 2000. Historical data of the first 10 years of the studied time period were used to train the ANN. The electrical network was then simulated to forecast peak power for the following 11 years. The calculated error was then used for years in which power consumption data were not available. The study demonstrated that average peak power rates increased by 4100 MW every 5 years. Various scenarios related to changes in population, the number of buildings, and the quantity of A/C units were then modelled to estimate long-term peak power demand. Results of the study demonstrated that population had the strongest impact on future power demand, while the number of buildings had the smallest impact. It was concluded that peak power growth can be controlled through the use of different immigration policies, increased A/C efficiency, and the use of vertical housing. 7 refs., 2 tabs., 6 figs

  20. Electric peak power forecasting by year 2025

    Energy Technology Data Exchange (ETDEWEB)

    Alsayegh, O.A.; Al-Matar, O.A.; Fairouz, F.A.; Al-Mulla Ali, A. [Kuwait Inst. for Scientific Research, Kuwait City (Kuwait). Div. of Environment and Urban Development

    2005-07-01

    Peak power demand in Kuwait up to the year 2025 was predicted using an artificial neural network (ANN) model. The aim of the study was to investigate the effect of air conditioning (A/C) units on long-term power demand. Five socio-economic factors were selected as inputs for the simulation: (1) gross national product, (2) population, (3) number of buildings, (4) imports of A/C units, and (5) index of industrial production. The study used socio-economic data from 1978 to 2000. Historical data of the first 10 years of the studied time period were used to train the ANN. The electrical network was then simulated to forecast peak power for the following 11 years. The calculated error was then used for years in which power consumption data were not available. The study demonstrated that average peak power rates increased by 4100 MW every 5 years. Various scenarios related to changes in population, the number of buildings, and the quantity of A/C units were then modelled to estimate long-term peak power demand. Results of the study demonstrated that population had the strongest impact on future power demand, while the number of buildings had the smallest impact. It was concluded that peak power growth can be controlled through the use of different immigration policies, increased A/C efficiency, and the use of vertical housing. 7 refs., 2 tabs., 6 figs.

  1. Do regional weather models contribute to better wind power forecasts? A few Norwegian case studies

    DEFF Research Database (Denmark)

    Bremnes, John Bjørnar; Giebel, Gregor

    2017-01-01

    resolution of this grid determines how accurate meteorological processes can be modeled and thereby also limits forecast quality. In this study, two global and four regional operational NWP models with spatial horizontal resolutions ranging from 1 to 32 km were applied to make wind power forecasts up to 66...

  2. A New Hybrid Model Based on Data Preprocessing and an Intelligent Optimization Algorithm for Electrical Power System Forecasting

    Directory of Open Access Journals (Sweden)

    Ping Jiang

    2015-01-01

    Full Text Available The establishment of electrical power system cannot only benefit the reasonable distribution and management in energy resources, but also satisfy the increasing demand for electricity. The electrical power system construction is often a pivotal part in the national and regional economic development plan. This paper constructs a hybrid model, known as the E-MFA-BP model, that can forecast indices in the electrical power system, including wind speed, electrical load, and electricity price. Firstly, the ensemble empirical mode decomposition can be applied to eliminate the noise of original time series data. After data preprocessing, the back propagation neural network model is applied to carry out the forecasting. Owing to the instability of its structure, the modified firefly algorithm is employed to optimize the weight and threshold values of back propagation to obtain a hybrid model with higher forecasting quality. Three experiments are carried out to verify the effectiveness of the model. Through comparison with other traditional well-known forecasting models, and models optimized by other optimization algorithms, the experimental results demonstrate that the hybrid model has the best forecasting performance.

  3. UD-WCMA: An Energy Estimation and Forecast Scheme for Solar Powered Wireless Sensor Networks

    KAUST Repository

    Dehwah, Ahmad H.

    2017-04-11

    Energy estimation and forecast represents an important role for energy management in solar-powered wireless sensor networks (WSNs). In general, the energy in such networks is managed over a finite time horizon in the future based on input solar power forecasts to enable continuous operation of the WSNs and achieve the sensing objectives while ensuring that no node runs out of energy. In this article, we propose a dynamic version of the weather conditioned moving average technique (UD-WCMA) to estimate and predict the variations of the solar power in a wireless sensor network. The presented approach combines the information from the real-time measurement data and a set of stored profiles representing the energy patterns in the WSNs location to update the prediction model. The UD-WCMA scheme is based on adaptive weighting parameters depending on the weather changes which makes it flexible compared to the existing estimation schemes without any precalibration. A performance analysis has been performed considering real irradiance profiles to assess the UD-WCMA prediction accuracy. Comparative numerical tests to standard forecasting schemes (EWMA, WCMA, and Pro-Energy) shows the outperformance of the new algorithm. The experimental validation has proven the interesting features of the UD-WCMA in real time low power sensor nodes.

  4. Methods of improvement of forecasting of development of mineral deposits' power supply

    Directory of Open Access Journals (Sweden)

    Alexander V. Putilov

    2015-03-01

    Full Text Available Mineral deposits (among which non-ferrous metals take a leading place are situated on the territory of our planet rather unevenly, and often in out-of-the-way places. Nuclear power (particularly, transportable nuclear power plants provides the new possibilities of power supply, which is very important for deposits' development. This article shares the economic aspects of forecasting in the field of power development (in particular, nuclear power on the basis of transportable nuclear power plants. Economic barriers of development of innovative nuclear technologies are considered on the example of transportable nuclear power plants. At the same time, there are given the ways of elimination of such barrier to development of this technology as methodical absence of investigation of a question of distribution of added cost between producers of innovative equipment and final product. Addition of new analytical tool (“business diagonal” is offered for a method of definition of economically efficient distribution of added cost (received as a result of introduction of innovative technologies between participants of production and consumption of atomic energy within the “economic cross” model. There is offered the order of use of method of cash flows discounting at calculations between nuclear market participants. Economic methods, offered in this article, may be used in forecasting of development of other energy technologies and introduction of prospective energy equipment.

  5. Electricity demand forecasting techniques

    International Nuclear Information System (INIS)

    Gnanalingam, K.

    1994-01-01

    Electricity demand forecasting plays an important role in power generation. The two areas of data that have to be forecasted in a power system are peak demand which determines the capacity (MW) of the plant required and annual energy demand (GWH). Methods used in electricity demand forecasting include time trend analysis and econometric methods. In forecasting, identification of manpower demand, identification of key planning factors, decision on planning horizon, differentiation between prediction and projection (i.e. development of different scenarios) and choosing from different forecasting techniques are important

  6. Modeling and forecasting of wind power generation - Regime-switching approaches

    DEFF Research Database (Denmark)

    Trombe, Pierre-Julien

    The present thesis addresses a number of challenges emerging from the increasing penetration of renewable energy sources into power systems. Focus is placed on wind energy and large-scale offshore wind farms. Indeed, offshore wind power variability is becoming a serious obstacle to the integration...... of more renewable energy into power systems since these systems are subjected to maintain a strict balance between electricity consumption and production, at any time. For this purpose, wind power forecasts offer an essential support to power system operators. In particular, there is a growing demand...... case study is the Horns Rev wind farm located in the North Sea. Regime-switching aspects of offshore wind power fluctuations are investigated. Several formulations of Markov-Switching models are proposed in order to better characterize the stochastic behavior of the underlying process and improve its...

  7. Hydraulic plant generation forecasting in Colombian power market using ANFIS

    Energy Technology Data Exchange (ETDEWEB)

    Moreno, Julian [Computer Science Department, Carrera 80 No. 65-223 Bloque M8A, Universidad Nacional de Colombia, Medellin (Colombia)

    2009-05-15

    In this paper an ANFIS model (adaptive neuro-fuzzy inference system) is proposed to forecast the monthly ideal generation of an agent with a hydraulic plant within the Colombian power market. The proposed model considers several factors as the plant's reservoir level, the expected hydraulic contributions of the rivers which feed it, and the expected weather conditions represented by the SST anomaly forecast in Nino 3.4 zone. The fitness of such model is measured with real data of a particular agent from period 2002-2007 and it is compared against a multiple linear regression model. The obtained results show a considerable decrease of the mean percentage error, which is an evidence of its validity and possible application to other agents. (author)

  8. Hydraulic plant generation forecasting in Colombian power market using ANFIS

    International Nuclear Information System (INIS)

    Moreno, Julian

    2009-01-01

    In this paper an ANFIS model (adaptive neuro-fuzzy inference system) is proposed to forecast the monthly ideal generation of an agent with a hydraulic plant within the Colombian power market. The proposed model considers several factors as the plant's reservoir level, the expected hydraulic contributions of the rivers which feed it, and the expected weather conditions represented by the SST anomaly forecast in Nino 3.4 zone. The fitness of such model is measured with real data of a particular agent from period 2002-2007 and it is compared against a multiple linear regression model. The obtained results show a considerable decrease of the mean percentage error, which is an evidence of its validity and possible application to other agents. (author)

  9. Medium- and long-term electric power demand forecasting based on the big data of smart city

    Science.gov (United States)

    Wei, Zhanmeng; Li, Xiyuan; Li, Xizhong; Hu, Qinghe; Zhang, Haiyang; Cui, Pengjie

    2017-08-01

    Based on the smart city, this paper proposed a new electric power demand forecasting model, which integrates external data such as meteorological information, geographic information, population information, enterprise information and economic information into the big database, and uses an improved algorithm to analyse the electric power demand and provide decision support for decision makers. The data mining technology is used to synthesize kinds of information, and the information of electric power customers is analysed optimally. The scientific forecasting is made based on the trend of electricity demand, and a smart city in north-eastern China is taken as a sample.

  10. Basic factors to forecast maintenance cost and failure processes for nuclear power plants

    International Nuclear Information System (INIS)

    Popova, Elmira; Yu, Wei; Kee, Ernie; Sun, Alice; Richards, Drew; Grantom, Rick

    2006-01-01

    Two types of maintenance interventions are usually administered at nuclear power plants: planned and corrective. The cost incurred includes the labor (manpower) cost, cost for new parts, or emergency order of expensive items. At the plant management level there is a budgeted amount of money to be spent every year for such operations. It is very important to have a good forecast for this cost since unexpected events can trigger it to a very high level. In this research we present a statistical factor model to forecast the maintenance cost for the incoming month. One of the factors is the expected number of unplanned (due to failure) maintenance interventions. We introduce a Bayesian model for the failure rate of the equipment, which is input to the cost forecasting model. The importance of equipment reliability and prediction in the commercial nuclear power plant is presented along with applicable governmental and industry organization requirements. A detailed statistical analysis is performed on a set of maintenance cost and failure data gathered at the South Texas Project Nuclear Operating Company (STPNOC) in Bay City, Texas, USA

  11. Studying The Effect of Window type On Power Spectrum Based On MATLAB

    Directory of Open Access Journals (Sweden)

    Soad T. Abed

    2012-06-01

    Full Text Available The representation that describes signal’s frequency behavior can be divided into two categories: linear representation such as the Fourier-transform and quadratic representation such as power spectrum. Power spectrum characterizes the signal’s energy distribution in the frequency domain, and can answer whether most of the power of the signal resides at low or high frequencies. By performing spectral analysis, some important features of signals can be discovered that are not obvious in the time waveform of the signal. One problem with spectrum analysis is that the duration of the signals is finite, although adjustable. Applying the FFT method to finite duration sequences can produce inadequate results because of “spectral leakage”, to reduce the spectral leakage FFT window function is applied. Power spectrum parameters are window size, window type, window over lap and number of FFT. The aim of this work is to demonstrate the effect of varying window type on the power spectrum using Mat Lab software. Five windows have been compared to study their effect on the spectrum of a typical data.

  12. Power-law modulation of the scalar power spectrum from a heavy field with a monomial potential

    Science.gov (United States)

    Huang, Qing-Guo; Pi, Shi

    2018-04-01

    The effects of heavy fields modulate the scalar power spectrum during inflation. We analytically calculate the modulations of the scalar power spectrum from a heavy field with a separable monomial potential, i.e. V(phi)~ phin. In general the modulation is characterized by a power-law oscillation which is reduced to the logarithmic oscillation in the case of n=2.

  13. Model independent foreground power spectrum estimation using WMAP 5-year data

    International Nuclear Information System (INIS)

    Ghosh, Tuhin; Souradeep, Tarun; Saha, Rajib; Jain, Pankaj

    2009-01-01

    In this paper, we propose and implement on WMAP 5 yr data a model independent approach of foreground power spectrum estimation for multifrequency observations of the CMB experiments. Recently, a model independent approach of CMB power spectrum estimation was proposed by Saha et al. 2006. This methodology demonstrates that the CMB power spectrum can be reliably estimated solely from WMAP data without assuming any template models for the foreground components. In the current paper, we extend this work to estimate the galactic foreground power spectrum using the WMAP 5 yr maps following a self-contained analysis. We apply the model independent method in harmonic basis to estimate the foreground power spectrum and frequency dependence of combined foregrounds. We also study the behavior of synchrotron spectral index variation over different regions of the sky. We use the full sky Haslam map as an external template to increase the degrees of freedom, while computing the synchrotron spectral index over the frequency range from 408 MHz to 94 GHz. We compare our results with those obtained from maximum entropy method foreground maps, which are formed in pixel space. We find that relative to our model independent estimates maximum entropy method maps overestimate the foreground power close to galactic plane and underestimates it at high latitudes.

  14. COSMIC EMULATION: FAST PREDICTIONS FOR THE GALAXY POWER SPECTRUM

    Energy Technology Data Exchange (ETDEWEB)

    Kwan, Juliana; Heitmann, Katrin; Habib, Salman; Frontiere, Nicholas; Pope, Adrian [High Energy Physics Division, Argonne National Laboratory, Lemont, IL 60439 (United States); Padmanabhan, Nikhil [Department of Physics, Yale University, 260 Whitney Ave., New Haven, CT 06520 (United States); Lawrence, Earl [Statistical Sciences, Los Alamos National Laboratory, Los Alamos, NM 87545 (United States); Finkel, Hal [Argonne Leadership Computing Facility, Argonne National Laboratory, Lemont, IL 60439 (United States)

    2015-09-01

    The halo occupation distribution (HOD) approach has proven to be an effective method for modeling galaxy clustering and bias. In this approach, galaxies of a given type are probabilistically assigned to individual halos in N-body simulations. In this paper, we present a fast emulator for predicting the fully nonlinear galaxy–galaxy auto and galaxy–dark matter cross power spectrum and correlation function over a range of freely specifiable HOD modeling parameters. The emulator is constructed using results from 100 HOD models run on a large ΛCDM N-body simulation, with Gaussian Process interpolation applied to a PCA-based representation of the galaxy power spectrum. The total error is currently ∼1% in the auto correlations and ∼2% in the cross correlations from z = 1 to z = 0, over the considered parameter range. We use the emulator to investigate the accuracy of various analytic prescriptions for the galaxy power spectrum, parametric dependencies in the HOD model, and the behavior of galaxy bias as a function of HOD parameters. Additionally, we obtain fully nonlinear predictions for tangential shear correlations induced by galaxy–galaxy lensing from our galaxy–dark matter cross power spectrum emulator. All emulation products are publicly available at http://www.hep.anl.gov/cosmology/CosmicEmu/emu.html.

  15. Wind power forecast error smoothing within a wind farm

    International Nuclear Information System (INIS)

    Saleck, Nadja; Bremen, Lueder von

    2007-01-01

    Smoothing of wind power forecast errors is well-known for large areas. Comparable effects within a wind farm are investigated in this paper. A Neural Network was taken to predict the power output of a wind farm in north-western Germany comprising 17 turbines. A comparison was done between an algorithm that fits mean wind and mean power data of the wind farm and a second algorithm that fits wind and power data individually for each turbine. The evaluation of root mean square errors (RMSE) shows that relative small smoothing effects occur. However, it can be shown for this wind farm that individual calculations have the advantage that only a few turbines are needed to give better results than the use of mean data. Furthermore different results occurred if predicted wind speeds are directly fitted to observed wind power or if predicted wind speeds are first fitted to observed wind speeds and then applied to a power curve. The first approach gives slightly better RMSE values, the bias improves considerably

  16. The non-linear power spectrum of the Lyman alpha forest

    International Nuclear Information System (INIS)

    Arinyo-i-Prats, Andreu; Miralda-Escudé, Jordi; Viel, Matteo; Cen, Renyue

    2015-01-01

    The Lyman alpha forest power spectrum has been measured on large scales by the BOSS survey in SDSS-III at z∼ 2.3, has been shown to agree well with linear theory predictions, and has provided the first measurement of Baryon Acoustic Oscillations at this redshift. However, the power at small scales, affected by non-linearities, has not been well examined so far. We present results from a variety of hydrodynamic simulations to predict the redshift space non-linear power spectrum of the Lyα transmission for several models, testing the dependence on resolution and box size. A new fitting formula is introduced to facilitate the comparison of our simulation results with observations and other simulations. The non-linear power spectrum has a generic shape determined by a transition scale from linear to non-linear anisotropy, and a Jeans scale below which the power drops rapidly. In addition, we predict the two linear bias factors of the Lyα forest and provide a better physical interpretation of their values and redshift evolution. The dependence of these bias factors and the non-linear power on the amplitude and slope of the primordial fluctuations power spectrum, the temperature-density relation of the intergalactic medium, and the mean Lyα transmission, as well as the redshift evolution, is investigated and discussed in detail. A preliminary comparison to the observations shows that the predicted redshift distortion parameter is in good agreement with the recent determination of Blomqvist et al., but the density bias factor is lower than observed. We make all our results publicly available in the form of tables of the non-linear power spectrum that is directly obtained from all our simulations, and parameters of our fitting formula

  17. Inflow forecasting at BPA

    Energy Technology Data Exchange (ETDEWEB)

    McManamon, A. [Bonneville Power Administration, Portland, OR (United States)

    2007-07-01

    The Columbia River Power System operates with consideration for flood control, endangered species, navigation, irrigation, water supply, recreation, other fish and wildlife concerns and power production. The Bonneville Power Association (BPA) located in Portland, Oregon is responsible for 35-40 per cent of the power consumed within the region. This presentation discussed inflow power concerns at BPA. The presentation illustrated elevational relief of projects; annual and daily variability; the hydrologic cycle; national river service weather forecasting service (NRSWFS); components of NRSWFS; and hydrologic forecast locations. Project operations and inventory were included along with a comparison of the 71-year average unregulated flow with regulated flow at the Dalles. Consistency between short-term and long-term forecasts and long-term streamflow forecasts were also illustrated in graphical format. The presentation also discussed the issue of reducing model and parameter uncertainty; reducing initial conditions uncertainty; snow updating; and reducing meteorological uncertainty. tabs., figs.

  18. Weak lensing of the cosmic microwave background: Power spectrum covariance

    International Nuclear Information System (INIS)

    Cooray, Asantha

    2002-01-01

    We discuss the non-Gaussian contribution to the power spectrum covariance of cosmic microwave background (CMB) anisotropies resulting through weak gravitational lensing angular deflections and the correlation of deflections with secondary sources of temperature fluctuations generated by the large scale structure, such as the integrated Sachs-Wolfe effect and the Sunyaev-Zel'dovich effect. This additional contribution to the covariance of binned angular power spectrum, beyond the well known cosmic variance and any associated instrumental noise, results from a trispectrum, or a four point correlation function, in temperature anisotropy data. With substantially wide bins in multipole space, the resulting non-Gaussian contribution from lensing to the binned power spectrum variance is insignificant out to multipoles of a few thousand and is not likely to affect the cosmological parameter estimation with acoustic peaks and the damping tail. The non-Gaussian contribution to covariance, however, should be considered when interpreting binned CMB power spectrum measurements at multipoles of a few thousand corresponding to angular scales of few arcminutes and less

  19. Anisotropic power spectrum of refractive-index fluctuation in hypersonic turbulence.

    Science.gov (United States)

    Li, Jiangting; Yang, Shaofei; Guo, Lixin; Cheng, Mingjian

    2016-11-10

    An anisotropic power spectrum of the refractive-index fluctuation in hypersonic turbulence was obtained by processing the experimental image of the hypersonic plasma sheath and transforming the generalized anisotropic von Kármán spectrum. The power spectrum suggested here can provide as good a fit to measured spectrum data for hypersonic turbulence as that recorded from the nano-planar laser scattering image. Based on the newfound anisotropic hypersonic turbulence power spectrum, Rytov approximation was employed to establish the wave structure function and the spatial coherence radius model of electromagnetic beam propagation in hypersonic turbulence. Enhancing the anisotropy characteristics of the hypersonic turbulence led to a significant improvement in the propagation performance of electromagnetic beams in hypersonic plasma sheath. The influence of hypersonic turbulence on electromagnetic beams increases with the increase of variance of the refractive-index fluctuation and the decrease of turbulence outer scale and anisotropy parameters. The spatial coherence radius was much smaller than that in atmospheric turbulence. These results are fundamental to understanding electromagnetic wave propagation in hypersonic turbulence.

  20. Medium-term electric power demand forecasting based on economic-electricity transmission model

    Science.gov (United States)

    Li, Wenfeng; Bao, Fangmin; Bai, Hongkun; Liu, Wei; Liu, Yongmin; Mao, Yubin; Wang, Jiangbo; Liu, Junhui

    2018-06-01

    Electric demand forecasting is a basic work to ensure the safe operation of power system. Based on the theories of experimental economics and econometrics, this paper introduces Prognoz Platform 7.2 intelligent adaptive modeling platform, and constructs the economic electricity transmission model that considers the economic development scenarios and the dynamic adjustment of industrial structure to predict the region's annual electricity demand, and the accurate prediction of the whole society's electricity consumption is realized. Firstly, based on the theories of experimental economics and econometrics, this dissertation attempts to find the economic indicator variables that drive the most economical growth of electricity consumption and availability, and build an annual regional macroeconomic forecast model that takes into account the dynamic adjustment of industrial structure. Secondly, it innovatively put forward the economic electricity directed conduction theory and constructed the economic power transfer function to realize the group forecast of the primary industry + rural residents living electricity consumption, urban residents living electricity, the second industry electricity consumption, the tertiary industry electricity consumption; By comparing with the actual value of economy and electricity in Henan province in 2016, the validity of EETM model is proved, and the electricity consumption of the whole province from 2017 to 2018 is predicted finally.

  1. Adaptive short-term electricity price forecasting using artificial neural networks in the restructured power markets

    International Nuclear Information System (INIS)

    Yamin, H.Y.; Shahidehpour, S.M.; Li, Z.

    2004-01-01

    This paper proposes a comprehensive model for the adaptive short-term electricity price forecasting using Artificial Neural Networks (ANN) in the restructured power markets. The model consists: price simulation, price forecasting, and performance analysis. The factors impacting the electricity price forecasting, including time factors, load factors, reserve factors, and historical price factor are discussed. We adopted ANN and proposed a new definition for the MAPE using the median to study the relationship between these factors and market price as well as the performance of the electricity price forecasting. The reserve factors are included to enhance the performance of the forecasting process. The proposed model handles the price spikes more efficiently due to considering the median instead of the average. The IEEE 118-bus system and California practical system are used to demonstrate the superiority of the proposed model. (author)

  2. Adaptive modelling and forecasting of offshore wind power fluctuations with Markov-switching autoregressive models

    DEFF Research Database (Denmark)

    Pinson, Pierre; Madsen, Henrik

    optimized is based on penalized maximum-likelihood, with exponential forgetting of past observations. MSAR models are then employed for 1-step-ahead point forecasting of 10-minute resolution time-series of wind power at two large offshore wind farms. They are favourably compared against persistence and Auto......Wind power production data at temporal resolutions of a few minutes exhibits successive periods with fluctuations of various dynamic nature and magnitude, which cannot be explained (so far) by the evolution of some explanatory variable. Our proposal is to capture this regime-switching behaviour......Regressive (AR) models. It is finally shown that the main interest of MSAR models lies in their ability to generate interval/density forecasts of significantly higher skill....

  3. Adaptive modelling and forecasting of offshore wind power fluctuations with Markov-switching autoregressive models

    DEFF Research Database (Denmark)

    Pinson, Pierre; Madsen, Henrik

    2012-01-01

    optimized is based on penalized maximum likelihood, with exponential forgetting of past observations. MSAR models are then employed for one-step-ahead point forecasting of 10 min resolution time series of wind power at two large offshore wind farms. They are favourably compared against persistence......Wind power production data at temporal resolutions of a few minutes exhibit successive periods with fluctuations of various dynamic nature and magnitude, which cannot be explained (so far) by the evolution of some explanatory variable. Our proposal is to capture this regime-switching behaviour...... and autoregressive models. It is finally shown that the main interest of MSAR models lies in their ability to generate interval/density forecasts of significantly higher skill....

  4. Orientation identification of the power spectrum

    NARCIS (Netherlands)

    Rudnaya, M.; Mattheij, R.M.M.; Maubach, J.M.L.

    2010-01-01

    The image Fourier transform is widely used for defocus and astigmatism correction in electron microscopy. The shape of a power spectrum (the square of a modulus of image Fourier transform) is directly related to the three microscope’s controls, namely defocus and two-fold (two-parameter)

  5. Nonlinear evolution of f(R) cosmologies. II. Power spectrum

    International Nuclear Information System (INIS)

    Oyaizu, Hiroaki; Hu, Wayne; Lima, Marcos

    2008-01-01

    We carry out a suite of cosmological simulations of modified action f(R) models where cosmic acceleration arises from an alteration of gravity instead of dark energy. These models introduce an extra scalar degree of freedom which enhances the force of gravity below the inverse mass or Compton scale of the scalar. The simulations exhibit the so-called chameleon mechanism, necessary for satisfying local constraints on gravity, where this scale depends on environment, in particular, the depth of the local gravitational potential. We find that the chameleon mechanism can substantially suppress the enhancement of power spectrum in the nonlinear regime if the background field value is comparable to or smaller than the depth of the gravitational potentials of typical structures. Nonetheless power spectrum enhancements at intermediate scales remain at a measurable level for models even when the expansion history is indistinguishable from a cosmological constant, cold dark matter model. Simple scaling relations that take the linear power spectrum into a nonlinear spectrum fail to capture the modifications of f(R) due to the change in collapsed structures, the chameleon mechanism, and the time evolution of the modifications.

  6. On minimally parametric primordial power spectrum reconstruction and the evidence for a red tilt

    International Nuclear Information System (INIS)

    Verde, Licia; Peiris, Hiranya

    2008-01-01

    The latest cosmological data seem to indicate a significant deviation from scale invariance of the primordial power spectrum when parameterized either by a power law or by a spectral index with non-zero 'running'. This deviation, by itself, serves as a powerful tool for discriminating among theories for the origin of cosmological structures such as inflationary models. Here, we use a minimally parametric smoothing spline technique to reconstruct the shape of the primordial power spectrum. This technique is well suited to searching for smooth features in the primordial power spectrum such as deviations from scale invariance or a running spectral index, although it would recover sharp features of high statistical significance. We use the WMAP three-year results in combination with data from a suite of higher resolution cosmic microwave background experiments (including the latest ACBAR 2008 release), as well as large-scale structure data from SDSS and 2dFGRS. We employ cross-validation to assess, using the data themselves, the optimal amount of smoothness in the primordial power spectrum consistent with the data. This minimally parametric reconstruction supports the evidence for a power law primordial power spectrum with a red tilt, but not for deviations from a power law power spectrum. Smooth variations in the primordial power spectrum are not significantly degenerate with the other cosmological parameters

  7. Ensemble-based Probabilistic Forecasting at Horns Rev

    DEFF Research Database (Denmark)

    Pinson, Pierre; Madsen, Henrik

    2009-01-01

    forecasting methodology. In a first stage, ensemble forecasts of meteorological variables are converted to power through a suitable power curve model. This modelemploys local polynomial regression, and is adoptively estimated with an orthogonal fitting method. The obtained ensemble forecasts of wind power...

  8. Wind power forecasting : state-of-the-art 2009.

    Energy Technology Data Exchange (ETDEWEB)

    Monteiro, C.; Bessa, R.; Miranda, V.; Botterud, A.; Wang, J.; Conzelmann, G.; Decision and Information Sciences; INESC Porto

    2009-11-20

    Many countries and regions are introducing policies aimed at reducing the environmental footprint from the energy sector and increasing the use of renewable energy. In the United States, a number of initiatives have been taken at the state level, from renewable portfolio standards (RPSs) and renewable energy certificates (RECs), to regional greenhouse gas emission control schemes. Within the U.S. Federal government, new energy and environmental policies and goals are also being crafted, and these are likely to increase the use of renewable energy substantially. The European Union is pursuing implementation of its ambitious 20/20/20 targets, which aim (by 2020) to reduce greenhouse gas emissions by 20% (as compared to 1990), increase the amount of renewable energy to 20% of the energy supply, and reduce the overall energy consumption by 20% through energy efficiency. With the current focus on energy and the environment, efficient integration of renewable energy into the electric power system is becoming increasingly important. In a recent report, the U.S. Department of Energy (DOE) describes a model-based scenario, in which wind energy provides 20% of the U.S. electricity demand in 2030. The report discusses a set of technical and economic challenges that have to be overcome for this scenario to unfold. In Europe, several countries already have a high penetration of wind power (i.e., in the range of 7 to 20% of electricity consumption in countries such as Germany, Spain, Portugal, and Denmark). The rapid growth in installed wind power capacity is expected to continue in the United States as well as in Europe. A large-scale introduction of wind power causes a number of challenges for electricity market and power system operators who will have to deal with the variability and uncertainty in wind power generation when making their scheduling and dispatch decisions. Wind power forecasting (WPF) is frequently identified as an important tool to address the variability and

  9. Particle swarm optimization-based support vector machine for forecasting dissolved gases content in power transformer oil

    Energy Technology Data Exchange (ETDEWEB)

    Fei, Sheng-wei; Wang, Ming-Jun; Miao, Yu-bin; Tu, Jun; Liu, Cheng-liang [School of Mechanical Engineering, Shanghai Jiaotong University, Shanghai 200240 (China)

    2009-06-15

    Forecasting of dissolved gases content in power transformer oil is a complicated problem due to its nonlinearity and the small quantity of training data. Support vector machine (SVM) has been successfully employed to solve regression problem of nonlinearity and small sample. However, the practicability of SVM is effected due to the difficulty of selecting appropriate SVM parameters. Particle swarm optimization (PSO) is a new optimization method, which is motivated by social behaviour of organisms such as bird flocking and fish schooling. The method not only has strong global search capability, but also is very easy to implement. Thus, the proposed PSO-SVM model is applied to forecast dissolved gases content in power transformer oil in this paper, among which PSO is used to determine free parameters of support vector machine. The experimental data from several electric power companies in China is used to illustrate the performance of proposed PSO-SVM model. The experimental results indicate that the PSO-SVM method can achieve greater forecasting accuracy than grey model, artificial neural network under the circumstances of small sample. (author)

  10. Particle swarm optimization-based support vector machine for forecasting dissolved gases content in power transformer oil

    Energy Technology Data Exchange (ETDEWEB)

    Fei Shengwei [School of Mechanical Engineering, Shanghai Jiaotong University, Shanghai 200240 (China)], E-mail: feishengwei@sohu.com; Wang Mingjun; Miao Yubin; Tu Jun; Liu Chengliang [School of Mechanical Engineering, Shanghai Jiaotong University, Shanghai 200240 (China)

    2009-06-15

    Forecasting of dissolved gases content in power transformer oil is a complicated problem due to its nonlinearity and the small quantity of training data. Support vector machine (SVM) has been successfully employed to solve regression problem of nonlinearity and small sample. However, the practicability of SVM is effected due to the difficulty of selecting appropriate SVM parameters. Particle swarm optimization (PSO) is a new optimization method, which is motivated by social behaviour of organisms such as bird flocking and fish schooling. The method not only has strong global search capability, but also is very easy to implement. Thus, the proposed PSO-SVM model is applied to forecast dissolved gases content in power transformer oil in this paper, among which PSO is used to determine free parameters of support vector machine. The experimental data from several electric power companies in China is used to illustrate the performance of proposed PSO-SVM model. The experimental results indicate that the PSO-SVM method can achieve greater forecasting accuracy than grey model, artificial neural network under the circumstances of small sample.

  11. Correlation-constrained and sparsity-controlled vector autoregressive model for spatio-temporal wind power forecasting

    DEFF Research Database (Denmark)

    Zhao, Yongning; Ye, Lin; Pinson, Pierre

    2018-01-01

    The ever-increasing number of wind farms has brought both challenges and opportunities in the development of wind power forecasting techniques to take advantage of interdependenciesbetweentensorhundredsofspatiallydistributedwind farms, e.g., over a region. In this paper, a Sparsity-Controlled Vec......The ever-increasing number of wind farms has brought both challenges and opportunities in the development of wind power forecasting techniques to take advantage of interdependenciesbetweentensorhundredsofspatiallydistributedwind farms, e.g., over a region. In this paper, a Sparsity...... matrices in direct manner. However this original SC-VAR is difficult to implement due to its complicated constraints and the lack of guidelines for setting its parameters. To reduce the complexity of this MINLP and to make it possible to incorporate prior expert knowledge to benefit model building...

  12. Shape of power spectrum of intermittent chaos

    International Nuclear Information System (INIS)

    So, B.C.; Mori, H.

    1984-01-01

    Power spectra of intermittent chaos are calculated analytically. It is found that the power spectrum near onset point consists of a large number of Lorentzian lines with two peaks around frequencies ω = 0 and ω = ω 0 , where ω 0 is a fundamental frequency of a periodic orbit before the onset point, and furthermore the envelope of lines around ω = 0 obeys the power law 1/ + ω +2 , whereas the envelope around ω 0 obeys 1/ + ω-ω 0 +4 . The universality of these power law dependence in a certain class of intermittent chaos are clarified from a phenomenological view point. (author)

  13. Electricity demand load forecasting of the Hellenic power system using an ARMA model

    Energy Technology Data Exchange (ETDEWEB)

    Pappas, S.Sp. [ASPETE - School of Pedagogical and Technological Education Department of Electrical Engineering Educators N. Heraklion, 141 21 Athens (Greece); Ekonomou, L.; Chatzarakis, G.E.; Skafidas, P.D. [ASPETE-School of Pedagogical and Technological Education, Department of Electrical Engineering Educators, N. Heraklion, 141 21 Athens (Greece); Karampelas, P. [Hellenic American University, IT Department, 12 Kaplanon Str., 106 80 Athens (Greece); Karamousantas, D.C. [Technological Educational Institute of Kalamata, Antikalamos, 24 100 Kalamata (Greece); Katsikas, S.K. [University of Piraeus, Department of Technology Education and Digital Systems, 150 Androutsou St., 18 532 Piraeus (Greece)

    2010-03-15

    Effective modeling and forecasting requires the efficient use of the information contained in the available data so that essential data properties can be extracted and projected into the future. As far as electricity demand load forecasting is concerned time series analysis has the advantage of being statistically adaptive to data characteristics compared to econometric methods which quite often are subject to errors and uncertainties in model specification and knowledge of causal variables. This paper presents a new method for electricity demand load forecasting using the multi-model partitioning theory and compares its performance with three other well established time series analysis techniques namely Corrected Akaike Information Criterion (AICC), Akaike's Information Criterion (AIC) and Schwarz's Bayesian Information Criterion (BIC). The suitability of the proposed method is illustrated through an application to actual electricity demand load of the Hellenic power system, proving the reliability and the effectiveness of the method and making clear its usefulness in the studies that concern electricity consumption and electricity prices forecasts. (author)

  14. On maximizing profit of wind-battery supported power station based on wind power and energy price forecasting

    DEFF Research Database (Denmark)

    Khalid, Muhammad; Aguilera, Ricardo P.; Savkin, Andrey V.

    2017-01-01

    This paper proposes a framework to develop an optimal power dispatch strategy for grid-connected wind power plants containing a Battery Energy Storage System (BESS). Considering the intermittent nature of wind power and rapidly varying electricity market price, short-term forecasting...... Dynamic Programming tool which can incorporate the predictions of both wind power and market price simultaneously as inputs in a receding horizon approach. The proposed strategy is validated using real electricity market price and wind power data in different scenarios of BESS power and capacity...... of these variables is used for efficient energy management. The predicted variability trends in market price assist in earning additional income which subsequently increase the operational profit. Then on the basis of income improvement, optimal capacity of the BESS can be determined. The proposed framework utilizes...

  15. Forecasting of Power Grid Investment in China Based on Support Vector Machine Optimized by Differential Evolution Algorithm and Grey Wolf Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Shuyu Dai

    2018-04-01

    Full Text Available In recent years, the construction of China’s power grid has experienced rapid development, and its scale has leaped into the first place in the world. Accurate and effective prediction of power grid investment can not only help pool funds and rationally arrange investment in power grid construction, but also reduce capital costs and economic risks, which plays a crucial role in promoting power grid investment planning and construction process. In order to forecast the power grid investment of China accurately, firstly on the basis of analyzing the influencing factors of power grid investment, the influencing factors system for China’s power grid investment forecasting is constructed in this article. The method of grey relational analysis is used for screening the main influencing factors as the prediction model input. Then, a novel power grid investment prediction model based on DE-GWO-SVM (support vector machine optimized by differential evolution and grey wolf optimization algorithm is proposed. Next, two cases are taken for empirical analysis to prove that the DE-GWO-SVM model has strong generalization capacity and has achieved a good prediction effect for power grid investment forecasting in China. Finally, the DE-GWO-SVM model is adopted to forecast power grid investment in China from 2018 to 2022.

  16. Regional PV power estimation and forecast to mitigate the impact of high photovoltaic penetration on electric grid.

    Science.gov (United States)

    Pierro, Marco; De Felice, Matteo; Maggioni, Enrico; Moser, David; Perotto, Alessandro; Spada, Francesco; Cornaro, Cristina

    2017-04-01

    The growing photovoltaic generation results in a stochastic variability of the electric demand that could compromise the stability of the grid and increase the amount of energy reserve and the energy imbalance cost. On regional scale, solar power estimation and forecast is becoming essential for Distribution System Operators, Transmission System Operator, energy traders, and aggregators of generation. Indeed the estimation of regional PV power can be used for PV power supervision and real time control of residual load. Mid-term PV power forecast can be employed for transmission scheduling to reduce energy imbalance and related cost of penalties, residual load tracking, trading optimization, secondary energy reserve assessment. In this context, a new upscaling method was developed and used for estimation and mid-term forecast of the photovoltaic distributed generation in a small area in the north of Italy under the control of a local DSO. The method was based on spatial clustering of the PV fleet and neural networks models that input satellite or numerical weather prediction data (centered on cluster centroids) to estimate or predict the regional solar generation. It requires a low computational effort and very few input information should be provided by users. The power estimation model achieved a RMSE of 3% of installed capacity. Intra-day forecast (from 1 to 4 hours) obtained a RMSE of 5% - 7% while the one and two days forecast achieve to a RMSE of 7% and 7.5%. A model to estimate the forecast error and the prediction intervals was also developed. The photovoltaic production in the considered region provided the 6.9% of the electric consumption in 2015. Since the PV penetration is very similar to the one observed at national level (7.9%), this is a good case study to analyse the impact of PV generation on the electric grid and the effects of PV power forecast on transmission scheduling and on secondary reserve estimation. It appears that, already with 7% of PV

  17. The power spectrum of inflationary attractors

    International Nuclear Information System (INIS)

    Broy, Benedict J.; Westphal, Alexander; Roest, Diederik

    2014-08-01

    Inflationary attractors predict the spectral index and tensor-to-scalar ratio to take specific values that are consistent with Planck. An example is the universal attractor for models with a generalised non-minimal coupling, leading to Starobinsky inflation. In this letter we demonstrate that it also predicts a specific relation between the amplitude of the power spectrum and the number of e-folds. The length and height of the inflationary plateau are related via the non-minimal coupling: in a wide variety of examples, the observed power normalisation leads to at least 55 flat e-foldings. Prior to this phase, the inflationary predictions vary and can account for the observational indications of power loss at large angular scales.

  18. Features in the primordial power spectrum of double D-term inflation

    International Nuclear Information System (INIS)

    Lesgourgues, Julien

    2000-01-01

    Recently, there has been some interest for building supersymmetric models of double inflation. These models, realistic from a particle physics point of view, predict a broken-scale-invariant power spectrum of primordial cosmological perturbations, that may explain eventual nontrivial features in the present matter power spectrum. In previous works, the primordial spectrum was calculated using analytic slow-roll approximations. However, these models involve a fast second-order phase transition during inflation, with a stage of spinodal instability, and an interruption of slow-roll. For our previous model of double D-term inflation, we simulate numerically the evolution of quantum fluctuations, taking into account the spinodal modes, and we show that the semiclassical approximation can be employed even during the transition, due to the presence of a second inflaton field. The primordial power spectrum possesses a rich structure, and possibly, a non-Gaussian spike on observable scales

  19. Spectrum and power allocation in cognitive multi-beam satellite communications with flexible satellite payloads

    Science.gov (United States)

    Liu, Zhihui; Wang, Haitao; Dong, Tao; Yin, Jie; Zhang, Tingting; Guo, Hui; Li, Dequan

    2018-02-01

    In this paper, the cognitive multi-beam satellite system, i.e., two satellite networks coexist through underlay spectrum sharing, is studied, and the power and spectrum allocation method is employed for interference control and throughput maximization. Specifically, the multi-beam satellite with flexible payload reuses the authorized spectrum of the primary satellite, adjusting its transmission band as well as power for each beam to limit its interference on the primary satellite below the prescribed threshold and maximize its own achievable rate. This power and spectrum allocation problem is formulated as a mixed nonconvex programming. For effective solving, we first introduce the concept of signal to leakage plus noise ratio (SLNR) to decouple multiple transmit power variables in the both objective and constraint, and then propose a heuristic algorithm to assign spectrum sub-bands. After that, a stepwise plus slice-wise algorithm is proposed to implement the discrete power allocation. Finally, simulation results show that adopting cognitive technology can improve spectrum efficiency of the satellite communication.

  20. Price Forecasting of Electricity Markets in the Presence of a High Penetration of Wind Power Generators

    Directory of Open Access Journals (Sweden)

    Saber Talari

    2017-11-01

    Full Text Available Price forecasting plays a vital role in the day-ahead markets. Once sellers and buyers access an accurate price forecasting, managing the economic risk can be conducted appropriately through offering or bidding suitable prices. In networks with high wind power penetration, the electricity price is influenced by wind energy; therefore, price forecasting can be more complicated. This paper proposes a novel hybrid approach for price forecasting of day-ahead markets, with high penetration of wind generators based on Wavelet transform, bivariate Auto-Regressive Integrated Moving Average (ARIMA method and Radial Basis Function Neural Network (RBFN. To this end, a weighted time series for wind dominated power systems is calculated and added to a bivariate ARIMA model along with the price time series. Moreover, RBFN is applied as a tool to correct the estimation error, and particle swarm optimization (PSO is used to optimize the structure and adapt the RBFN to the particular training set. This method is evaluated on the Spanish electricity market, which shows the efficiency of this approach. This method has less error compared with other methods especially when it considers the effects of large-scale wind generators.

  1. Short-term electric power demand forecasting based on economic-electricity transmission model

    Science.gov (United States)

    Li, Wenfeng; Bai, Hongkun; Liu, Wei; Liu, Yongmin; Wang, Yubin Mao; Wang, Jiangbo; He, Dandan

    2018-04-01

    Short-term electricity demand forecasting is the basic work to ensure safe operation of the power system. In this paper, a practical economic electricity transmission model (EETM) is built. With the intelligent adaptive modeling capabilities of Prognoz Platform 7.2, the econometric model consists of three industrial added value and income levels is firstly built, the electricity demand transmission model is also built. By multiple regression, moving averages and seasonal decomposition, the problem of multiple correlations between variables is effectively overcome in EETM. The validity of EETM is proved by comparison with the actual value of Henan Province. Finally, EETM model is used to forecast the electricity consumption of the 1-4 quarter of 2018.

  2. Error Assessment of Solar Irradiance Forecasts and AC Power from Energy Conversion Model in Grid-Connected Photovoltaic Systems

    Directory of Open Access Journals (Sweden)

    Gianfranco Chicco

    2015-12-01

    Full Text Available Availability of effective estimation of the power profiles of photovoltaic systems is essential for studying how to increase the share of intermittent renewable sources in the electricity mix of many countries. For this purpose, weather forecasts, together with historical data of the meteorological quantities, provide fundamental information. The weak point of the forecasts depends on variable sky conditions, when the clouds successively cover and uncover the solar disc. This causes remarkable positive and negative variations in the irradiance pattern measured at the photovoltaic (PV site location. This paper starts from 1 to 3 days-ahead solar irradiance forecasts available during one year, with a few points for each day. These forecasts are interpolated to obtain more irradiance estimations per day. The estimated irradiance data are used to classify the sky conditions into clear, variable or cloudy. The results are compared with the outcomes of the same classification carried out with the irradiance measured in meteorological stations at two real PV sites. The occurrence of irradiance spikes in “broken cloud” conditions is identified and discussed. From the measured irradiance, the Alternating Current (AC power injected into the grid at two PV sites is estimated by using a PV energy conversion model. The AC power errors resulting from the PV model with respect to on-site AC power measurements are shown and discussed.

  3. Computational Intelligence Techniques Applied to the Day Ahead PV Output Power Forecast: PHANN, SNO and Mixed

    Directory of Open Access Journals (Sweden)

    Emanuele Ogliari

    2018-06-01

    Full Text Available An accurate forecast of the exploitable energy from Renewable Energy Sources is extremely important for the stability issues of the electric grid and the reliability of the bidding markets. This paper presents a comparison among different forecasting methods of the photovoltaic output power introducing a new method that mixes some peculiarities of the others: the Physical Hybrid Artificial Neural Network and the five parameters model estimated by the Social Network Optimization. In particular, the day-ahead forecasts evaluated against real data measured for two years in an existing photovoltaic plant located in Milan, Italy, are compared by means both new and the most common error indicators. Results reported in this work show the best forecasting capability of the new “mixed method” which scored the best forecast skill and Enveloped Mean Absolute Error on a yearly basis (47% and 24.67%, respectively.

  4. Wind power forecasting-a review of the state of the art

    DEFF Research Database (Denmark)

    Giebel, Gregor; Kariniotakis, George

    2017-01-01

    This chapter gives an overview over past and present attempts to predict wind power for single turbines, wind, farms or for whole regions, for a few minutes up to a few days ahead. It is based on a survey and report (Giebel et al., 2011) initiated in the frame of the European project ANEMOS, whic...... integration of the forecasts in the work flow of end users....

  5. Application of power spectrum, cepstrum, higher order spectrum and neural network analyses for induction motor fault diagnosis

    Science.gov (United States)

    Liang, B.; Iwnicki, S. D.; Zhao, Y.

    2013-08-01

    The power spectrum is defined as the square of the magnitude of the Fourier transform (FT) of a signal. The advantage of FT analysis is that it allows the decomposition of a signal into individual periodic frequency components and establishes the relative intensity of each component. It is the most commonly used signal processing technique today. If the same principle is applied for the detection of periodicity components in a Fourier spectrum, the process is called the cepstrum analysis. Cepstrum analysis is a very useful tool for detection families of harmonics with uniform spacing or the families of sidebands commonly found in gearbox, bearing and engine vibration fault spectra. Higher order spectra (HOS) (also known as polyspectra) consist of higher order moment of spectra which are able to detect non-linear interactions between frequency components. For HOS, the most commonly used is the bispectrum. The bispectrum is the third-order frequency domain measure, which contains information that standard power spectral analysis techniques cannot provide. It is well known that neural networks can represent complex non-linear relationships, and therefore they are extremely useful for fault identification and classification. This paper presents an application of power spectrum, cepstrum, bispectrum and neural network for fault pattern extraction of induction motors. The potential for using the power spectrum, cepstrum, bispectrum and neural network as a means for differentiating between healthy and faulty induction motor operation is examined. A series of experiments is done and the advantages and disadvantages between them are discussed. It has been found that a combination of power spectrum, cepstrum and bispectrum plus neural network analyses could be a very useful tool for condition monitoring and fault diagnosis of induction motors.

  6. Economic impact analysis of load forecasting

    International Nuclear Information System (INIS)

    Ranaweera, D.K.; Karady, G.G.; Farmer, R.G.

    1997-01-01

    Short term load forecasting is an essential function in electric power system operations and planning. Forecasts are needed for a variety of utility activities such as generation scheduling, scheduling of fuel purchases, maintenance scheduling and security analysis. Depending on power system characteristics, significant forecasting errors can lead to either excessively conservative scheduling or very marginal scheduling. Either can induce heavy economic penalties. This paper examines the economic impact of inaccurate load forecasts. Monte Carlo simulations were used to study the effect of different load forecasting accuracy. Investigations into the effect of improving the daily peak load forecasts, effect of different seasons of the year and effect of utilization factors are presented

  7. Development of a forecasting method of a region`s electric power demand. 1. Forecasting economic and social indexes; Chiikibetsu denryoku juyo yosoku shuhono kaihatsu ni tsuite. 1. Keizai shakai shihyo no yosoku

    Energy Technology Data Exchange (ETDEWEB)

    Minato, Y. [Shikoku Research Institute Inc., Kagawa (Japan); Yokoi, Y. [The University of Tokushima, Tokushima (Japan)

    1996-01-20

    This paper relates to the forecasting method of the electric power demands (kWh and kW) of a region, approached by not only time series analysis but economic and social indexes. Those indexes, based on historical statistics such as census and establishment statistics, are rearranged from an administrative division to a managerial division of the electric power company, and applied as fundamental information for forecasting the area`s kWh and also sales promotion. This method of forecasting the area`s kWh is based on the concept that area`s kWh is strongly connected with the population their lifestyle and their activity within the region. In the paper, the framework of the computational model system and forecast result are discussed. The population, number of households and their members, and number of employed persons, are all evaluated. The forecasting method of the area`s population proposed here is based on the concept that the transition of population consists of both natural growth and immigration. By estimating both factors, the future area`s population can be easily forecasted. The information of whether the population is increasing or decreasing is useful for forecasting the region`s kWh and required sales promotion. 8 refs., 8 figs., 3 tabs.

  8. Hybrid Forecasting Approach Based on GRNN Neural Network and SVR Machine for Electricity Demand Forecasting

    Directory of Open Access Journals (Sweden)

    Weide Li

    2017-01-01

    Full Text Available Accurate electric power demand forecasting plays a key role in electricity markets and power systems. The electric power demand is usually a non-linear problem due to various unknown reasons, which make it difficult to get accurate prediction by traditional methods. The purpose of this paper is to propose a novel hybrid forecasting method for managing and scheduling the electricity power. EEMD-SCGRNN-PSVR, the proposed new method, combines ensemble empirical mode decomposition (EEMD, seasonal adjustment (S, cross validation (C, general regression neural network (GRNN and support vector regression machine optimized by the particle swarm optimization algorithm (PSVR. The main idea of EEMD-SCGRNN-PSVR is respectively to forecast waveform and trend component that hidden in demand series to substitute directly forecasting original electric demand. EEMD-SCGRNN-PSVR is used to predict the one week ahead half-hour’s electricity demand in two data sets (New South Wales (NSW and Victorian State (VIC in Australia. Experimental results show that the new hybrid model outperforms the other three models in terms of forecasting accuracy and model robustness.

  9. Load forecasting

    International Nuclear Information System (INIS)

    Mak, H.

    1995-01-01

    Slides used in a presentation at The Power of Change Conference in Vancouver, BC in April 1995 about the changing needs for load forecasting were presented. Technological innovations and population increase were said to be the prime driving forces behind the changing needs in load forecasting. Structural changes, market place changes, electricity supply planning changes, and changes in planning objectives were other factors discussed. It was concluded that load forecasting was a form of information gathering, that provided important market intelligence

  10. Adiabatic regularization of the power spectrum in nonminimally coupled general single-field inflation

    Science.gov (United States)

    Alinea, Allan L.; Kubota, Takahiro

    2018-03-01

    We perform adiabatic regularization of power spectrum in nonminimally coupled general single-field inflation with varying speed of sound. The subtraction is performed within the framework of earlier study by Urakawa and Starobinsky dealing with the canonical inflation. Inspired by Fakir and Unruh's model on nonminimally coupled chaotic inflation, we find upon imposing near scale-invariant condition, that the subtraction term exponentially decays with the number of e -folds. As in the result for the canonical inflation, the regularized power spectrum tends to the "bare" power spectrum as the Universe expands during (and even after) inflation. This work justifies the use of the "bare" power spectrum in standard calculation in the most general context of slow-roll single-field inflation involving nonminimal coupling and varying speed of sound.

  11. Power and energy balances. Forecast 2008

    International Nuclear Information System (INIS)

    2005-01-01

    Both the energy and power balance in 2008 is slightly better than the former Nordel estimate for 2007. This is due to additional investments in new generation capacity, new interconnections of total 1 000 MW to outside Nordel and reduced demand forecast in Sweden. The Nordic electricity system is able to meet the estimated consumption and the corresponding typical power demand pattern in average conditions. In long term the market is expected to maintain a reasonable balance between supply, imports and demand. Lower precipitation or colder temperature result in higher market prices that give incentives for increased imports, demand response and investments. This is expected to maintain the balance between supply and demand in the short and long term even in extreme situations. Allocation between imports and demand response in reality depends on the prevailing market prices and available generation resources outside Nordel. The interconnection capacities are expected to enable import volumes that can meet the increased peak demand. Some Nordic areas can be exposed to a risk for rationing or other measures because of extremely low precipitation. Nordic transmission capacities may prevent full utilization of Nordic thermal power in certain areas. The planned reinforcements in the 'five prioritised cross-sections' will improve the situation. The power balance and the internal bottlenecks in the continental Europe can have an effect on the import possibilities to the Nordic countries. The annual energy consumption in the Nordic market is estimated to grow by 20 TWh by year 2008 (1.2%la) from 395 TWh in 2004 (temperature corrected). In the three year period investments in power generation is expected to increase the available generation capacity and capability by 1500 MW and 10 TWhla in average conditions. Iceland is not included in the figures. The annual energy consumption in Iceland is estimated to grow by about 6.8 TWh by year 2008 (15 %la) due to two new aluminium

  12. A Beacon Transmission Power Control Algorithm Based on Wireless Channel Load Forecasting in VANETs.

    Science.gov (United States)

    Mo, Yuanfu; Yu, Dexin; Song, Jun; Zheng, Kun; Guo, Yajuan

    2015-01-01

    In a vehicular ad hoc network (VANET), the periodic exchange of single-hop status information broadcasts (beacon frames) produces channel loading, which causes channel congestion and induces information conflict problems. To guarantee fairness in beacon transmissions from each node and maximum network connectivity, adjustment of the beacon transmission power is an effective method for reducing and preventing channel congestion. In this study, the primary factors that influence wireless channel loading are selected to construct the KF-BCLF, which is a channel load forecasting algorithm based on a recursive Kalman filter and employs multiple regression equation. By pre-adjusting the transmission power based on the forecasted channel load, the channel load was kept within a predefined range; therefore, channel congestion was prevented. Based on this method, the CLF-BTPC, which is a transmission power control algorithm, is proposed. To verify KF-BCLF algorithm, a traffic survey method that involved the collection of floating car data along a major traffic road in Changchun City is employed. By comparing this forecast with the measured channel loads, the proposed KF-BCLF algorithm was proven to be effective. In addition, the CLF-BTPC algorithm is verified by simulating a section of eight-lane highway and a signal-controlled urban intersection. The results of the two verification process indicate that this distributed CLF-BTPC algorithm can effectively control channel load, prevent channel congestion, and enhance the stability and robustness of wireless beacon transmission in a vehicular network.

  13. A Beacon Transmission Power Control Algorithm Based on Wireless Channel Load Forecasting in VANETs.

    Directory of Open Access Journals (Sweden)

    Yuanfu Mo

    Full Text Available In a vehicular ad hoc network (VANET, the periodic exchange of single-hop status information broadcasts (beacon frames produces channel loading, which causes channel congestion and induces information conflict problems. To guarantee fairness in beacon transmissions from each node and maximum network connectivity, adjustment of the beacon transmission power is an effective method for reducing and preventing channel congestion. In this study, the primary factors that influence wireless channel loading are selected to construct the KF-BCLF, which is a channel load forecasting algorithm based on a recursive Kalman filter and employs multiple regression equation. By pre-adjusting the transmission power based on the forecasted channel load, the channel load was kept within a predefined range; therefore, channel congestion was prevented. Based on this method, the CLF-BTPC, which is a transmission power control algorithm, is proposed. To verify KF-BCLF algorithm, a traffic survey method that involved the collection of floating car data along a major traffic road in Changchun City is employed. By comparing this forecast with the measured channel loads, the proposed KF-BCLF algorithm was proven to be effective. In addition, the CLF-BTPC algorithm is verified by simulating a section of eight-lane highway and a signal-controlled urban intersection. The results of the two verification process indicate that this distributed CLF-BTPC algorithm can effectively control channel load, prevent channel congestion, and enhance the stability and robustness of wireless beacon transmission in a vehicular network.

  14. Contribution of Strong Discontinuities to the Power Spectrum of the Solar Wind

    International Nuclear Information System (INIS)

    Borovsky, Joseph E.

    2010-01-01

    Eight and a half years of magnetic field measurements (2 22 samples) from the ACE spacecraft in the solar wind at 1 A.U. are analyzed. Strong (large-rotation-angle) discontinuities in the solar wind are collected and measured. An artificial time series is created that preserves the timing and amplitudes of the discontinuities. The power spectral density of the discontinuity series is calculated and compared with the power spectral density of the solar-wind magnetic field. The strong discontinuities produce a power-law spectrum in the ''inertial subrange'' with a spectral index near the Kolmogorov -5/3 index. The discontinuity spectrum contains about half of the power of the full solar-wind magnetic field over this ''inertial subrange.'' Warnings are issued about the significant contribution of discontinuities to the spectrum of the solar wind, complicating interpretation of spectral power and spectral indices.

  15. Constraints on models with a break in the primordial power spectrum

    Energy Technology Data Exchange (ETDEWEB)

    Li Hong, E-mail: hongli@mail.ihep.ac.c [Institute of High Energy Physics, Chinese Academy of Science, P.O. Box 918-4, Beijing 100049 (China); Theoretical Physics Center for Science Facilities (TPCSF), Chinese Academy of Science (China); Kavli Institute for Theoretical Physics, Chinese Academy of Science, Beijing 100190 (China); Xia Junqing [Scuola Internazionale Superiore di Studi Avanzati, Via Bonomea 265, I-34136 Trieste (Italy); Brandenberger, Robert [Department of Physics, McGill University, 3600 University Street, Montreal, QC, H3A 2T8 (Canada); Institute of High Energy Physics, Chinese Academy of Science, P.O. Box 918-4, Beijing 100049 (China); Theoretical Physics Center for Science Facilities (TPCSF), Chinese Academy of Science (China); Kavli Institute for Theoretical Physics, Chinese Academy of Science, Beijing 100190 (China); Zhang Xinmin [Institute of High Energy Physics, Chinese Academy of Science, P.O. Box 918-4, Beijing 100049 (China); Theoretical Physics Center for Science Facilities (TPCSF), Chinese Academy of Science (China)

    2010-07-05

    One of the characteristics of the 'Matter Bounce' scenario, an alternative to cosmological inflation for producing a scale-invariant spectrum of primordial adiabatic fluctuations on large scales, is a break in the power spectrum at a characteristic scale, below which the spectral index changes from n{sub s}=1 to n{sub s}=3. We study the constraints which current cosmological data place on the location of such a break, and more generally on the position of the break and the slope at length scales smaller than the break. The observational data we use include the WMAP five-year data set (WMAP5), other CMB data from BOOMERanG, CBI, VSA, and ACBAR, large-scale structure data from the Sloan Digital Sky Survey (SDSS, their luminous red galaxies sample), Type Ia Supernovae data (the 'Union' compilation), and the Sloan Digital Sky Survey Lyman-{alpha} forest power spectrum (Ly{alpha}) data. We employ the Markov Chain Monte Carlo method to constrain the features in the primordial power spectrum which are motivated by the matter bounce model. We give an upper limit on the length scale where the break in the spectrum occurs.

  16. Constraints on models with a break in the primordial power spectrum

    International Nuclear Information System (INIS)

    Li Hong; Xia Junqing; Brandenberger, Robert; Zhang Xinmin

    2010-01-01

    One of the characteristics of the 'Matter Bounce' scenario, an alternative to cosmological inflation for producing a scale-invariant spectrum of primordial adiabatic fluctuations on large scales, is a break in the power spectrum at a characteristic scale, below which the spectral index changes from n s =1 to n s =3. We study the constraints which current cosmological data place on the location of such a break, and more generally on the position of the break and the slope at length scales smaller than the break. The observational data we use include the WMAP five-year data set (WMAP5), other CMB data from BOOMERanG, CBI, VSA, and ACBAR, large-scale structure data from the Sloan Digital Sky Survey (SDSS, their luminous red galaxies sample), Type Ia Supernovae data (the 'Union' compilation), and the Sloan Digital Sky Survey Lyman-α forest power spectrum (Lyα) data. We employ the Markov Chain Monte Carlo method to constrain the features in the primordial power spectrum which are motivated by the matter bounce model. We give an upper limit on the length scale where the break in the spectrum occurs.

  17. Assessment of storm forecast

    DEFF Research Database (Denmark)

    Cutululis, Nicolaos Antonio; Hahmann, Andrea N.; Huus Bjerge, Martin

    When wind speed exceeds a certain value, wind turbines shut-down in order to protect their structure. This leads to sudden wind plants shut down and to new challenges concerning the secure operation of the pan-European electric system with future large scale offshore wind power. This task aims...... stopped, completely or partially, producing due to extreme wind speeds. Wind speed and power measurements from those events are presented and compared to the forecast available at Energinet.dk. The analysis looked at wind speed and wind power forecast. The main conclusion of the analysis is that the wind...... to consider it an EWP) and that the available wind speed forecasts are given as a mean wind speed over a rather large area. At wind power level, the analysis shows that prediction of accurate production levels from a wind farm experiencing EWP is rather poor. This is partially because the power curve...

  18. Intra-Minute Cloud Passing Forecasting Based on a Low Cost IoT Sensor—A Solution for Smoothing the Output Power of PV Power Plants

    Science.gov (United States)

    Sukič, Primož; Štumberger, Gorazd

    2017-01-01

    Clouds moving at a high speed in front of the Sun can cause step changes in the output power of photovoltaic (PV) power plants, which can lead to voltage fluctuations and stability problems in the connected electricity networks. These effects can be reduced effectively by proper short-term cloud passing forecasting and suitable PV power plant output power control. This paper proposes a low-cost Internet of Things (IoT)-based solution for intra-minute cloud passing forecasting. The hardware consists of a Raspberry PI Model B 3 with a WiFi connection and an OmniVision OV5647 sensor with a mounted wide-angle lens, a circular polarizing (CPL) filter and a natural density (ND) filter. The completely new algorithm for cloud passing forecasting uses the green and blue colors in the photo to determine the position of the Sun, to recognize the clouds, and to predict their movement. The image processing is performed in several stages, considering selectively only a small part of the photo relevant to the movement of the clouds in the vicinity of the Sun in the next minute. The proposed algorithm is compact, fast and suitable for implementation on low cost processors with low computation power. The speed of the cloud parts closest to the Sun is used to predict when the clouds will cover the Sun. WiFi communication is used to transmit this data to the PV power plant control system in order to decrease the output power slowly and smoothly. PMID:28505078

  19. Forecasts for CMB μ and i-type spectral distortion constraints on the primordial power spectrum on scales 8∼4 Mpc−1 with the future Pixie-like experiments

    International Nuclear Information System (INIS)

    Khatri, Rishi; Sunyaev, Rashid A.

    2013-01-01

    Silk damping at redshifts 1.5 × 10 4 ∼ 6 erases CMB anisotropies on scales corresponding to the comoving wavenumbers 8∼ 4 Mpc −1 (10 5 ∼ 8 ). This dissipated energy is gained by the CMB monopole, creating distortions from a blackbody in the CMB spectrum of the μ-type and the i-type. We study, using Fisher matrices, the constraints we can get from measurements of these spectral distortions on the primordial power spectrum from future experiments such as Pixie, and how these constraints change as we change the frequency resolution and the sensitivity of the experiment. We show that the additional information in the shape of the i-type distortions, in combination with the μ-type distortions, allows us to break the degeneracy between the amplitude and the spectral index of the power spectrum on these scales and leads to much tighter constraints. We quantify the information contained in both the μ-type distortions and the i-type distortions taking into account the partial degeneracy with the y-type distortions and the temperature of the blackbody part of the CMB. We also calculate the constraints possible on the primordial power spectrum when the spectral distortion information is combined with the CMB anisotropies measured by the WMAP, SPT, ACT and Planck experiments

  20. Supernovae anisotropy power spectrum

    Energy Technology Data Exchange (ETDEWEB)

    Ghodsi, Hoda; Baghram, Shant [Department of Physics, Sharif University of Technology, P.O. Box 11155-9161, Tehran (Iran, Islamic Republic of); Habibi, Farhang, E-mail: h.ghodsi@mehr.sharif.ir, E-mail: baghram@sharif.edu, E-mail: habibi@lal.in2p3.fr [LAL-IN2P3/CNRS, BP 34, 91898 Orsay Cedex (France)

    2017-10-01

    We contribute another anisotropy study to this field of research using Type Ia supernovae (SNe Ia). In this work, we utilise the power spectrum calculation method and apply it to both the current SNe Ia data and simulation. Using the Union2.1 data set at all redshifts, we compare the spectrum of the residuals of the observed distance moduli to that expected from an isotropic universe affected by the Union2.1 observational uncertainties at low multipoles. Through this comparison we find a dipolar anisotropy with tension of less that 2σ towards l = 171° ± 21° and b = −26° ± 28° which is mainly induced by anisotropic spatial distribution of the SNe with z > 0.2 rather than being a cosmic effect. Furthermore, we find a tension of ∼ 4σ at ℓ = 4 between the two spectra. Our simulations are constructed with the characteristics of the upcoming surveys like the Large Synoptic Survey Telescope (LSST), which shall bring us the largest SNe Ia collection to date. We make predictions for the amplitude of a possible dipolar anisotropy that would be detectable by future SNe Ia surveys.

  1. Signature of short distance physics on inflation power spectrum and CMB anisotropy

    International Nuclear Information System (INIS)

    Das, Suratna; Mohanty, Subhendra

    2009-01-01

    The inflaton field responsible for inflation may not be a canonical fundamental scalar. It is possible that the inflaton is a composite of fermions or it may have a decay width. In these cases the standard procedure for calculating the power spectrum is not applicable and a new formalism needs to be developed to determine the effect of short range interactions of the inflaton on the power spectrum and the CMB anisotropy. We develop a general formalism for computing the power spectrum of curvature perturbations for such non-canonical cases by using the flat space Källén-Lehmann spectral function in curved quasi-de Sitter space assuming implicitly that the Bunch-Davis boundary conditions enforces the inflaton mode functions to be plane wave in the short wavelength limit and a complete set of mode functions exists in quasi-de Sitter space. It is observed that the inflaton with a decay width suppresses the power at large scale while a composite inflaton's power spectrum oscillates at large scales. These observations may be vindicated in the WMAP data and confirmed by future observations with PLANCK

  2. On-line quantile regression in the RKHS (Reproducing Kernel Hilbert Space) for operational probabilistic forecasting of wind power

    International Nuclear Information System (INIS)

    Gallego-Castillo, Cristobal; Bessa, Ricardo; Cavalcante, Laura; Lopez-Garcia, Oscar

    2016-01-01

    Wind power probabilistic forecast is being used as input in several decision-making problems, such as stochastic unit commitment, operating reserve setting and electricity market bidding. This work introduces a new on-line quantile regression model based on the Reproducing Kernel Hilbert Space (RKHS) framework. Its application to the field of wind power forecasting involves a discussion on the choice of the bias term of the quantile models, and the consideration of the operational framework in order to mimic real conditions. Benchmark against linear and splines quantile regression models was performed for a real case study during a 18 months period. Model parameter selection was based on k-fold crossvalidation. Results showed a noticeable improvement in terms of calibration, a key criterion for the wind power industry. Modest improvements in terms of Continuous Ranked Probability Score (CRPS) were also observed for prediction horizons between 6 and 20 h ahead. - Highlights: • New online quantile regression model based on the Reproducing Kernel Hilbert Space. • First application to operational probabilistic wind power forecasting. • Modest improvements of CRPS for prediction horizons between 6 and 20 h ahead. • Noticeable improvements in terms of Calibration due to online learning.

  3. Constraining the primordial power spectrum from SNIa lensing dispersion

    Energy Technology Data Exchange (ETDEWEB)

    Ben-Dayan, Ido [Deutsches Elektronen-Synchrotron (DESY), Hamburg (Germany); Kalaydzhyan, Tigran [State Univ. of New York, Stony Brook, NY (United States). Dept. of Physics and Astronomy

    2013-09-15

    The (absence of detecting) lensing dispersion of Supernovae type Ia (SNIa) can be used as a novel and extremely efficient probe of cosmology. In this preliminary example we analyze its consequences for the primordial power spectrum. The main setback is the knowledge of the power spectrum in the non-linear regime, 1 Mpc{sup -1}power spectrum. The probe extends our handle on the spectrum to a total of 12-15 inflation e-folds. These constraints are so strong that they are already ruling out a large portion of the parameter space allowed by PLANCK for running {alpha}{identical_to}dn{sub s}/d ln k and running of running {beta}{identical_to}d{sup 2}n{sub s}/d ln k{sup 2}. The bounds follow a linear relation to a very good accuracy. A conservative bound disfavours any enhancement above the line {beta}(k{sub 0})=0.032-0.41{alpha}(k{sub 0}) and a realistic estimate disfavours any enhancement above the line {beta}(k{sub 0})=0.019-0.45{alpha}(k{sub 0}).

  4. Forecastability as a Design Criterion in Wind Resource Assessment: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, J.; Hodge, B. M.

    2014-04-01

    This paper proposes a methodology to include the wind power forecasting ability, or 'forecastability,' of a site as a design criterion in wind resource assessment and wind power plant design stages. The Unrestricted Wind Farm Layout Optimization (UWFLO) methodology is adopted to maximize the capacity factor of a wind power plant. The 1-hour-ahead persistence wind power forecasting method is used to characterize the forecastability of a potential wind power plant, thereby partially quantifying the integration cost. A trade-off between the maximum capacity factor and the forecastability is investigated.

  5. Classical and modern power spectrum estimation for tune measurement in CSNS RCS

    International Nuclear Information System (INIS)

    Yang Xiaoyu; Xu Taoguang; Fu Shinian; Zeng Lei; Bian Xiaojuan

    2013-01-01

    Precise measurement of betatron tune is required for good operating condition of CSNS RCS. The fractional part of betatron tune is important and it can be measured by analyzing the signals of beam position from the appointed BPM. Usually these signals are contaminated during the acquisition process, therefore several power spectrum methods are used to improve the frequency resolution. In this article classical and modern power spectrum methods are used. In order to compare their performance, the results of simulation data and IQT data from J-PARC RCS are discussed. It is shown that modern power spectrum estimation has better performance than the classical ones, though the calculation is more complex. (authors)

  6. Power spectrum, correlation function, and tests for luminosity bias in the CfA redshift survey

    Science.gov (United States)

    Park, Changbom; Vogeley, Michael S.; Geller, Margaret J.; Huchra, John P.

    1994-01-01

    We describe and apply a method for directly computing the power spectrum for the galaxy distribution in the extension of the Center for Astrophysics Redshift Survey. Tests show that our technique accurately reproduces the true power spectrum for k greater than 0.03 h Mpc(exp -1). The dense sampling and large spatial coverage of this survey allow accurate measurement of the redshift-space power spectrum on scales from 5 to approximately 200 h(exp -1) Mpc. The power spectrum has slope n approximately equal -2.1 on small scales (lambda less than or equal 25 h(exp -1) Mpc) and n approximately -1.1 on scales 30 less than lambda less than 120 h(exp -1) Mpc. On larger scales the power spectrum flattens somewhat, but we do not detect a turnover. Comparison with N-body simulations of cosmological models shows that an unbiased, open universe CDM model (OMEGA h = 0.2) and a nonzero cosmological constant (CDM) model (OMEGA h = 0.24, lambda(sub zero) = 0.6, b = 1.3) match the CfA power spectrum over the wavelength range we explore. The standard biased CDM model (OMEGA h = 0.5, b = 1.5) fails (99% significance level) because it has insufficient power on scales lambda greater than 30 h(exp -1) Mpc. Biased CDM with a normalization that matches the Cosmic Microwave Background (CMB) anisotropy (OMEGA h = 0.5, b = 1.4, sigma(sub 8) (mass) = 1) has too much power on small scales to match the observed galaxy power spectrum. This model with b = 1 matches both Cosmic Background Explorer Satellite (COBE) and the small-scale power spect rum but has insufficient power on scales lambda approximately 100 h(exp -1) Mpc. We derive a formula for the effect of small-scale peculiar velocities on the power spectrum and combine this formula with the linear-regime amplification described by Kaiser to compute an estimate of the real-space power spectrum. Two tests reveal luminosity bias in the galaxy distribution: First, the amplitude of the pwer spectrum is approximately 40% larger for the brightest

  7. The Wind Forecast Improvement Project (WFIP). A Public/Private Partnership for Improving Short Term Wind Energy Forecasts and Quantifying the Benefits of Utility Operations -- the Northern Study Area

    Energy Technology Data Exchange (ETDEWEB)

    Finley, Cathy [WindLogics, St. Paul, MN (United States)

    2014-04-30

    This report contains the results from research aimed at improving short-range (0-6 hour) hub-height wind forecasts in the NOAA weather forecast models through additional data assimilation and model physics improvements for use in wind energy forecasting. Additional meteorological observing platforms including wind profilers, sodars, and surface stations were deployed for this study by NOAA and DOE, and additional meteorological data at or near wind turbine hub height were provided by South Dakota State University and WindLogics/NextEra Energy Resources over a large geographical area in the U.S. Northern Plains for assimilation into NOAA research weather forecast models. The resulting improvements in wind energy forecasts based on the research weather forecast models (with the additional data assimilation and model physics improvements) were examined in many different ways and compared with wind energy forecasts based on the current operational weather forecast models to quantify the forecast improvements important to power grid system operators and wind plant owners/operators participating in energy markets. Two operational weather forecast models (OP_RUC, OP_RAP) and two research weather forecast models (ESRL_RAP, HRRR) were used as the base wind forecasts for generating several different wind power forecasts for the NextEra Energy wind plants in the study area. Power forecasts were generated from the wind forecasts in a variety of ways, from very simple to quite sophisticated, as they might be used by a wide range of both general users and commercial wind energy forecast vendors. The error characteristics of each of these types of forecasts were examined and quantified using bulk error statistics for both the local wind plant and the system aggregate forecasts. The wind power forecast accuracy was also evaluated separately for high-impact wind energy ramp events. The overall bulk error statistics calculated over the first six hours of the forecasts at both the

  8. An Optimized Prediction Intervals Approach for Short Term PV Power Forecasting

    Directory of Open Access Journals (Sweden)

    Qiang Ni

    2017-10-01

    Full Text Available High quality photovoltaic (PV power prediction intervals (PIs are essential to power system operation and planning. To improve the reliability and sharpness of PIs, in this paper, a new method is proposed, which involves the model uncertainties and noise uncertainties, and PIs are constructed with a two-step formulation. In the first step, the variance of model uncertainties is obtained by using extreme learning machine to make deterministic forecasts of PV power. In the second stage, innovative PI-based cost function is developed to optimize the parameters of ELM and noise uncertainties are quantization in terms of variance. The performance of the proposed approach is examined by using the PV power and meteorological data measured from 1kW rooftop DC micro-grid system. The validity of the proposed method is verified by comparing the experimental analysis with other benchmarking methods, and the results exhibit a superior performance.

  9. Analysis and forecast of maintenance in power plants; Instandhaltungsanalyse und -prognose fuer Grosskraftwerke

    Energy Technology Data Exchange (ETDEWEB)

    Kittan, Thomas [Vattenfall Europe Generation AG, Spremberg (Germany). Standort Technischer Service; Herold, Matthias [TUEV SUED Industrie Service GmbH, Chemnitz (Germany); Baumann, Carsten [TUEV SUED Industrie Service GmbH, Dresden (Germany)

    2011-07-01

    A comprehensive assessment of the 'Schwarze Pumpe' power station has given insights into the effectiveness of maintenance measures and facilitated the budgeting of future maintenance for four additional power station units. Vattenfall commissioned experts from TUeV SUeD Industrie Service to carry out third-party analysis of its maintenance measures and forecast future maintenance budgets. The aim was to obtain a valid data set enabling the detailed assessment of past and future activities and pointing out potentials for improvement. (orig.)

  10. The shape of the primordial power spectrum: A last stand before Planck data

    International Nuclear Information System (INIS)

    Peiris, Hiranya V.; Verde, Licia

    2010-01-01

    We present a minimally parametric reconstruction of the primordial power spectrum using the most recent cosmic microwave background and large-scale structure data sets. Our goal is to constrain the shape of the power spectrum while simultaneously avoiding strong theoretical priors and over-fitting of the data. We find no evidence for any departure from a power-law spectral index. We also find that an exact scale-invariant power spectrum is disfavored by the data, but this conclusion is weaker than the corresponding result assuming a theoretically-motivated power-law spectral index prior. The reconstruction shows that better data are crucial to justify the adoption of such a strong theoretical prior observationally. These results can be used to determine the robustness of our present knowledge when compared with forthcoming precision data from Planck.

  11. Wind tunnel study of the power output spectrum in a micro wind farm

    International Nuclear Information System (INIS)

    Bossuyt, Juliaan; Meyers, Johan; Howland, Michael F.; Meneveau, Charles

    2016-01-01

    Instrumented small-scale porous disk models are used to study the spectrum of a surrogate for the power output in a micro wind farm with 100 models of wind turbines. The power spectra of individual porous disk models in the first row of the wind farm show the expected -5/3 power law at higher frequencies. Downstream models measure an increased variance due to wake effects. Conversely, the power spectrum of the sum of the power over the entire wind farm shows a peak at the turbine-to-turbine travel frequency between the model turbines, and a near -5/3 power law region at a much wider range of lower frequencies, confirming previous LES results. Comparison with the spectrum that would result when assuming that the signals are uncorrelated, highlights the strong effects of correlations and anti-correlations in the fluctuations at various frequencies. (paper)

  12. A comparison between the ECMWF and COSMO Ensemble Prediction Systems applied to short-term wind power forecasting on real data

    DEFF Research Database (Denmark)

    Alessandrini, S.; Sperati, S.; Pinson, Pierre

    2013-01-01

    together with a single forecast power value for each future time horizon. A comparison between two different ensemble forecasting models, ECMWF EPS (Ensemble Prediction System in use at the European Centre for Medium-Range Weather Forecasts) and COSMO-LEPS (Limited-area Ensemble Prediction System developed...... ahead forecast horizon. A statistical calibration of the ensemble wind speed members based on the use of past wind speed measurements is explained. The two models are compared using common verification indices and diagrams. The higher horizontal resolution model (COSMO-LEPS) shows slightly better...

  13. An automatic method to determine cutoff frequency based on image power spectrum

    International Nuclear Information System (INIS)

    Beis, J.S.; Vancouver Hospital and Health Sciences Center, British Columbia; Celler, A.; Barney, J.S.

    1995-01-01

    The authors present an algorithm for automatically choosing filter cutoff frequency (F c ) using the power spectrum of the projections. The method is based on the assumption that the expectation of the image power spectrum is the sum of the expectation of the blurred object power spectrum (dominant at low frequencies) plus a constant value due to Poisson noise. By considering the discrete components of the noise-dominated high-frequency spectrum as a Gaussian distribution N(μ,σ), the Student t-test determines F c as the highest frequency for which the image frequency components are unlikely to be drawn from N (μ,σ). The method is general and can be applied to any filter. In this work, the authors tested the approach using the Metz restoration filter on simulated, phantom, and patient data with good results. Quantitative performance of the technique was evaluated by plotting recovery coefficient (RC) versus NMSE of reconstructed images

  14. Dark Energy Constraints from the Thermal Sunyaev Zeldovich Power Spectrum

    Science.gov (United States)

    Bolliet, Boris; Comis, Barbara; Komatsu, Eiichiro; Macías-Pérez, Juan Francisco

    2018-03-01

    We constrain the dark energy equation of state parameter, w, using the power spectrum of the thermal Sunyaev-Zeldovich (tSZ) effect. We improve upon previous analyses by taking into account the trispectrum in the covariance matrix and marginalising over the foreground parameters, the correlated noise, the mass bias B in the Planck universal pressure profile, and all the relevant cosmological parameters (i.e., not just Ωm and σ8). We find that the amplitude of the tSZ power spectrum at ℓ ≲ 103 depends primarily on F ≡ σ8(Ωm/B)0.40h-0.21, where B is related to more commonly used variable b by B = (1 - b)-1. We measure this parameter with 2.6% precision, F = 0.460 ± 0.012 (68% CL). By fixing the bias to B = 1.25 and adding the local determination of the Hubble constant H0 and the amplitude of the primordial power spectrum constrained by the Planck Cosmic Microwave Background (CMB) data, we find w = -1.10 ± 0.12, σ8 = 0.802 ± 0.037, and Ωm = 0.265 ± 0.022 (68% CL). Our limit on w is consistent with and is as tight as that from the distance-alone constraint from the CMB and H0. Finally, by combining the tSZ power spectrum and the CMB data we find, in the Λ Cold Dark Matter (CDM) model, the mass bias of B = 1.71 ± 0.17, i.e., 1 - b = 0.58 ± 0.06 (68% CL).

  15. The influence of the new ECMWF Ensemble Prediction System resolution on wind power forecast accuracy and uncertainty estimation

    DEFF Research Database (Denmark)

    Alessandrini, S.; Pinson, Pierre; Sperati, S.

    2011-01-01

    The importance of wind power forecasting (WPF) is nowadays commonly recognized because it represents a useful tool to reduce problems of grid integration and to facilitate energy trading. If on one side the prediction accuracy is fundamental to these scopes, on the other it has become also clear...... by a recalibration procedure that allowed obtaining a more uniform distribution among the 51 intervals, making the ensemble spread large enough to include the observations. After that it was observed that the EPS power spread seemed to have enough correlation with the error calculated on the deterministic forecast...

  16. 2015 Plan. Project 2: the electric power sector and the Brazilian economy: insertion and forecasts

    International Nuclear Information System (INIS)

    1993-05-01

    This project shows the economic and the energetic view of the Brazilian electric power sector, mentioning the actual conjuncture; the economy evolution; some sector forecasts; demographical aspects; international price of petroleum and National Energetic Matrix. (C.G.C.)

  17. Red, Straight, no bends: primordial power spectrum reconstruction from CMB and large-scale structure

    Energy Technology Data Exchange (ETDEWEB)

    Ravenni, Andrea [Dipartimento di Fisica e Astronomia ' ' G. Galilei' ' , Università degli Studi di Padova, via Marzolo 8, I-35131, Padova (Italy); Verde, Licia; Cuesta, Antonio J., E-mail: andrea.ravenni@pd.infn.it, E-mail: liciaverde@icc.ub.edu, E-mail: ajcuesta@icc.ub.edu [Institut de Ciències del Cosmos (ICCUB), Universitat de Barcelona (IEEC-UB), Martí i Franquès 1, E08028 Barcelona (Spain)

    2016-08-01

    We present a minimally parametric, model independent reconstruction of the shape of the primordial power spectrum. Our smoothing spline technique is well-suited to search for smooth features such as deviations from scale invariance, and deviations from a power law such as running of the spectral index or small-scale power suppression. We use a comprehensive set of the state-of the art cosmological data: Planck observations of the temperature and polarisation anisotropies of the cosmic microwave background, WiggleZ and Sloan Digital Sky Survey Data Release 7 galaxy power spectra and the Canada-France-Hawaii Lensing Survey correlation function. This reconstruction strongly supports the evidence for a power law primordial power spectrum with a red tilt and disfavours deviations from a power law power spectrum including small-scale power suppression such as that induced by significantly massive neutrinos. This offers a powerful confirmation of the inflationary paradigm, justifying the adoption of the inflationary prior in cosmological analyses.

  18. Modelling and short-term forecasting of daily peak power demand in Victoria using two-dimensional wavelet based SDP models

    International Nuclear Information System (INIS)

    Truong, Nguyen-Vu; Wang, Liuping; Wong, Peter K.C.

    2008-01-01

    Power demand forecasting is of vital importance to the management and planning of power system operations which include generation, transmission, distribution, as well as system's security analysis and economic pricing processes. This paper concerns the modeling and short-term forecast of daily peak power demand in the state of Victoria, Australia. In this study, a two-dimensional wavelet based state dependent parameter (SDP) modelling approach is used to produce a compact mathematical model for this complex nonlinear dynamic system. In this approach, a nonlinear system is expressed by a set of linear regressive input and output terms (state variables) multiplied by the respective state dependent parameters that carry the nonlinearities in the form of 2-D wavelet series expansions. This model is identified based on historical data, descriptively representing the relationship and interaction between various components which affect the peak power demand of a certain day. The identified model has been used to forecast daily peak power demand in the state of Victoria, Australia in the time period from the 9th of August 2007 to the 24th of August 2007. With a MAPE (mean absolute prediction error) of 1.9%, it has clearly implied the effectiveness of the identified model. (author)

  19. Wavelet-based multi-resolution analysis and artificial neural networks for forecasting temperature and thermal power consumption

    OpenAIRE

    Eynard , Julien; Grieu , Stéphane; Polit , Monique

    2011-01-01

    15 pages; International audience; As part of the OptiEnR research project, the present paper deals with outdoor temperature and thermal power consumption forecasting. This project focuses on optimizing the functioning of a multi-energy district boiler (La Rochelle, west coast of France), adding to the plant a thermal storage unit and implementing a model-based predictive controller. The proposed short-term forecast method is based on the concept of time series and uses both a wavelet-based mu...

  20. Galactic densities, substructure and the initial power spectrum

    International Nuclear Information System (INIS)

    Bullock, J.S.; Zentner, A.R.

    2003-01-01

    Although the currently favored cold dark matter plus cosmological constant model for structure formation assumes an n = 1 scale-invariant initial power spectrum, most inflation models produce at least mild deviations from n = 1. Because the lever arm from the CMB normalization to galaxy scales is long, even a small 'tilt' can have important implications for galactic observations. Here we calculate the COBS-normalized power spectra for several well-motivated models of inflation and compute implications for the substructure content and central densities of galaxy halos. Using an analytic model, normalized against N-body simulations, we show that while halos in the standard (n = 1) model are overdense by a factor of ∼ 6 compared to observations, several of our example inflation+LCDM models predict halo densities well within the range of observations, which prefer models with n ∼ 0.85. We go on to use a semi-analytic model (also normalized against N-body simulations) to follow the merger histories of galaxy-sized halos and track the orbital decay, disruption, and evolution of the merging substructure. Models with n ∼ 0.85 predict a factor of ∼ 3 fewer subhalos at a fixed circular velocity than the standard n 1 case. Although this level of reduction does not resolve the 'dwarf satellite problem', it does imply that the level of feedback required to match the observed number of dwarfs is sensitive to the initial power spectrum. Finally, the fraction of galaxy-halo mass that is bound up in substructure is consistent with limits imposed by multiply imaged quasars for all models considered: f sat > 0.01 even for an effective tilt of n ∼ 0.8. We conclude that, at their current level, lensing constraints of this kind do not provide an interesting probe of the primordial power spectrum

  1. An independent system operator's perspective on operational ramp forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Porter, G. [New Brunswick System Operator, Fredericton, NB (Canada)

    2010-07-01

    One of the principal roles of the power system operator is to select the most economical resources to reliably supply electric system power needs. Operational wind power production forecasts are required by system operators in order to understand the impact of ramp event forecasting on dispatch functions. A centralized dispatch approach can contribute to a more efficient use of resources that traditional economic dispatch methods. Wind ramping events can have a significant impact on system reliability. Power systems can have constrained or robust transmission systems, and may also be islanded or have large connections to neighbouring systems. Power resources can include both flexible and inflexible generation resources. Wind integration tools must be used by system operators to improve communications and connections with wind power plants. Improved wind forecasting techniques are also needed. Sensitivity to forecast errors is dependent on current system conditions. System operators require basic production forecasts, probabilistic forecasts, and event forecasts. Forecasting errors were presented as well as charts outlining the implications of various forecasts. tabs., figs.

  2. Short-term spatio-temporal wind power forecast in robust look-ahead power system dispatch

    KAUST Repository

    Xie, Le; Gu, Yingzhong; Zhu, Xinxin; Genton, Marc G.

    2014-01-01

    forecasts, the overall cost benefits on system dispatch can be quantified. We integrate the improved forecast with an advanced robust look-ahead dispatch framework. This integrated forecast and economic dispatch framework is tested in a modified IEEE RTS 24

  3. Study on the Evolution Mechanism and Development Forecasting of China’s Power Supply Structure Clean Development

    Directory of Open Access Journals (Sweden)

    Xiaohua Song

    2017-02-01

    Full Text Available The clean development of China’s power supply structure has become a crucial strategic problem for the low-carbon, green development of Chinese society. Considering the subsistent developments of optimized allocation of energy resources and efficient utilization, the urgent need to solve environmental pollution, and the continuously promoted power market-oriented reform, further study of China’s power structure clean development has certain theoretical value. Based on the data analysis, this paper analyzes the key factors that influence the evolution process of the structure with the help of system dynamics theory and carries out comprehensive assessments after the construction of the structure evaluation system. Additionally, a forecasting model of the power supply structure development based on the Vector Autoregressive Model (VAR has been put forward to forecast the future structure. Through the research of policy review and scenario analysis, the paths and directions of structure optimization are proposed. In this paper, the system dynamics, vector autoregressive model (VAR, policy mining, and scenario analysis methods are combined to systematically demonstrate the evolution of China’s power structure, and predict the future direction of development. This research may provide a methodological and practical reference for the analysis of China’s power supply structure optimization development and for theoretical studies.

  4. Day-ahead wind speed forecasting using f-ARIMA models

    International Nuclear Information System (INIS)

    Kavasseri, Rajesh G.; Seetharaman, Krithika

    2009-01-01

    With the integration of wind energy into electricity grids, it is becoming increasingly important to obtain accurate wind speed/power forecasts. Accurate wind speed forecasts are necessary to schedule dispatchable generation and tariffs in the day-ahead electricity market. This paper examines the use of fractional-ARIMA or f-ARIMA models to model, and forecast wind speeds on the day-ahead (24 h) and two-day-ahead (48 h) horizons. The models are applied to wind speed records obtained from four potential wind generation sites in North Dakota. The forecasted wind speeds are used in conjunction with the power curve of an operational (NEG MICON, 750 kW) turbine to obtain corresponding forecasts of wind power production. The forecast errors in wind speed/power are analyzed and compared with the persistence model. Results indicate that significant improvements in forecasting accuracy are obtained with the proposed models compared to the persistence method. (author)

  5. 7 CFR 1710.206 - Approval requirements for load forecasts prepared pursuant to approved load forecast work plans.

    Science.gov (United States)

    2010-01-01

    ... financial ratings, and participation in reliability council, power pool, regional transmission group, power... analysis and modeling of the borrower's electric system loads as provided for in the load forecast work plan. (5) A narrative discussing the borrower's past, existing, and forecast of future electric system...

  6. A new method to cluster genomes based on cumulative Fourier power spectrum.

    Science.gov (United States)

    Dong, Rui; Zhu, Ziyue; Yin, Changchuan; He, Rong L; Yau, Stephen S-T

    2018-06-20

    Analyzing phylogenetic relationships using mathematical methods has always been of importance in bioinformatics. Quantitative research may interpret the raw biological data in a precise way. Multiple Sequence Alignment (MSA) is used frequently to analyze biological evolutions, but is very time-consuming. When the scale of data is large, alignment methods cannot finish calculation in reasonable time. Therefore, we present a new method using moments of cumulative Fourier power spectrum in clustering the DNA sequences. Each sequence is translated into a vector in Euclidean space. Distances between the vectors can reflect the relationships between sequences. The mapping between the spectra and moment vector is one-to-one, which means that no information is lost in the power spectra during the calculation. We cluster and classify several datasets including Influenza A, primates, and human rhinovirus (HRV) datasets to build up the phylogenetic trees. Results show that the new proposed cumulative Fourier power spectrum is much faster and more accurately than MSA and another alignment-free method known as k-mer. The research provides us new insights in the study of phylogeny, evolution, and efficient DNA comparison algorithms for large genomes. The computer programs of the cumulative Fourier power spectrum are available at GitHub (https://github.com/YaulabTsinghua/cumulative-Fourier-power-spectrum). Copyright © 2018. Published by Elsevier B.V.

  7. Forecasted balance sheet of the power supply and demand equilibrium in France. 2007 issue

    International Nuclear Information System (INIS)

    2007-01-01

    Conformably with the law from February 10, 2000, RTE, the French power transportation network is liable for establishing, at least every two year, a pluri-annual forecasted balance sheet of the power supply and demand equilibrium. Its aim is to identify the unbalance risks between the power consumption and the available generation means. To perform this technical expertise, RTE establishes some forecasts of domestic power consumption which are compared to the known perspectives of evolution of the production means. Two main changes have been taken into consideration in this analysis: the improvement of the energy efficiency, and the decay of power consumption in the big industry. Therefore, the new reference scenario indicates a consumption growth of 1.3% per year up to 2010 and 1% only for the next decade, i.e. 534 TWh of annual power consumption for 2020. On the offer side, several projects of new production means (mainly gas combined cycles) have been accepted during the last two years which represent more than 13000 MW of additional power. On the other hand, the decommissioning of several old fossil fuel power plants is foreseen for 2015 and represent 4400 MW. The offer based on decentralized production means is changing too, mainly thanks to the development of the wind power industry. In order to reach the supply-demand equilibrium, an acceptability threshold for failure duration has been defined by the public authorities and is limited to 3 hours per year. According to the reference scenario, the security of supplies in France seems to be reasonably assured for the next five years to come. A complement of 10500 MW will be necessary to meet the demand foreseen for 2020. (J.S.)

  8. Prophetic forecast on the nuclear power applications

    International Nuclear Information System (INIS)

    Lee, Chang-Kun

    1996-01-01

    It was asked to attempt the ''prophetic forecast''. The time required for the doubling of world population continued to shrink, and now it is mere 40 years. The life of a contemporary person is now sustained by some 30,000 different ''daily necessities'', and despite such proliferation of options, the avarice for much more has not diminished. Over the past 35 years, the Korean population has increased by 1.79 times, and the electric power generation by 168.53 fold. Similar mushrooming trends have occurred in water and food consumption, clothing, plastics, paper, iron and steel, aluminum and so forth. The annual minimum temperature in Seoul has sharply jumped up in the last 80 years, and in the last 2-3 years, sea level went up by 10 mm per annum. Nuclear energy will play a crucial role in helping save all forms of life on the earth and keep the biosphere clean and livable, by reducing the discharge of detrimental gases and contaminating effluents. The main cause of various problems is human population burst, but now there may be a reason for some optimism as far as containing unbounded population growth, by the dilution of sperm density in human semen. In order to avoid the crashing of a large planetoid on the earth in 2126, nuclear architects must develop powerful and accurate nuclear weapons to shoot it off course. The prophetic view is that by the active and judicious applications of nuclear power and technology, the continued survival of mankind will be able to be ensured. (K.I.)

  9. Prophetic forecast on the nuclear power applications

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Chang-Kun [Atomic Energy Commission (Korea, Republic of)

    1996-10-01

    It was asked to attempt the ``prophetic forecast``. The time required for the doubling of world population continued to shrink, and now it is mere 40 years. The life of a contemporary person is now sustained by some 30,000 different ``daily necessities``, and despite such proliferation of options, the avarice for much more has not diminished. Over the past 35 years, the Korean population has increased by 1.79 times, and the electric power generation by 168.53 fold. Similar mushrooming trends have occurred in water and food consumption, clothing, plastics, paper, iron and steel, aluminum and so forth. The annual minimum temperature in Seoul has sharply jumped up in the last 80 years, and in the last 2-3 years, sea level went up by 10 mm per annum. Nuclear energy will play a crucial role in helping save all forms of life on the earth and keep the biosphere clean and livable, by reducing the discharge of detrimental gases and contaminating effluents. The main cause of various problems is human population burst, but now there may be a reason for some optimism as far as containing unbounded population growth, by the dilution of sperm density in human semen. In order to avoid the crashing of a large planetoid on the earth in 2126, nuclear architects must develop powerful and accurate nuclear weapons to shoot it off course. The prophetic view is that by the active and judicious applications of nuclear power and technology, the continued survival of mankind will be able to be ensured. (K.I.)

  10. Charting the Parameter Space of the 21-cm Power Spectrum

    Science.gov (United States)

    Cohen, Aviad; Fialkov, Anastasia; Barkana, Rennan

    2018-05-01

    The high-redshift 21-cm signal of neutral hydrogen is expected to be observed within the next decade and will reveal epochs of cosmic evolution that have been previously inaccessible. Due to the lack of observations, many of the astrophysical processes that took place at early times are poorly constrained. In recent work we explored the astrophysical parameter space and the resulting large variety of possible global (sky-averaged) 21-cm signals. Here we extend our analysis to the fluctuations in the 21-cm signal, accounting for those introduced by density and velocity, Lyα radiation, X-ray heating, and ionization. While the radiation sources are usually highlighted, we find that in many cases the density fluctuations play a significant role at intermediate redshifts. Using both the power spectrum and its slope, we show that properties of high-redshift sources can be extracted from the observable features of the fluctuation pattern. For instance, the peak amplitude of ionization fluctuations can be used to estimate whether heating occurred early or late and, in the early case, to also deduce the cosmic mean ionized fraction at that time. The slope of the power spectrum has a more universal redshift evolution than the power spectrum itself and can thus be used more easily as a tracer of high-redshift astrophysics. Its peaks can be used, for example, to estimate the redshift of the Lyα coupling transition and the redshift of the heating transition (and the mean gas temperature at that time). We also show that a tight correlation is predicted between features of the power spectrum and of the global signal, potentially yielding important consistency checks.

  11. Impact of onsite solar generation on system load demand forecast

    International Nuclear Information System (INIS)

    Kaur, Amanpreet; Pedro, Hugo T.C.; Coimbra, Carlos F.M.

    2013-01-01

    Highlights: • We showed the impact onsite solar generation on system demand load forecast. • Forecast performance degrades by 9% and 3% for 1 h and 15 min forecast horizons. • Error distribution for onsite case is best characterized as t-distribution. • Relation between error, solar penetration and solar variability is characterized. - Abstract: Net energy metering tariffs have encouraged the growth of solar PV in the distribution grid. The additional variability associated with weather-dependent renewable energy creates new challenges for power system operators that must maintain and operate ancillary services to balance the grid. To deal with these issues power operators mostly rely on demand load forecasts. Electric load forecast has been used in power industry for a long time and there are several well established load forecasting models. But the performance of these models for future scenario of high renewable energy penetration is unclear. In this work, the impact of onsite solar power generation on the demand load forecast is analyzed for a community that meets between 10% and 15% of its annual power demand and 3–54% of its daily power demand from a solar power plant. Short-Term Load Forecasts (STLF) using persistence, machine learning and regression-based forecasting models are presented for two cases: (1) high solar penetration and (2) no penetration. Results show that for 1-h and 15-min forecasts the accuracy of the models drops by 9% and 3% with high solar penetration. Statistical analysis of the forecast errors demonstrate that the error distribution is best characterized as a t-distribution for the high penetration scenario. Analysis of the error distribution as a function of daily solar penetration for different levels of variability revealed that the solar power variability drives the forecast error magnitude whereas increasing penetration level has a much smaller contribution. This work concludes that the demand forecast error distribution

  12. Ensemble Solar Forecasting Statistical Quantification and Sensitivity Analysis: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Cheung, WanYin; Zhang, Jie; Florita, Anthony; Hodge, Bri-Mathias; Lu, Siyuan; Hamann, Hendrik F.; Sun, Qian; Lehman, Brad

    2015-12-08

    Uncertainties associated with solar forecasts present challenges to maintain grid reliability, especially at high solar penetrations. This study aims to quantify the errors associated with the day-ahead solar forecast parameters and the theoretical solar power output for a 51-kW solar power plant in a utility area in the state of Vermont, U.S. Forecasts were generated by three numerical weather prediction (NWP) models, including the Rapid Refresh, the High Resolution Rapid Refresh, and the North American Model, and a machine-learning ensemble model. A photovoltaic (PV) performance model was adopted to calculate theoretical solar power generation using the forecast parameters (e.g., irradiance, cell temperature, and wind speed). Errors of the power outputs were quantified using statistical moments and a suite of metrics, such as the normalized root mean squared error (NRMSE). In addition, the PV model's sensitivity to different forecast parameters was quantified and analyzed. Results showed that the ensemble model yielded forecasts in all parameters with the smallest NRMSE. The NRMSE of solar irradiance forecasts of the ensemble NWP model was reduced by 28.10% compared to the best of the three NWP models. Further, the sensitivity analysis indicated that the errors of the forecasted cell temperature attributed only approximately 0.12% to the NRMSE of the power output as opposed to 7.44% from the forecasted solar irradiance.

  13. Day-Ahead Wind Power Forecasting Using a Two-Stage Hybrid Modeling Approach Based on SCADA and Meteorological Information, and Evaluating the Impact of Input-Data Dependency on Forecasting Accuracy

    OpenAIRE

    Dehua Zheng; Min Shi; Yifeng Wang; Abinet Tesfaye Eseye; Jianhua Zhang

    2017-01-01

    The power generated by wind generators is usually associated with uncertainties, due to the intermittency of wind speed and other weather variables. This creates a big challenge for transmission system operators (TSOs) and distribution system operators (DSOs) in terms of connecting, controlling and managing power networks with high-penetration wind energy. Hence, in these power networks, accurate wind power forecasts are essential for their reliable and efficient operation. They support TSOs ...

  14. Short-term residential load forecasting: Impact of calendar effects and forecast granularity

    DEFF Research Database (Denmark)

    Lusis, Peter; Khalilpour, Kaveh Rajab; Andrew, Lachlan

    2017-01-01

    forecasting for a single-customer or even down at an appliance level. Access to high resolution data from smart meters has enabled the research community to assess conventional load forecasting techniques and develop new forecasting strategies suitable for demand-side disaggregated loads. This paper studies...... how calendar effects, forecasting granularity and the length of the training set affect the accuracy of a day-ahead load forecast for residential customers. Root mean square error (RMSE) and normalized RMSE were used as forecast error metrics. Regression trees, neural networks, and support vector...... regression yielded similar average RMSE results, but statistical analysis showed that regression trees technique is significantly better. The use of historical load profiles with daily and weekly seasonality, combined with weather data, leaves the explicit calendar effects a very low predictive power...

  15. Wind Energy Management System EMS Integration Project: Incorporating Wind Generation and Load Forecast Uncertainties into Power Grid Operations

    Energy Technology Data Exchange (ETDEWEB)

    Makarov, Yuri V.; Huang, Zhenyu; Etingov, Pavel V.; Ma, Jian; Guttromson, Ross T.; Subbarao, Krishnappa; Chakrabarti, Bhujanga B.

    2010-01-01

    The power system balancing process, which includes the scheduling, real time dispatch (load following) and regulation processes, is traditionally based on deterministic models. Since the conventional generation needs time to be committed and dispatched to a desired megawatt level, the scheduling and load following processes use load and wind and solar power production forecasts to achieve future balance between the conventional generation and energy storage on the one side, and system load, intermittent resources (such as wind and solar generation), and scheduled interchange on the other side. Although in real life the forecasting procedures imply some uncertainty around the load and wind/solar forecasts (caused by forecast errors), only their mean values are actually used in the generation dispatch and commitment procedures. Since the actual load and intermittent generation can deviate from their forecasts, it becomes increasingly unclear (especially, with the increasing penetration of renewable resources) whether the system would be actually able to meet the conventional generation requirements within the look-ahead horizon, what the additional balancing efforts would be needed as we get closer to the real time, and what additional costs would be incurred by those needs. To improve the system control performance characteristics, maintain system reliability, and minimize expenses related to the system balancing functions, it becomes necessary to incorporate the predicted uncertainty ranges into the scheduling, load following, and, in some extent, into the regulation processes. It is also important to address the uncertainty problem comprehensively by including all sources of uncertainty (load, intermittent generation, generators’ forced outages, etc.) into consideration. All aspects of uncertainty such as the imbalance size (which is the same as capacity needed to mitigate the imbalance) and generation ramping requirement must be taken into account. The latter

  16. Comparative Study on KNN and SVM Based Weather Classification Models for Day Ahead Short Term Solar PV Power Forecasting

    Directory of Open Access Journals (Sweden)

    Fei Wang

    2017-12-01

    Full Text Available Accurate solar photovoltaic (PV power forecasting is an essential tool for mitigating the negative effects caused by the uncertainty of PV output power in systems with high penetration levels of solar PV generation. Weather classification based modeling is an effective way to increase the accuracy of day-ahead short-term (DAST solar PV power forecasting because PV output power is strongly dependent on the specific weather conditions in a given time period. However, the accuracy of daily weather classification relies on both the applied classifiers and the training data. This paper aims to reveal how these two factors impact the classification performance and to delineate the relation between classification accuracy and sample dataset scale. Two commonly used classification methods, K-nearest neighbors (KNN and support vector machines (SVM are applied to classify the daily local weather types for DAST solar PV power forecasting using the operation data from a grid-connected PV plant in Hohhot, Inner Mongolia, China. We assessed the performance of SVM and KNN approaches, and then investigated the influences of sample scale, the number of categories, and the data distribution in different categories on the daily weather classification results. The simulation results illustrate that SVM performs well with small sample scale, while KNN is more sensitive to the length of the training dataset and can achieve higher accuracy than SVM with sufficient samples.

  17. Modeling and forecasting of electrical power demands for capacity planning

    Energy Technology Data Exchange (ETDEWEB)

    Al-Shobaki, Salman [Department of Industrial Engineering, Hashemite University, Zarka 13115 (Jordan); Mohsen, Mousa [Department of Mechanical Engineering, Hashemite University, Zarka 13115 (Jordan)

    2008-11-15

    This paper describes the development of forecasting models to predict future generation and electrical power consumption in Jordan. This is critical to production cost since power is generated by burning expensive imported oil. Currently, the National Electric Power Company (NEPCO) is using regression models that only accounts for trend dynamics in their planning of loads and demand levels. The models are simplistic and are based on generated energy historical levels. They produce results on yearly bases and do not account for monthly variability in demand levels. The paper presents two models, one based on the generated energy data and the other is based on the consumed energy data. The models account for trend, monthly seasonality, and cycle dynamics. Both models are compared to NEPCO's model and indicate that NEPCO is producing energy at levels higher than needed (5.25%) thus increasing the loss in generated energy. The developed models also show a 13% difference between the generated energy and the consumed energy that is lost due to transmission line and in-house consumption. (author)

  18. Modeling and forecasting of electrical power demands for capacity planning

    International Nuclear Information System (INIS)

    Al-Shobaki, Salman; Mohsen, Mousa

    2008-01-01

    This paper describes the development of forecasting models to predict future generation and electrical power consumption in Jordan. This is critical to production cost since power is generated by burning expensive imported oil. Currently, the National Electric Power Company (NEPCO) is using regression models that only accounts for trend dynamics in their planning of loads and demand levels. The models are simplistic and are based on generated energy historical levels. They produce results on yearly bases and do not account for monthly variability in demand levels. The paper presents two models, one based on the generated energy data and the other is based on the consumed energy data. The models account for trend, monthly seasonality, and cycle dynamics. Both models are compared to NEPCO's model and indicate that NEPCO is producing energy at levels higher than needed (5.25%) thus increasing the loss in generated energy. The developed models also show a 13% difference between the generated energy and the consumed energy that is lost due to transmission line and in-house consumption

  19. A Multi Time Scale Wind Power Forecasting Model of a Chaotic Echo State Network Based on a Hybrid Algorithm of Particle Swarm Optimization and Tabu Search

    Directory of Open Access Journals (Sweden)

    Xiaomin Xu

    2015-11-01

    Full Text Available The uncertainty and regularity of wind power generation are caused by wind resources’ intermittent and randomness. Such volatility brings severe challenges to the wind power grid. The requirements for ultrashort-term and short-term wind power forecasting with high prediction accuracy of the model used, have great significance for reducing the phenomenon of abandoned wind power , optimizing the conventional power generation plan, adjusting the maintenance schedule and developing real-time monitoring systems. Therefore, accurate forecasting of wind power generation is important in electric load forecasting. The echo state network (ESN is a new recurrent neural network composed of input, hidden layer and output layers. It can approximate well the nonlinear system and achieves great results in nonlinear chaotic time series forecasting. Besides, the ESN is simpler and less computationally demanding than the traditional neural network training, which provides more accurate training results. Aiming at addressing the disadvantages of standard ESN, this paper has made some improvements. Combined with the complementary advantages of particle swarm optimization and tabu search, the generalization of ESN is improved. To verify the validity and applicability of this method, case studies of multitime scale forecasting of wind power output are carried out to reconstruct the chaotic time series of the actual wind power generation data in a certain region to predict wind power generation. Meanwhile, the influence of seasonal factors on wind power is taken into consideration. Compared with the classical ESN and the conventional Back Propagation (BP neural network, the results verify the superiority of the proposed method.

  20. The Short-Term Power Load Forecasting Based on Sperm Whale Algorithm and Wavelet Least Square Support Vector Machine with DWT-IR for Feature Selection

    Directory of Open Access Journals (Sweden)

    Jin-peng Liu

    2017-07-01

    Full Text Available Short-term power load forecasting is an important basis for the operation of integrated energy system, and the accuracy of load forecasting directly affects the economy of system operation. To improve the forecasting accuracy, this paper proposes a load forecasting system based on wavelet least square support vector machine and sperm whale algorithm. Firstly, the methods of discrete wavelet transform and inconsistency rate model (DWT-IR are used to select the optimal features, which aims to reduce the redundancy of input vectors. Secondly, the kernel function of least square support vector machine LSSVM is replaced by wavelet kernel function for improving the nonlinear mapping ability of LSSVM. Lastly, the parameters of W-LSSVM are optimized by sperm whale algorithm, and the short-term load forecasting method of W-LSSVM-SWA is established. Additionally, the example verification results show that the proposed model outperforms other alternative methods and has a strong effectiveness and feasibility in short-term power load forecasting.

  1. Planck 2013 results. XXI. All-sky Compton parameter power spectrum and high-order statistics

    CERN Document Server

    Ade, P.A.R.; Armitage-Caplan, C.; Arnaud, M.; Ashdown, M.; Atrio-Barandela, F.; Aumont, J.; Baccigalupi, C.; Banday, A.J.; Barreiro, R.B.; Bartlett, J.G.; Battaner, E.; Benabed, K.; Benoit, A.; Benoit-Levy, A.; Bernard, J.P.; Bersanelli, M.; Bielewicz, P.; Bobin, J.; Bock, J.J.; Bonaldi, A.; Bond, J.R.; Borrill, J.; Bouchet, F.R.; Bridges, M.; Bucher, M.; Burigana, C.; Butler, R.C.; Cardoso, J.F.; Carvalho, P.; Catalano, A.; Challinor, A.; Chamballu, A.; Chiang, L.Y.; Chiang, H.C.; Christensen, P.R.; Church, S.; Clements, D.L.; Colombi, S.; Colombo, L.P.L.; Comis, B.; Couchot, F.; Coulais, A.; Crill, B.P.; Curto, A.; Cuttaia, F.; Da Silva, A.; Danese, L.; Davies, R.D.; Davis, R.J.; de Bernardis, P.; de Rosa, A.; de Zotti, G.; Delabrouille, J.; Delouis, J.M.; Desert, F.X.; Dickinson, C.; Diego, J.M.; Dolag, K.; Dole, H.; Donzelli, S.; Dore, O.; Douspis, M.; Dupac, X.; Efstathiou, G.; Ensslin, T.A.; Eriksen, H.K.; Finelli, F.; Flores-Cacho, I.; Forni, O.; Frailis, M.; Franceschi, E.; Galeotta, S.; Ganga, K.; Genova-Santos, R.T.; Giard, M.; Giardino, G.; Giraud-Heraud, Y.; Gonzalez-Nuevo, J.; Gorski, K.M.; Gratton, S.; Gregorio, A.; Gruppuso, A.; Hansen, F.K.; Hanson, D.; Harrison, D.; Henrot-Versille, S.; Hernandez-Monteagudo, C.; Herranz, D.; Hildebrandt, S.R.; Hivon, E.; Hobson, M.; Holmes, W.A.; Hornstrup, A.; Hovest, W.; Huffenberger, K.M.; Hurier, G.; Jaffe, T.R.; Jaffe, A.H.; Jones, W.C.; Juvela, M.; Keihanen, E.; Keskitalo, R.; Kisner, T.S.; Kneissl, R.; Knoche, J.; Knox, L.; Kunz, M.; Kurki-Suonio, H.; Lacasa, F.; Lagache, G.; Lahteenmaki, A.; Lamarre, J.M.; Lasenby, A.; Laureijs, R.J.; Lawrence, C.R.; Leahy, J.P.; Leonardi, R.; Leon-Tavares, J.; Lesgourgues, J.; Liguori, M.; Lilje, P.B.; Linden-Vornle, M.; Lopez-Caniego, M.; Lubin, P.M.; Macias-Perez, J.F.; Maffei, B.; Maino, D.; Mandolesi, N.; Marcos-Caballero, A.; Maris, M.; Marshall, D.J.; Martin, P.G.; Martinez-Gonzalez, E.; Masi, S.; Matarrese, S.; Matthai, F.; Mazzotta, P.; Melchiorri, A.; Melin, J.B.; Mendes, L.; Mennella, A.; Migliaccio, M.; Mitra, S.; Miville-Deschenes, M.A.; Moneti, A.; Montier, L.; Morgante, G.; Mortlock, D.; Moss, A.; Munshi, D.; Naselsky, P.; Nati, F.; Natoli, P.; Netterfield, C.B.; Norgaard-Nielsen, H.U.; Noviello, F.; Novikov, D.; Novikov, I.; Osborne, S.; Oxborrow, C.A.; Paci, F.; Pagano, L.; Pajot, F.; Paoletti, D.; Partridge, B.; Pasian, F.; Patanchon, G.; Perdereau, O.; Perotto, L.; Perrotta, F.; Piacentini, F.; Piat, M.; Pierpaoli, E.; Pietrobon, D.; Plaszczynski, S.; Pointecouteau, E.; Polenta, G.; Ponthieu, N.; Popa, L.; Poutanen, T.; Pratt, G.W.; Prezeau, G.; Prunet, S.; Puget, J.L.; Rachen, J.P.; Rebolo, R.; Reinecke, M.; Remazeilles, M.; Renault, C.; Ricciardi, S.; Riller, T.; Ristorcelli, I.; Rocha, G.; Rosset, C.; Rossetti, M.; Roudier, G.; Rubino-Martin, J.A.; Rusholme, B.; Sandri, M.; Santos, D.; Savini, G.; Scott, D.; Seiffert, M.D.; Shellard, E.P.S.; Spencer, L.D.; Starck, J.L.; Stolyarov, V.; Stompor, R.; Sudiwala, R.; Sunyaev, R.; Sureau, F.; Sutton, D.; Suur-Uski, A.S.; Sygnet, J.F.; Tauber, J.A.; Tavagnacco, D.; Terenzi, L.; Toffolatti, L.; Tomasi, M.; Tristram, M.; Tucci, M.; Tuovinen, J.; Umana, G.; Valenziano, L.; Valiviita, J.; Van Tent, B.; Varis, J.; Vielva, P.; Villa, F.; Vittorio, N.; Wade, L.A.; Wandelt, B.D.; White, S.D.M.; Yvon, D.; Zacchei, A.; Zonca, A.

    2014-01-01

    We have constructed the first all-sky map of the thermal Sunyaev-Zeldovich (tSZ) effect by applying specifically tailored component separation algorithms to the 100 to 857 GHz frequency channel maps from the Planck survey. These maps show an obvious galaxy cluster tSZ signal that is well matched with blindly detected clusters in the Planck SZ catalogue. To characterize the signal in the tSZ map we have computed its angular power spectrum. At large angular scales ($\\ell 500$) the clustered Cosmic Infrared Background (CIB) and residual point sources are the major contaminants. These foregrounds are carefully modelled and subtracted. We measure the tSZ power spectrum in angular scales, $0.17^{\\circ} \\lesssim \\theta \\lesssim 3.0^{\\circ}$, that were previously unexplored. The measured tSZ power spectrum is consistent with that expected from the Planck catalogue of SZ sources, with additional clear evidence of signal from unresolved clusters and, potentially, diffuse warm baryons. We use the tSZ power spectrum to ...

  2. Spatial electric load forecasting

    CERN Document Server

    Willis, H Lee

    2002-01-01

    Spatial Electric Load Forecasting Consumer Demand for Power and ReliabilityCoincidence and Load BehaviorLoad Curve and End-Use ModelingWeather and Electric LoadWeather Design Criteria and Forecast NormalizationSpatial Load Growth BehaviorSpatial Forecast Accuracy and Error MeasuresTrending MethodsSimulation Method: Basic ConceptsA Detailed Look at the Simulation MethodBasics of Computerized SimulationAnalytical Building Blocks for Spatial SimulationAdvanced Elements of Computerized SimulationHybrid Trending-Simulation MethodsAdvanced

  3. Coastal and Riverine Flood Forecast Model powered by ADCIRC

    Science.gov (United States)

    Khalid, A.; Ferreira, C.

    2017-12-01

    Coastal flooding is becoming a major threat to increased population in the coastal areas. To protect coastal communities from tropical storms & hurricane damages, early warning systems are being developed. These systems have the capability of real time flood forecasting to identify hazardous coastal areas and aid coastal communities in rescue operations. State of the art hydrodynamic models forced by atmospheric forcing have given modelers the ability to forecast storm surge, water levels and currents. This helps to identify the areas threatened by intense storms. Study on Chesapeake Bay area has gained national importance because of its combined riverine and coastal phenomenon, which leads to greater uncertainty in flood predictions. This study presents an automated flood forecast system developed by following Advanced Circulation (ADCIRC) Surge Guidance System (ASGS) guidelines and tailored to take in riverine and coastal boundary forcing, thus includes all the hydrodynamic processes to forecast total water in the Potomac River. As studies on tidal and riverine flow interaction are very scarce in number, our forecast system would be a scientific tool to examine such area and fill the gaps with precise prediction for Potomac River. Real-time observations from National Oceanic and Atmospheric Administration (NOAA) and field measurements have been used as model boundary feeding. The model performance has been validated by using major historical riverine and coastal flooding events. Hydrodynamic model ADCIRC produced promising predictions for flood inundation areas. As better forecasts can be achieved by using coupled models, this system is developed to take boundary conditions from Global WaveWatchIII for the research purposes. Wave and swell propagation will be fed through Global WavewatchIII model to take into account the effects of swells and currents. This automated forecast system is currently undergoing rigorous testing to include any missing parameters which

  4. 1/f noise in music and speech. [Power spectrum studies

    Energy Technology Data Exchange (ETDEWEB)

    Voss, R.F.; Clarke, J.

    1975-11-27

    The power spectrum, S(f), of many fluctuating physical variables, V(t), is approximately ''1/f-like.'' Loudness fluctuations in music and speech and pitch (melody) fluctuations in music were found to exhibit 1/f power spectra. This observation has implications for stochastic music composition. 3 figures. (RWR)

  5. A power spectrum approach to tally convergence in Monte Carlo criticality calculation

    International Nuclear Information System (INIS)

    Ueki, Taro

    2017-01-01

    In Monte Carlo criticality calculation, confidence interval estimation is based on the central limit theorem (CLT) for a series of tallies from generations in equilibrium. A fundamental assertion resulting from CLT is the convergence in distribution (CID) of the interpolated standardized time series (ISTS) of tallies. In this work, the spectral analysis of ISTS has been conducted in order to assess the convergence of tallies in terms of CID. Numerical results obtained indicate that the power spectrum of ISTS is equal to the theoretically predicted power spectrum of Brownian motion for tallies of effective neutron multiplication factor; on the other hand, the power spectrum of ISTS of a strongly correlated series of tallies from local powers fluctuates wildly while maintaining the spectral form of fractional Brownian motion. The latter result is the evidence of a case where a series of tallies are away from CID, while the spectral form supports normality assumption on the sample mean. It is also demonstrated that one can make the unbiased estimation of the standard deviation of sample mean well before CID occurs. (author)

  6. Normalized Noise Power Spectrum of Full Field Digital Mammography System

    International Nuclear Information System (INIS)

    Isa, Norriza Mohd; Wan Hassan, Wan Muhamad Saridan

    2010-01-01

    A method to measure noise power spectrum of a full field digital mammography system is presented. The effect of X-ray radiation dose, size and configuration of region of interest on normalized noise power spectrum (NNPS) was investigated. Flat field images were acquired using RQA-M2 beam quality technique (Mo/Mo anode-filter, 28 kV, 2 mm Al) with different clinical radiation doses. The images were cropped at about 4 cm from the edge of the breast wall and then divided into different size of non-overlapping or overlapping segments. NNPS was determined through detrending, 2-D fast Fourier transformation and normalization. Our measurement shows that high radiation dose gave lower NNPS at a specific beam quality.

  7. Observational constraints on the primordial curvature power spectrum

    Science.gov (United States)

    Emami, Razieh; Smoot, George F.

    2018-01-01

    CMB temperature fluctuation observations provide a precise measurement of the primordial power spectrum on large scales, corresponding to wavenumbers 10‑3 Mpc‑1 lesssim k lesssim 0.1 Mpc‑1, [1-7, 11]. Luminous red galaxies and galaxy clusters probe the matter power spectrum on overlapping scales (0.02 Mpc‑1 lesssim k lesssim 0.7 Mpc‑1 [10, 12-20]), while the Lyman-alpha forest reaches slightly smaller scales (0.3 Mpc‑1 lesssim k lesssim 3 Mpc‑1 [22]). These observations indicate that the primordial power spectrum is nearly scale-invariant with an amplitude close to 2 × 10‑9, [5, 23-28]. These observations strongly support Inflation and motivate us to obtain observations and constraints reaching to smaller scales on the primordial curvature power spectrum and by implication on Inflation. We are able to obtain limits to much higher values of k lesssim 105 Mpc‑1 and with less sensitivity even higher k lesssim 1019‑ 1023 Mpc‑1 using limits from CMB spectral distortions and other limits on ultracompact minihalo objects (UCMHs) and Primordial Black Holes (PBHs). PBHs are one of the known candidates for the Dark Matter (DM). Due to their very early formation, they could give us valuable information about the primordial curvature perturbations. These are complementary to other cosmological bounds on the amplitude of the primordial fluctuations. In this paper, we revisit and collect all the published constraints on both PBHs and UCMHs. We show that unless one uses the CMB spectral distortion, PBHs give us a very relaxed bounds on the primordial curvature perturbations. UCMHs, on the other hand, are very informative over a reasonable k range (3 lesssim k lesssim 106 Mpc‑1) and lead to significant upper-bounds on the curvature spectrum. We review the conditions under which the tighter constraints on the UCMHs could imply extremely strong bounds on the fraction of DM that could be PBHs in reasonable models. Failure to satisfy these conditions would

  8. Application and verification of ECMWF seasonal forecast for wind energy

    Science.gov (United States)

    Žagar, Mark; Marić, Tomislav; Qvist, Martin; Gulstad, Line

    2015-04-01

    A good understanding of long-term annual energy production (AEP) is crucial when assessing the business case of investing in green energy like wind power. The art of wind-resource assessment has emerged into a scientific discipline on its own, which has advanced at high pace over the last decade. This has resulted in continuous improvement of the AEP accuracy and, therefore, increase in business case certainty. Harvesting the full potential output of a wind farm or a portfolio of wind farms depends heavily on optimizing operation and management strategy. The necessary information for short-term planning (up to 14 days) is provided by standard weather and power forecasting services, and the long-term plans are based on climatology. However, the wind-power industry is lacking quality information on intermediate scales of the expected variability in seasonal and intra-annual variations and their geographical distribution. The seasonal power forecast presented here is designed to bridge this gap. The seasonal power production forecast is based on the ECMWF seasonal weather forecast and the Vestas' high-resolution, mesoscale weather library. The seasonal weather forecast is enriched through a layer of statistical post-processing added to relate large-scale wind speed anomalies to mesoscale climatology. The resulting predicted energy production anomalies, thus, include mesoscale effects not captured by the global forecasting systems. The turbine power output is non-linearly related to the wind speed, which has important implications for the wind power forecast. In theory, the wind power is proportional to the cube of wind speed. However, due to the nature of turbine design, this exponent is close to 3 only at low wind speeds, becomes smaller as the wind speed increases, and above 11-13 m/s the power output remains constant, called the rated power. The non-linear relationship between wind speed and the power output generally increases sensitivity of the forecasted power

  9. Scaling-law for the energy dependence of anatomic power spectrum in dedicated breast CT

    Energy Technology Data Exchange (ETDEWEB)

    Vedantham, Srinivasan; Shi, Linxi; Glick, Stephen J.; Karellas, Andrew [Department of Radiology, University of Massachusetts Medical School, Worcester, Massachusetts 01655 (United States)

    2013-01-15

    Purpose: To determine the x-ray photon energy dependence of the anatomic power spectrum of the breast when imaged with dedicated breast computed tomography (CT). Methods: A theoretical framework for scaling the empirically determined anatomic power spectrum at one x-ray photon energy to that at any given x-ray photon energy when imaged with dedicated breast CT was developed. Theory predicted that when the anatomic power spectrum is fitted with a power curve of the form k f{sup -{beta}}, where k and {beta} are fit coefficients and f is spatial frequency, the exponent {beta} would be independent of x-ray photon energy (E), and the amplitude k scales with the square of the difference in energy-dependent linear attenuation coefficients of fibroglandular and adipose tissues. Twenty mastectomy specimens based numerical phantoms that were previously imaged with a benchtop flat-panel cone-beam CT system were converted to 3D distribution of glandular weight fraction (f{sub g}) and were used to verify the theoretical findings. The 3D power spectrum was computed in terms of f{sub g} and after converting to linear attenuation coefficients at monoenergetic x-ray photon energies of 20-80 keV in 5 keV intervals. The 1D power spectra along the axes were extracted and fitted with a power curve of the form k f{sup -{beta}}. The energy dependence of k and {beta} were analyzed. Results: For the 20 mastectomy specimen based numerical phantoms used in the study, the exponent {beta} was found to be in the range of 2.34-2.42, depending on the axis of measurement. Numerical simulations agreed with the theoretical predictions that for a power-law anatomic spectrum of the form k f{sup -{beta}}, {beta} was independent of E and k(E) =k{sub 1}[{mu}{sub g}(E) -{mu}{sub a}(E)]{sup 2}, where k{sub 1} is a constant, and {mu}{sub g}(E) and {mu}{sub a}(E) represent the energy-dependent linear attenuation coefficients of fibroglandular and adipose tissues, respectively. Conclusions: Numerical

  10. The Coyote Universe II: Cosmological Models and Precision Emulation of the Nonlinear Matter Power Spectrum

    Energy Technology Data Exchange (ETDEWEB)

    Heitmann, Katrin [Los Alamos National Laboratory; Habib, Salman [Los Alamos National Laboratory; Higdon, David [Los Alamos National Laboratory; Williams, Brian J [Los Alamos National Laboratory; White, Martin [Los Alamos National Laboratory; Wagner, Christian [Los Alamos National Laboratory

    2008-01-01

    The power spectrum of density fluctuations is a foundational source of cosmological information. Precision cosmological probes targeted primarily at investigations of dark energy require accurate theoretical determinations of the power spectrum in the nonlinear regime. To exploit the observational power of future cosmological surveys, accuracy demands on the theory are at the one percent level or better. Numerical simulations are currently the only way to produce sufficiently error-controlled predictions for the power spectrum. The very high computational cost of (precision) N-body simulations is a major obstacle to obtaining predictions in the nonlinear regime, while scanning over cosmological parameters. Near-future observations, however, are likely to provide a meaningful constraint only on constant dark energy equation of state 'wCDM' cosmologies. In this paper we demonstrate that a limited set of only 37 cosmological models -- the 'Coyote Universe' suite -- can be used to predict the nonlinear matter power spectrum at the required accuracy over a prior parameter range set by cosmic microwave background observations. This paper is the second in a series of three, with the final aim to provide a high-accuracy prediction scheme for the nonlinear matter power spectrum for wCDM cosmologies.

  11. Power spectrum of an injection-locked Josephson oscillator

    International Nuclear Information System (INIS)

    Stancampiano, C.V.; Shapiro, S.

    1975-01-01

    Experiments have shown that a Josephson oscillator, exposed to a weak narrow-band input signal, exhibits behavior characteristic of an injection-locked oscillator. When in lock, Adler's theory of injection locking describes the experimental observations reasonably well. The range of applicability of the theory is extended to the out-of-lock regime where a spectrum of output frequencies is observed. Obtaining the theoretical output power spectrum requires solving a differential equation having the same form as the equation describing the resistively shunted junction model of Stewart and of McCumber. Experimental measurements of the output spectrum of a nearly locked Josephson oscillator are shown to be in reasonable agreement with the theory. Additional results discussed briefly include the observation of a frequency dependence of the locked Josephson oscillator output and experiments in which a Josephson oscillator-mixer was injection locked by a weak signal at the rf

  12. A spectrum of power plant simulators for effective training

    International Nuclear Information System (INIS)

    Foulke, L.R.

    1987-01-01

    This paper discusses the subject of training simulator fidelity and describes a spectrum of fidelity levels of power plant simulators to optimize training effectiveness. The body of knowledge about the relationship between power plant simulator fidelity and training effectiveness is reviewed, and a number of conjectures about this relationship are made based on the perspective of over 20 simulator-years of experience in training nuclear power plant operators. Developments are described for a new class of emerging simulator which utilize high resolution graphics to emphasize the visualization step of effective training

  13. Estimates of Uncertainty around the RBA's Forecasts

    OpenAIRE

    Peter Tulip; Stephanie Wallace

    2012-01-01

    We use past forecast errors to construct confidence intervals and other estimates of uncertainty around the Reserve Bank of Australia's forecasts of key macroeconomic variables. Our estimates suggest that uncertainty about forecasts is high. We find that the RBA's forecasts have substantial explanatory power for the inflation rate but not for GDP growth.

  14. Wind power forecasting: IEA Wind Task 36 & future research issues

    DEFF Research Database (Denmark)

    Giebel, Gregor; Cline, J.; Frank, Helmut Paul

    2016-01-01

    the weather prediction side and from the usage of the forecasts. The new International Energy Agency (IEA) Task on Forecasting for Wind Energy tries to organise international collaboration, among national meteorological centres with an interest and/or large projects on wind forecast improvements (NOAA, DWD...

  15. Reduction of wind power induced reserve requirements by advanced shortest-term forecasts and prediction intervals

    Energy Technology Data Exchange (ETDEWEB)

    Dobschinski, Jan; Wessel, Arne; Lange, Bernhard; Bremen, Lueder von [Fraunhofer Institut fuer Windenergie und Energiesystemtechnik (IWES), Kassel (Germany)

    2009-07-01

    In electricity systems with large penetration of wind power, the limited predictability of the wind power generation leads to an increase in reserve and balancing requirements. At first the present study concentrates on the capability of dynamic day-ahead prediction intervals to reduce the wind power induced reserve and balancing requirements. Alternatively the reduction of large forecast errors of the German wind power generation by using advanced shortest-term predictions has been evaluated in a second approach. With focus on the allocation of minute reserve power the aim is to estimate the maximal remaining uncertainty after trading activities on the intraday market. Finally both approaches were used in a case study concerning the reserve requirements induced by the total German wind power expansion in 2007. (orig.)

  16. A methodology based on dynamic artificial neural network for short-term forecasting of the power output of a PV generator

    International Nuclear Information System (INIS)

    Almonacid, F.; Pérez-Higueras, P.J.; Fernández, Eduardo F.; Hontoria, L.

    2014-01-01

    Highlights: • The output of the majority of renewables energies depends on the variability of the weather conditions. • The short-term forecast is going to be essential for effectively integrating solar energy sources. • A new method based on artificial neural network to predict the power output of a PV generator one hour ahead is proposed. • This new method is based on dynamic artificial neural network to predict global solar irradiance and the air temperature. • The methodology developed can be used to estimate the power output of a PV generator with a satisfactory margin of error. - Abstract: One of the problems of some renewables energies is that the output of these kinds of systems is non-dispatchable depending on variability of weather conditions that cannot be predicted and controlled. From this point of view, the short-term forecast is going to be essential for effectively integrating solar energy sources, being a very useful tool for the reliability and stability of the grid ensuring that an adequate supply is present. In this paper a new methodology for forecasting the output of a PV generator one hour ahead based on dynamic artificial neural network is presented. The results of this study show that the proposed methodology could be used to forecast the power output of PV systems one hour ahead with an acceptable degree of accuracy

  17. LOAD FORECASTING FOR POWER SYSTEM PLANNING AND OPERATION USING ARTIFICIAL NEURAL NETWORK AT AL BATINAH REGION OMAN

    Directory of Open Access Journals (Sweden)

    HUSSEIN A. ABDULQADER

    2012-08-01

    Full Text Available Load forecasting is essential part for the power system planning and operation. In this paper the modeling and design of artificial neural network for load forecasting is carried out in a particular region of Oman. Neural network approach helps to reduce the problem associated with conventional method and has the advantage of learning directly from the historical data. The neural network here uses data such as past load; weather information like humidity and temperatures. Once the neural network is trained for the past set of data it can give a prediction of future load. This reduces the capital investment reducing the equipments to be installed. The actual data are taken from the Mazoon Electrical Company, Oman. The data of load for the year 2007, 2008 and 2009 are collected for a particular region called Al Batinah in Oman and trained using neural networks to forecast the future. The main objective is to forecast the amount of electricity needed for better load distribution in the areas of this region in Oman. The load forecasting is done for the year 2010 and is validated for the accuracy.

  18. Normalized noise power spectrum of full field digital mammography system

    International Nuclear Information System (INIS)

    Norriza Mohd Isa; Wan Muhamad Saridan Wan Hassan

    2009-01-01

    A method to measure noise power spectrum of a full field digital mammography system is presented. The effect of X-ray radiation dose, size and configuration of region of interest on normalized noise power spectrum (NNPS) was investigated. Flat field images were acquired using RQA-M2 beam quality technique (Mo/Mo anode-filter, 28 kV, 2 mm Al) with different clinical radiation doses. The images were cropped at about 4 cm from the edge of the breast wall and then divided into different size of non-overlapping or overlapping segments. NNPS was determined through detrending, 2-D fast Fourier transformation and normalization. Our measurement shows that high radiation dose gave lower NNPS at a specific beam quality. (Author)

  19. The 3D Power Spectrum from Angular Clustering of Galaxies in Early SDSS Data

    CERN Document Server

    Dodelson, Scott; Tegmark, Max; Scranton, Ryan; Budavari, Tamas; Connolly, Andrew; Csabai, Istvan; Eisenstein, Daniel; Frieman, Joshua A.; Gunn, James E.; Hui, Lam; Jain, Bhuvnesh; Johnston, David; Kent, Stephen M.; Loveday, Jon; Nichol, Robert C.; O'Connell, Liam; Scoccimarro, Roman; Sheth, Ravi K.; Stebbins, Albert; Strauss, Michael A.; Szalay, Alexander S.; Szapudi, Istvan; Vogeley, Michael S.; Zehavi, Idit; Annis, James; Bahcall, Neta A.; Brinkman, Jon; Doi, Mamoru; Fukugita, Masataka; Hennessy, Greg; Ivezic, Zeljko; Knapp, Gillian R.; Kunszt, Peter; Lamb, Don Q.; Lee, Brian C.; Lupton, Robert H.; Munn, Jeffrey A.; Peoples, John; Pier, Jeffrey R.; Rockosi, Constance; Schlegel, David; Stoughton, Christopher; Tucker, Douglas L.; Yanny, Brian; York, Donald G.; Dodelson, Scott; Narayanan, Vijay K.; Tegmark, Max; Scranton, Ryan; Budavari, Tamas; Connolly, Andrew; Csabai, Istvan; Eisenstein, Daniel; Frieman, Joshua A.; Gunn, James E.; Hui, Lam; Jain, Bhuvnesh; Johnston, David; Kent, Stephen; Loveday, Jon; Nichol, Robert C.; Connell, Liam O'; Scoccimarro, Roman; Sheth, Ravi K.; Stebbins, Albert; Strauss, Michael A.; Szalay, Alexander S.; Szapudi, Istv\\'an; Vogeley, Michael S.; Zehavi, Idit

    2001-01-01

    Early photometric data from the Sloan Digital Sky Survey (SDSS) contain angular positions for 1.5 million galaxies. In companion papers, the angular correlation function $w(\\theta)$ and 2D power spectrum $C_l$ of these galaxies are presented. Here we invert Limber's equation to extract the 3D power spectrum from the angular results. We accomplish this using an estimate of $dn/dz$, the redshift distribution of galaxies in four different magnitude slices in the SDSS photometric catalog. The resulting 3D power spectrum estimates from $w(\\theta)$ and $C_l$ agree with each other and with previous estimates over a range in wavenumbers $0.03 < k/{\\rm h Mpc}^{-1} < 1$. The galaxies in the faintest magnitude bin ($21 < \\rstar < 22$, which have median redshift $z_m=0.43$) are less clustered than the galaxies in the brightest magnitude bin ($18 < \\rstar < 19$ with $z_m=0.17$), especially on scales where nonlinearities are important. The derived power spectrum agrees with that of Szalay et al. (2001) wh...

  20. A New Strategy for Short-Term Load Forecasting

    Directory of Open Access Journals (Sweden)

    Yi Yang

    2013-01-01

    Full Text Available Electricity is a special energy which is hard to store, so the electricity demand forecasting remains an important problem. Accurate short-term load forecasting (STLF plays a vital role in power systems because it is the essential part of power system planning and operation, and it is also fundamental in many applications. Considering that an individual forecasting model usually cannot work very well for STLF, a hybrid model based on the seasonal ARIMA model and BP neural network is presented in this paper to improve the forecasting accuracy. Firstly the seasonal ARIMA model is adopted to forecast the electric load demand day ahead; then, by using the residual load demand series obtained in this forecasting process as the original series, the follow-up residual series is forecasted by BP neural network; finally, by summing up the forecasted residual series and the forecasted load demand series got by seasonal ARIMA model, the final load demand forecasting series is obtained. Case studies show that the new strategy is quite useful to improve the accuracy of STLF.

  1. Short-Term Wind Speed Forecasting for Power System Operations

    KAUST Repository

    Zhu, Xinxin; Genton, Marc G.

    2012-01-01

    some statistical short-term wind speed forecasting models, including traditional time series approaches and more advanced space-time statistical models. It also discusses the evaluation of forecast accuracy, in particular, the need for realistic loss

  2. Using forecast information for storm ride-through control

    DEFF Research Database (Denmark)

    Barahona Garzón, Braulio; Trombe, Pierre-Julien; Vincent, Claire Louise

    2013-01-01

    Using probabilistic forecast information in control algorithms can improve the performance of wind farms during periods of extreme winds. This work presents a wind farm supervisor control concept that uses probabilistic forecast information to ride-through a storm with softer ramps of power. Wind...... speed forecasts are generated with a statistical approach (i.e. time series models). The supervisor control is based on a set of logical rules that consider point forecasts and predictive densities to ramp-down the power of the wind farm before the storm hits. The potential of this supervisor control...

  3. How to estimate the 3D power spectrum of the Lyman-α forest

    Science.gov (United States)

    Font-Ribera, Andreu; McDonald, Patrick; Slosar, Anže

    2018-01-01

    We derive and numerically implement an algorithm for estimating the 3D power spectrum of the Lyman-α (Lyα) forest flux fluctuations. The algorithm exploits the unique geometry of Lyα forest data to efficiently measure the cross-spectrum between lines of sight as a function of parallel wavenumber, transverse separation and redshift. We start by approximating the global covariance matrix as block-diagonal, where only pixels from the same spectrum are correlated. We then compute the eigenvectors of the derivative of the signal covariance with respect to cross-spectrum parameters, and project the inverse-covariance-weighted spectra onto them. This acts much like a radial Fourier transform over redshift windows. The resulting cross-spectrum inference is then converted into our final product, an approximation of the likelihood for the 3D power spectrum expressed as second order Taylor expansion around a fiducial model. We demonstrate the accuracy and scalability of the algorithm and comment on possible extensions. Our algorithm will allow efficient analysis of the upcoming Dark Energy Spectroscopic Instrument dataset.

  4. Global Energy Forecasting Competition 2012

    DEFF Research Database (Denmark)

    Hong, Tao; Pinson, Pierre; Fan, Shu

    2014-01-01

    The Global Energy Forecasting Competition (GEFCom2012) attracted hundreds of participants worldwide, who contributed many novel ideas to the energy forecasting field. This paper introduces both tracks of GEFCom2012, hierarchical load forecasting and wind power forecasting, with details...... on the aspects of the problem, the data, and a summary of the methods used by selected top entries. We also discuss the lessons learned from this competition from the organizers’ perspective. The complete data set, including the solution data, is published along with this paper, in an effort to establish...

  5. International Workshop on Industry Practices for Forecasting

    CERN Document Server

    Poggi, Jean-Michel; Brossat, Xavier

    2015-01-01

    The chapters in this volume stress the need for advances in theoretical understanding to go hand-in-hand with the widespread practical application of forecasting in industry. Forecasting and time series prediction have enjoyed considerable attention over the last few decades, fostered by impressive advances in observational capabilities and measurement procedures. On June 5-7, 2013, an international Workshop on Industry Practices for FORecasting was held in Paris, France, organized and supported by the OSIRIS Department of Electricité de France Research and Development Division. In keeping with tradition, both theoretical statistical results and practical contributions on this active field of statistical research and on forecasting issues in a rapidly evolving industrial environment are presented. The volume reflects the broad spectrum of the conference, including 16 articles contributed by specialists in various areas. The material compiled is broad in scope and ranges from new findings on forecasting in in...

  6. A New Neural Network Approach to Short Term Load Forecasting of Electrical Power Systems

    Directory of Open Access Journals (Sweden)

    Farshid Keynia

    2011-03-01

    Full Text Available Short-term load forecast (STLF is an important operational function in both regulated power systems and deregulated open electricity markets. However, STLF is not easy to handle due to the nonlinear and random-like behaviors of system loads, weather conditions, and social and economic environment variations. Despite the research work performed in the area, more accurate and robust STLF methods are still needed due to the importance and complexity of STLF. In this paper, a new neural network approach for STLF is proposed. The proposed neural network has a novel learning algorithm based on a new modified harmony search technique. This learning algorithm can widely search the solution space in various directions, and it can also avoid the overfitting problem, trapping in local minima and dead bands. Based on this learning algorithm, the suggested neural network can efficiently extract the input/output mapping function of the forecast process leading to high STLF accuracy. The proposed approach is tested on two practical power systems and the results obtained are compared with the results of several other recently published STLF methods. These comparisons confirm the validity of the developed approach.

  7. Enhanced regional forecasting considering single wind farm distribution for upscaling

    International Nuclear Information System (INIS)

    Bremen, Lueder von; Saleck, Nadja; Heinemann, Detlev

    2007-01-01

    With increasing wind power penetration the need for more accurate wind power forecasts increases to raise the market value of wind power. State-of-the-art wind power forecasting tools are considered either statistical or physical. Fundamentally new techniques are rare, thus it is tried to establish a new approach. The spatial decomposition of wind power generation in Germany can be done with principle component analysis to extract the main pattern of variability. They have a physical meaning when linked with typical weather situation. The first four eigenvectors explain about 94 % of the observed variance. The time-evolving principle components are linked with the total wind power feed-in in Germany and are used for its estimation. A new wind power forecasting model has been implemented with this approach and shows very good results that are comparable with state-of-the-art commercial wind power forecast models. The day-ahead forecast error for a common intercomparison period Jan-Jul 2006 is 4.4 %. The suggested approach offers wide ranges for future developments (e.g. several NWP models), because it is computationally very cheap to run

  8. On the dynamics of the power spectrum during lower hybrid current drive in Tokamaks

    International Nuclear Information System (INIS)

    Bizarro, J.P.

    1993-01-01

    An investigation is provided on the propagation and absorption of the power spectrum during lower hybrid current drive in Tokamaks. A combined ray tracing and Fokker-Planck code is utilized and stochastic effects induced by toroidicity are correctly taken into account by using a large number of rays. It is shown that when strong wave damping prevails the absorbed spectrum is very similar in shape to the launched one, although some broadening and shifting in parallel wave index generally occur, and power deposition is localized. If the wave damping is weak and stochastic effects are important, rays end up sweeping the entire plasma cross-section, power deposition turns out to be extended, and the absorbed spectrum is much broader than the launched one

  9. Predicting Power Outages Using Multi-Model Ensemble Forecasts

    Science.gov (United States)

    Cerrai, D.; Anagnostou, E. N.; Yang, J.; Astitha, M.

    2017-12-01

    Power outages affect every year millions of people in the United States, affecting the economy and conditioning the everyday life. An Outage Prediction Model (OPM) has been developed at the University of Connecticut for helping utilities to quickly restore outages and to limit their adverse consequences on the population. The OPM, operational since 2015, combines several non-parametric machine learning (ML) models that use historical weather storm simulations and high-resolution weather forecasts, satellite remote sensing data, and infrastructure and land cover data to predict the number and spatial distribution of power outages. A new methodology, developed for improving the outage model performances by combining weather- and soil-related variables using three different weather models (WRF 3.7, WRF 3.8 and RAMS/ICLAMS), will be presented in this study. First, we will present a performance evaluation of each model variable, by comparing historical weather analyses with station data or reanalysis over the entire storm data set. Hence, each variable of the new outage model version is extracted from the best performing weather model for that variable, and sensitivity tests are performed for investigating the most efficient variable combination for outage prediction purposes. Despite that the final variables combination is extracted from different weather models, this ensemble based on multi-weather forcing and multi-statistical model power outage prediction outperforms the currently operational OPM version that is based on a single weather forcing variable (WRF 3.7), because each model component is the closest to the actual atmospheric state.

  10. Swarm Intelligence-Based Hybrid Models for Short-Term Power Load Prediction

    Directory of Open Access Journals (Sweden)

    Jianzhou Wang

    2014-01-01

    Full Text Available Swarm intelligence (SI is widely and successfully applied in the engineering field to solve practical optimization problems because various hybrid models, which are based on the SI algorithm and statistical models, are developed to further improve the predictive abilities. In this paper, hybrid intelligent forecasting models based on the cuckoo search (CS as well as the singular spectrum analysis (SSA, time series, and machine learning methods are proposed to conduct short-term power load prediction. The forecasting performance of the proposed models is augmented by a rolling multistep strategy over the prediction horizon. The test results are representative of the out-performance of the SSA and CS in tuning the seasonal autoregressive integrated moving average (SARIMA and support vector regression (SVR in improving load forecasting, which indicates that both the SSA-based data denoising and SI-based intelligent optimization strategy can effectively improve the model’s predictive performance. Additionally, the proposed CS-SSA-SARIMA and CS-SSA-SVR models provide very impressive forecasting results, demonstrating their strong robustness and universal forecasting capacities in terms of short-term power load prediction 24 hours in advance.

  11. Spatial load forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Willis, H.L.; Engel, M.V.; Buri, M.J.

    1995-04-01

    The reliability, efficiency, and economy of a power delivery system depend mainly on how well its substations, transmission lines, and distribution feeders are located within the utility service area, and how well their capacities match power needs in their respective localities. Often, utility planners are forced to commit to sites, rights of way, and equipment capacities year in advance. A necessary element of effective expansion planning is a forecast of where and how much demand must be served by the future T and D system. This article reports that a three-stage method forecasts with accuracy and detail, allowing meaningful determination of sties and sizes for future substation, transmission, and distribution facilities.

  12. Halo Pressure Profile through the Skew Cross-power Spectrum of the Sunyaev–Zel’dovich Effect and CMB Lensing in Planck

    Energy Technology Data Exchange (ETDEWEB)

    Timmons, Nicholas; Cooray, Asantha; Feng, Chang [Department of Physics and Astronomy, University of California, Irvine, CA 92697 (United States); Keating, Brian [Department of Physics, University of California, San Diego, La Jolla, CA 92093 (United States)

    2017-11-01

    We measure the cosmic microwave background (CMB) skewness power spectrum in Planck , using frequency maps of the HFI instrument and the Sunyaev–Zel’dovich (SZ) component map. The two-to-one skewness power spectrum measures the cross-correlation between CMB lensing and the thermal SZ effect. We also directly measure the same cross-correlation using the Planck CMB lensing map and the SZ map and compare it to the cross-correlation derived from the skewness power spectrum. We model fit the SZ power spectrum and CMB lensing–SZ cross-power spectrum via the skewness power spectrum to constrain the gas pressure profile of dark matter halos. The gas pressure profile is compared to existing measurements in the literature including a direct estimate based on the stacking of SZ clusters in Planck .

  13. Short-term forecasting model for aggregated regional hydropower generation

    International Nuclear Information System (INIS)

    Monteiro, Claudio; Ramirez-Rosado, Ignacio J.; Fernandez-Jimenez, L. Alfredo

    2014-01-01

    Highlights: • Original short-term forecasting model for the hourly hydropower generation. • The use of NWP forecasts allows horizons of several days. • New variable to represent the capacity level for generating hydroelectric energy. • The proposed model significantly outperforms the persistence model. - Abstract: This paper presents an original short-term forecasting model of the hourly electric power production for aggregated regional hydropower generation. The inputs of the model are previously recorded values of the aggregated hourly production of hydropower plants and hourly water precipitation forecasts using Numerical Weather Prediction tools, as well as other hourly data (load demand and wind generation). This model is composed of three modules: the first one gives the prediction of the “monthly” hourly power production of the hydropower plants; the second module gives the prediction of hourly power deviation values, which are added to that obtained by the first module to achieve the final forecast of the hourly hydropower generation; the third module allows a periodic adjustment of the prediction of the first module to improve its BIAS error. The model has been applied successfully to the real-life case study of the short-term forecasting of the aggregated hydropower generation in Spain and Portugal (Iberian Peninsula Power System), achieving satisfactory results for the next-day forecasts. The model can be valuable for agents involved in electricity markets and useful for power system operations

  14. Maximal compression of the redshift-space galaxy power spectrum and bispectrum

    Science.gov (United States)

    Gualdi, Davide; Manera, Marc; Joachimi, Benjamin; Lahav, Ofer

    2018-05-01

    We explore two methods of compressing the redshift-space galaxy power spectrum and bispectrum with respect to a chosen set of cosmological parameters. Both methods involve reducing the dimension of the original data vector (e.g. 1000 elements) to the number of cosmological parameters considered (e.g. seven ) using the Karhunen-Loève algorithm. In the first case, we run MCMC sampling on the compressed data vector in order to recover the 1D and 2D posterior distributions. The second option, approximately 2000 times faster, works by orthogonalizing the parameter space through diagonalization of the Fisher information matrix before the compression, obtaining the posterior distributions without the need of MCMC sampling. Using these methods for future spectroscopic redshift surveys like DESI, Euclid, and PFS would drastically reduce the number of simulations needed to compute accurate covariance matrices with minimal loss of constraining power. We consider a redshift bin of a DESI-like experiment. Using the power spectrum combined with the bispectrum as a data vector, both compression methods on average recover the 68 {per cent} credible regions to within 0.7 {per cent} and 2 {per cent} of those resulting from standard MCMC sampling, respectively. These confidence intervals are also smaller than the ones obtained using only the power spectrum by 81 per cent, 80 per cent, and 82 per cent respectively, for the bias parameter b1, the growth rate f, and the scalar amplitude parameter As.

  15. A Novel Nonlinear Combined Forecasting System for Short-Term Load Forecasting

    Directory of Open Access Journals (Sweden)

    Chengshi Tian

    2018-03-01

    Full Text Available Short-term load forecasting plays an indispensable role in electric power systems, which is not only an extremely challenging task but also a concerning issue for all society due to complex nonlinearity characteristics. However, most previous combined forecasting models were based on optimizing weight coefficients to develop a linear combined forecasting model, while ignoring that the linear combined model only considers the contribution of the linear terms to improving the model’s performance, which will lead to poor forecasting results because of the significance of the neglected and potential nonlinear terms. In this paper, a novel nonlinear combined forecasting system, which consists of three modules (improved data pre-processing module, forecasting module and the evaluation module is developed for short-term load forecasting. Different from the simple data pre-processing of most previous studies, the improved data pre-processing module based on longitudinal data selection is successfully developed in this system, which further improves the effectiveness of data pre-processing and then enhances the final forecasting performance. Furthermore, the modified support vector machine is developed to integrate all the individual predictors and obtain the final prediction, which successfully overcomes the upper drawbacks of the linear combined model. Moreover, the evaluation module is incorporated to perform a scientific evaluation for the developed system. The half-hourly electrical load data from New South Wales are employed to verify the effectiveness of the developed forecasting system, and the results reveal that the developed nonlinear forecasting system can be employed in the dispatching and planning for smart grids.

  16. Variability of the Magnetic Field Power Spectrum in the Solar Wind at Electron Scales

    Science.gov (United States)

    Roberts, Owen Wyn; Alexandrova, O.; Kajdič, P.; Turc, L.; Perrone, D.; Escoubet, C. P.; Walsh, A.

    2017-12-01

    At electron scales, the power spectrum of solar-wind magnetic fluctuations can be highly variable and the dissipation mechanisms of the magnetic energy into the various particle species is under debate. In this paper, we investigate data from the Cluster mission’s STAFF Search Coil magnetometer when the level of turbulence is sufficiently high that the morphology of the power spectrum at electron scales can be investigated. The Cluster spacecraft sample a disturbed interval of plasma where two streams of solar wind interact. Meanwhile, several discontinuities (coherent structures) are seen in the large-scale magnetic field, while at small scales several intermittent bursts of wave activity (whistler waves) are present. Several different morphologies of the power spectrum can be identified: (1) two power laws separated by a break, (2) an exponential cutoff near the Taylor shifted electron scales, and (3) strong spectral knees at the Taylor shifted electron scales. These different morphologies are investigated by using wavelet coherence, showing that, in this interval, a clear break and strong spectral knees are features that are associated with sporadic quasi parallel propagating whistler waves, even for short times. On the other hand, when no signatures of whistler waves at ∼ 0.1{--}0.2{f}{ce} are present, a clear break is difficult to find and the spectrum is often more characteristic of a power law with an exponential cutoff.

  17. Limited Area Forecasting and Statistical Modelling for Wind Energy Scheduling

    DEFF Research Database (Denmark)

    Rosgaard, Martin Haubjerg

    forecast accuracy for operational wind power scheduling. Numerical weather prediction history and scales of atmospheric motion are summarised, followed by a literature review of limited area wind speed forecasting. Hereafter, the original contribution to research on the topic is outlined. The quality...... control of wind farm data used as forecast reference is described in detail, and a preliminary limited area forecasting study illustrates the aggravation of issues related to numerical orography representation and accurate reference coordinates at ne weather model resolutions. For the o shore and coastal...... sites studied limited area forecasting is found to deteriorate wind speed prediction accuracy, while inland results exhibit a steady forecast performance increase with weather model resolution. Temporal smoothing of wind speed forecasts is shown to improve wind power forecast performance by up to almost...

  18. Spatial and Temporal Wind Power Forecasting by Case-Based Reasoning Using Big-Data

    Directory of Open Access Journals (Sweden)

    Fabrizio De Caro

    2017-02-01

    Full Text Available The massive penetration of wind generators in electrical power systems asks for effective wind power forecasting tools, which should be high reliable, in order to mitigate the effects of the uncertain generation profiles, and fast enough to enhance power system operation. To address these two conflicting objectives, this paper advocates the role of knowledge discovery from big-data, by proposing the integration of adaptive Case Based Reasoning models, and cardinality reduction techniques based on Partial Least Squares Regression, and Principal Component Analysis. The main idea is to learn from a large database of historical climatic observations, how to solve the windforecasting problem, avoiding complex and time-consuming computations. To assess the benefits derived by the application of the proposed methodology in complex application scenarios, the experimental results obtained in a real case study will be presented and discussed.

  19. Wind Energy Management System Integration Project Incorporating Wind Generation and Load Forecast Uncertainties into Power Grid Operations

    Energy Technology Data Exchange (ETDEWEB)

    Makarov, Yuri V.; Huang, Zhenyu; Etingov, Pavel V.; Ma, Jian; Guttromson, Ross T.; Subbarao, Krishnappa; Chakrabarti, Bhujanga B.

    2010-09-01

    The power system balancing process, which includes the scheduling, real time dispatch (load following) and regulation processes, is traditionally based on deterministic models. Since the conventional generation needs time to be committed and dispatched to a desired megawatt level, the scheduling and load following processes use load and wind power production forecasts to achieve future balance between the conventional generation and energy storage on the one side, and system load, intermittent resources (such as wind and solar generation) and scheduled interchange on the other side. Although in real life the forecasting procedures imply some uncertainty around the load and wind forecasts (caused by forecast errors), only their mean values are actually used in the generation dispatch and commitment procedures. Since the actual load and intermittent generation can deviate from their forecasts, it becomes increasingly unclear (especially, with the increasing penetration of renewable resources) whether the system would be actually able to meet the conventional generation requirements within the look-ahead horizon, what the additional balancing efforts would be needed as we get closer to the real time, and what additional costs would be incurred by those needs. In order to improve the system control performance characteristics, maintain system reliability, and minimize expenses related to the system balancing functions, it becomes necessary to incorporate the predicted uncertainty ranges into the scheduling, load following, and, in some extent, into the regulation processes. It is also important to address the uncertainty problem comprehensively, by including all sources of uncertainty (load, intermittent generation, generators’ forced outages, etc.) into consideration. All aspects of uncertainty such as the imbalance size (which is the same as capacity needed to mitigate the imbalance) and generation ramping requirement must be taken into account. The latter unique

  20. Impact of forecast errors on expansion planning of power systems with a renewables target

    DEFF Research Database (Denmark)

    Pineda, Salvador; Morales González, Juan Miguel; Boomsma, Trine Krogh

    2015-01-01

    This paper analyzes the impact of production forecast errors on the expansion planning of a power system and investigates the influence of market design to facilitate the integration of renewable generation. For this purpose, we propose a programming modeling framework to determine the generation...... and transmission expansion plan that minimizes system-wide investment and operating costs, while ensuring a given share of renewable generation in the electricity supply. Unlike existing ones, this framework includes both a day-ahead and a balancing market so as to capture the impact of both production forecasts...... and the associated prediction errors. Within this framework, we consider two paradigmatic market designs that essentially differ in whether the day-ahead generation schedule and the subsequent balancing re-dispatch are co-optimized or not. The main features and results of the model set-ups are discussed using...

  1. Investigations on the relationship between power spectrum and signal-to-noise ratio of frequency-swept pulses

    International Nuclear Information System (INIS)

    Zhang Zhuhong; Fan Diayuan

    1993-01-01

    The criterion for obtaining compressed chirp pulses with high signal-to-noise ratio is the shape of the power spectrum, a chirp pulse of Gaussian shaped power spectrum without modulation is needed in CPA system to get the clean compressed pulses. 4 refs., 2 figs

  2. A Comparison of the Performance of Advanced Statistical Techniques for the Refinement of Day-ahead and Longer NWP-based Wind Power Forecasts

    Science.gov (United States)

    Zack, J. W.

    2015-12-01

    Predictions from Numerical Weather Prediction (NWP) models are the foundation for wind power forecasts for day-ahead and longer forecast horizons. The NWP models directly produce three-dimensional wind forecasts on their respective computational grids. These can be interpolated to the location and time of interest. However, these direct predictions typically contain significant systematic errors ("biases"). This is due to a variety of factors including the limited space-time resolution of the NWP models and shortcomings in the model's representation of physical processes. It has become common practice to attempt to improve the raw NWP forecasts by statistically adjusting them through a procedure that is widely known as Model Output Statistics (MOS). The challenge is to identify complex patterns of systematic errors and then use this knowledge to adjust the NWP predictions. The MOS-based improvements are the basis for much of the value added by commercial wind power forecast providers. There are an enormous number of statistical approaches that can be used to generate the MOS adjustments to the raw NWP forecasts. In order to obtain insight into the potential value of some of the newer and more sophisticated statistical techniques often referred to as "machine learning methods" a MOS-method comparison experiment has been performed for wind power generation facilities in 6 wind resource areas of California. The underlying NWP models that provided the raw forecasts were the two primary operational models of the US National Weather Service: the GFS and NAM models. The focus was on 1- and 2-day ahead forecasts of the hourly wind-based generation. The statistical methods evaluated included: (1) screening multiple linear regression, which served as a baseline method, (2) artificial neural networks, (3) a decision-tree approach called random forests, (4) gradient boosted regression based upon an decision-tree algorithm, (5) support vector regression and (6) analog ensemble

  3. The Atacama Cosmology Telescope: temperature and gravitational lensing power spectrum measurements from three seasons of data

    Energy Technology Data Exchange (ETDEWEB)

    Das, Sudeep [Argonne National Laboratory, 9700 S. Cass Ave., Lemont, IL 60439 (United States); Louis, Thibaut; Calabrese, Erminia; Dunkley, Joanna [Sub-department of Astrophysics, University of Oxford, Keble Road, Oxford, OX1 3RH (United Kingdom); Nolta, Michael R.; Bond, J Richard; Hajian, Amir; Hincks, Adam D. [Canadian Institute for Theoretical Astrophysics, University of Toronto, Toronto, ON, M5S 3H8 Canada (Canada); Addison, Graeme E.; Halpern, Mark [Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, V6T 1Z4 Canada (Canada); Battistelli, Elia S. [Department of Physics, University of Rome ' ' La Sapienza' ' , Piazzale Aldo Moro 5, I-00185 Rome (Italy); Crichton, Devin; Gralla, Megan [Dept. of Physics and Astronomy, The Johns Hopkins University, 3400 N. Charles St., Baltimore, MD 21218-2686 (United States); Devlin, Mark J.; Dicker, Simon [Department of Physics and Astronomy, University of Pennsylvania, 209 South 33rd Street, Philadelphia, PA, 19104 (United States); Dünner, Rolando [Departamento de Astronomía y Astrofísica, Facultad de Física, Pontificía Universidad Católica, Casilla 306, Santiago 22 (Chile); Fowler, Joseph W. [NIST Quantum Devices Group, 325 Broadway Mailcode 817.03, Boulder, CO, 80305 (United States); Hasselfield, Matthew; Hlozek, Renée [Department of Astrophysical Sciences, Peyton Hall, Princeton University, Princeton, NJ 08544 (United States); Hilton, Matt, E-mail: sudeepphys@gmail.com [Centre for Astronomy and Particle Theory, School of Physics and Astronomy, University of Nottingham, NG7 2RD (United Kingdom); and others

    2014-04-01

    We present the temperature power spectra of the cosmic microwave background (CMB) derived from the three seasons of data from the Atacama Cosmology Telescope (ACT) at 148 GHz and 218 GHz, as well as the cross-frequency spectrum between the two channels. We detect and correct for contamination due to the Galactic cirrus in our equatorial maps. We present the results of a number of tests for possible systematic error and conclude that any effects are not significant compared to the statistical errors we quote. Where they overlap, we cross-correlate the ACT and the South Pole Telescope (SPT) maps and show they are consistent. The measurements of higher-order peaks in the CMB power spectrum provide an additional test of the ΛCDM cosmological model, and help constrain extensions beyond the standard model. The small angular scale power spectrum also provides constraining power on the Sunyaev-Zel'dovich effects and extragalactic foregrounds. We also present a measurement of the CMB gravitational lensing convergence power spectrum at 4.6σ detection significance.

  4. The Atacama Cosmology Telescope: Temperature and Gravitational Lensing Power Spectrum Measurements from Three Seasons of Data

    Science.gov (United States)

    Das, Sudeep; Louis, Thibaut; Nolta, Michael R.; Addison, Graeme E.; Battisetti, Elia S.; Bond, J. Richard; Calabrese, Erminia; Crichton, Devin; Devlin, Mark J.; Dicker, Simon; hide

    2014-01-01

    We present the temperature power spectra of the cosmic microwave background (CMB) derived from the three seasons of data from the Atacama Cosmology Telescope (ACT) at 148 GHz and 218 GHz, as well as the cross-frequency spectrum between the two channels. We detect and correct for contamination due to the Galactic cirrus in our equatorial maps. We present the results of a number of tests for possible systematic error and conclude that any effects are not significant compared to the statistical errors we quote. Where they overlap, we cross-correlate the ACT and the South Pole Telescope (SPT) maps and show they are consistent. The measurements of higher-order peaks in the CMB power spectrum provide an additional test of the ?CDM cosmological model, and help constrain extensions beyond the standard model. The small angular scale power spectrum also provides constraining power on the Sunyaev-Zel'dovich effects and extragalactic foregrounds. We also present a measurement of the CMB gravitational lensing convergence power spectrum at 4.6s detection significance.

  5. The Atacama Cosmology Telescope: temperature and gravitational lensing power spectrum measurements from three seasons of data

    International Nuclear Information System (INIS)

    Das, Sudeep; Louis, Thibaut; Calabrese, Erminia; Dunkley, Joanna; Nolta, Michael R.; Bond, J Richard; Hajian, Amir; Hincks, Adam D.; Addison, Graeme E.; Halpern, Mark; Battistelli, Elia S.; Crichton, Devin; Gralla, Megan; Devlin, Mark J.; Dicker, Simon; Dünner, Rolando; Fowler, Joseph W.; Hasselfield, Matthew; Hlozek, Renée; Hilton, Matt

    2014-01-01

    We present the temperature power spectra of the cosmic microwave background (CMB) derived from the three seasons of data from the Atacama Cosmology Telescope (ACT) at 148 GHz and 218 GHz, as well as the cross-frequency spectrum between the two channels. We detect and correct for contamination due to the Galactic cirrus in our equatorial maps. We present the results of a number of tests for possible systematic error and conclude that any effects are not significant compared to the statistical errors we quote. Where they overlap, we cross-correlate the ACT and the South Pole Telescope (SPT) maps and show they are consistent. The measurements of higher-order peaks in the CMB power spectrum provide an additional test of the ΛCDM cosmological model, and help constrain extensions beyond the standard model. The small angular scale power spectrum also provides constraining power on the Sunyaev-Zel'dovich effects and extragalactic foregrounds. We also present a measurement of the CMB gravitational lensing convergence power spectrum at 4.6σ detection significance

  6. H-ATLAS: THE COSMIC ABUNDANCE OF DUST FROM THE FAR-INFRARED BACKGROUND POWER SPECTRUM

    Energy Technology Data Exchange (ETDEWEB)

    Thacker, Cameron; Cooray, Asantha; Smidt, Joseph; De Bernardis, Francesco; Mitchell-Wynne, K. [Department of Physics and Astronomy, University of California, Irvine, CA 92697 (United States); Amblard, A. [NASA Ames Research Center, Moffett Field, CA 94035 (United States); Auld, R.; Eales, S.; Pascale, E. [School of Physics and Astronomy, Cardiff University, The Parade, Cardiff, CF24 3AA (United Kingdom); Baes, M.; Michalowski, M. J. [Sterrenkundig Observatorium, Universiteit Gent, KrijgslAAn 281 S9, B-9000 Gent (Belgium); Clements, D. L.; Dariush, A.; Hopwood, R. [Physics Department, Imperial College London, South Kensington campus, London, SW7 2AZ (United Kingdom); De Zotti, G. [INAF, Osservatorio Astronomico di Padova, Vicolo Osservatorio 5, I-35122 Padova (Italy); Dunne, L.; Maddox, S. [Department of Physics and Astronomy, University of Canterbury, Private Bag 4800, Christchurch (New Zealand); Hoyos, C. [School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD (United Kingdom); Ibar, E. [UK Astronomy Technology Centre, The Royal Observatory, Blackford Hill, Edinburgh, EH9 3HJ (United Kingdom); Jarvis, M. [Astrophysics, Department of Physics, Keble Road, Oxford, OX1 3RH (United Kingdom); and others

    2013-05-01

    We present a measurement of the angular power spectrum of the cosmic far-infrared background (CFIRB) anisotropies in one of the extragalactic fields of the Herschel Astrophysical Terahertz Large Area Survey at 250, 350, and 500 {mu}m bands. Consistent with recent measurements of the CFIRB power spectrum in Herschel-SPIRE maps, we confirm the existence of a clear one-halo term of galaxy clustering on arcminute angular scales with large-scale two-halo term of clustering at 30 arcmin to angular scales of a few degrees. The power spectrum at the largest angular scales, especially at 250 {mu}m, is contaminated by the Galactic cirrus. The angular power spectrum is modeled using a conditional luminosity function approach to describe the spatial distribution of unresolved galaxies that make up the bulk of the CFIRB. Integrating over the dusty galaxy population responsible for the background anisotropies, we find that the cosmic abundance of dust, relative to the critical density, to be between {Omega}{sub dust} = 10{sup -6} and 8 Multiplication-Sign 10{sup -6} in the redshift range z {approx} 0-3. This dust abundance is consistent with estimates of the dust content in the universe using quasar reddening and magnification measurements in the Sloan Digital Sky Survey.

  7. Wind Power accuracy and forecast. D3.1. Assumptions on accuracy of wind power to be considered at short and long term horizons

    Energy Technology Data Exchange (ETDEWEB)

    Morthorst, P.E.; Coulondre, J.M.; Schroeder, S.T.; Meibom, P.

    2010-07-15

    The main objective of the Optimate project (An Open Platform to Test Integration in new MArkeT designs of massive intermittent Energy sources dispersed in several regional power markets) is to develop a new tool for testing these new market designs with large introduction of variable renewable energy sources. In Optimate a novel network/system/market modelling approach is being developed, generating an open simulation platform able to exhibit the comparative benefits of several market design options. This report constitutes delivery 3.1 on the assumptions on accuracy of wind power to be considered at short and long term horizons. The report handles the issues of state-of-the-art prediction, how predictions for wind power enter into the Optimate model and a simple and a more advanced methodology of how to generate trajectories of prediction errors to be used in Optimate. The main conclusion is that undoubtedly, the advanced approach is to be preferred to the simple one seen from a theoretical viewpoint. However, the advanced approach was developed to the Wilmar-model with the purpose of describing the integration of large-scale wind power in Europe. As the main purpose of the Optimate model is not to test the integration of wind power, but to test new market designs assuming a strong growth in wind power production, a more simplified approach for describing wind power forecasts should be sufficient. Thus a further development of the simple approach is suggested, eventually including correlations between geographical areas. In this report the general methodologies for generating trajectories for wind power forecasts are outlined. However, the methods are not yet implemented. In the next phase of Optimate, the clusters will be defined and the needed data collected. Following this phase actual results will be generated to be used in Optimate. (LN)

  8. Normalized noise power spectrum of full field digital mammography detector system

    International Nuclear Information System (INIS)

    Norriza Mohd Isa; Wan Muhamad Saridan Wan Hassan

    2009-01-01

    Full text: A method to measure noise power spectrum of a full field digital mammography system is presented. The effect of X-ray radiation dose, size and configuration of region of interest on normalized noise power spectrum (NNPS) was investigated. Flat field images were acquired using RQA-M2 beam quality technique (Mo/Mo anode-filter, 28 kV, 2 mm Al) with different clinical radiation doses. The images were cropped at about 4 cm from the edge of the breast wall and then divided into different size of non-overlapping or overlapping segments. NNPS was determined through de trending, 2-D fast Fourier transformation and normalization. Our measurement shows that high radiation dose gave lower NNPS at a specific beam quality. (author)

  9. Epoch of reionization 21 cm forecasting from MCMC-constrained semi-numerical models

    Science.gov (United States)

    Hassan, Sultan; Davé, Romeel; Finlator, Kristian; Santos, Mario G.

    2017-06-01

    The recent low value of Planck Collaboration XLVII integrated optical depth to Thomson scattering suggests that the reionization occurred fairly suddenly, disfavouring extended reionization scenarios. This will have a significant impact on the 21 cm power spectrum. Using a semi-numerical framework, we improve our model from instantaneous to include time-integrated ionization and recombination effects, and find that this leads to more sudden reionization. It also yields larger H II bubbles that lead to an order of magnitude more 21 cm power on large scales, while suppressing the small-scale ionization power. Local fluctuations in the neutral hydrogen density play the dominant role in boosting the 21 cm power spectrum on large scales, while recombinations are subdominant. We use a Monte Carlo Markov chain approach to constrain our model to observations of the star formation rate functions at z = 6, 7, 8 from Bouwens et al., the Planck Collaboration XLVII optical depth measurements and the Becker & Bolton ionizing emissivity data at z ˜ 5. We then use this constrained model to perform 21 cm forecasting for Low Frequency Array, Hydrogen Epoch of Reionization Array and Square Kilometre Array in order to determine how well such data can characterize the sources driving reionization. We find that the Mock 21 cm power spectrum alone can somewhat constrain the halo mass dependence of ionizing sources, the photon escape fraction and ionizing amplitude, but combining the Mock 21 cm data with other current observations enables us to separately constrain all these parameters. Our framework illustrates how the future 21 cm data can play a key role in understanding the sources and topology of reionization as observations improve.

  10. Power Spectrum of a Noisy System Close to a Heteroclinic Orbit

    Science.gov (United States)

    Giner-Baldó, Jordi; Thomas, Peter J.; Lindner, Benjamin

    2017-07-01

    We consider a two-dimensional dynamical system that possesses a heteroclinic orbit connecting four saddle points. This system is not able to show self-sustained oscillations on its own. If endowed with white Gaussian noise it displays stochastic oscillations, the frequency and quality factor of which are controlled by the noise intensity. This stochastic oscillation of a nonlinear system with noise is conveniently characterized by the power spectrum of suitable observables. In this paper we explore different analytical and semianalytical ways to compute such power spectra. Besides a number of explicit expressions for the power spectrum, we find scaling relations for the frequency, spectral width, and quality factor of the stochastic heteroclinic oscillator in the limit of weak noise. In particular, the quality factor shows a slow logarithmic increase with decreasing noise of the form Q˜ [ln (1/D)]^2. Our results are compared to numerical simulations of the respective Langevin equations.

  11. Forecast of nuclear energetics

    Energy Technology Data Exchange (ETDEWEB)

    Sikora, W

    1976-01-01

    The forecast concerning the development of nuclear energetics is presented. Some information on economics of nuclear power plants is given. The nuclear fuel reserves are estimated on the background of power resources of the world. The safety and environment protection problems are mentioned.

  12. Matter power spectrum and the challenge of percent accuracy

    International Nuclear Information System (INIS)

    Schneider, Aurel; Teyssier, Romain; Potter, Doug; Stadel, Joachim; Reed, Darren S.; Onions, Julian; Pearce, Frazer R.; Smith, Robert E.; Springel, Volker; Scoccimarro, Roman

    2016-01-01

    Future galaxy surveys require one percent precision in the theoretical knowledge of the power spectrum over a large range including very nonlinear scales. While this level of accuracy is easily obtained in the linear regime with perturbation theory, it represents a serious challenge for small scales where numerical simulations are required. In this paper we quantify the precision of present-day N -body methods, identifying main potential error sources from the set-up of initial conditions to the measurement of the final power spectrum. We directly compare three widely used N -body codes, Ramses, Pkdgrav3, and Gadget3 which represent three main discretisation techniques: the particle-mesh method, the tree method, and a hybrid combination of the two. For standard run parameters, the codes agree to within one percent at k ≤1 h Mpc −1 and to within three percent at k ≤10 h Mpc −1 . We also consider the bispectrum and show that the reduced bispectra agree at the sub-percent level for k ≤ 2 h Mpc −1 . In a second step, we quantify potential errors due to initial conditions, box size, and resolution using an extended suite of simulations performed with our fastest code Pkdgrav3. We demonstrate that the simulation box size should not be smaller than L =0.5 h −1 Gpc to avoid systematic finite-volume effects (while much larger boxes are required to beat down the statistical sample variance). Furthermore, a maximum particle mass of M p =10 9 h −1 M ⊙ is required to conservatively obtain one percent precision of the matter power spectrum. As a consequence, numerical simulations covering large survey volumes of upcoming missions such as DES, LSST, and Euclid will need more than a trillion particles to reproduce clustering properties at the targeted accuracy.

  13. Nonlinear evolution of the matter power spectrum in modified theories of gravity

    International Nuclear Information System (INIS)

    Koyama, Kazuya; Taruya, Atsushi; Hiramatsu, Takashi

    2009-01-01

    We present a formalism to calculate the nonlinear matter power spectrum in modified gravity models that explain the late-time acceleration of the Universe without dark energy. Any successful modified gravity models should contain a mechanism to recover general relativity (GR) on small scales in order to avoid the stringent constrains on deviations from GR at solar system scales. Based on our formalism, the quasi-nonlinear power spectrum in the Dvali-Gabadadze-Porratti braneworld models and f(R) gravity models are derived by taking into account the mechanism to recover GR properly. We also extrapolate our predictions to fully nonlinear scales using the parametrized post-Friedmann framework. In the Dvali-Gabadadze-Porratti and f(R) gravity models, the predicted nonlinear power spectrum is shown to reproduce N-body results. We find that the mechanism to recover GR suppresses the difference between the modified gravity models and dark energy models with the same expansion history, but the difference remains large at the weakly nonlinear regime in these models. Our formalism is applicable to a wide variety of modified gravity models and it is ready to use once consistent models for modified gravity are developed.

  14. Integration of wind generation forecasts. Volume 2

    International Nuclear Information System (INIS)

    Ahlstrom, M.; Zavadil, B.; Jones, L.

    2005-01-01

    WindLogics is a company that specializes in atmospheric modelling, visualization and fine-scale forecasting systems for the wind power industry. A background of the organization was presented. The complexities of wind modelling were discussed. Issues concerning location and terrain, shear, diurnal and interannual variability were reviewed. It was suggested that wind power producers should aim to be mainstream, and that variability should be considered as intrinsic to fuel supply. Various utility operating impacts were outlined. Details of an Xcel NSP wind integration study were presented, as well as a studies conducted in New York state and Colorado. It was concluded that regulations and load following impacts with wind energy integration are modest. Overall impacts are dominated by costs incurred to accommodate wind generation variability and uncertainty in the day-ahead time frame. Cost impacts can be reduced with adjustments to operating strategies, improvements in wind forecasting and access to real-time markets. Details of WindLogic's wind energy forecast system were presented, as well as examples of day ahead and hour ahead forecasts and wind speed and power forecasts. Screenshots of control room integration, EMS integration and simulations were presented. Details of a utility-scale wind energy forecasting system funded by Xcel Renewable Development Fund (RDF) were also presented. The goal of the system was to optimize the way that wind forecast information is integrated into the control room environment. Project components were outlined. It was concluded that accurate day-ahead forecasting can lead to significant asset optimization. It was recommended that wind plants share data, and aim to resolve issues concerning grid codes and instrumentation. refs., tabs., figs

  15. Forecast of auroral activity

    International Nuclear Information System (INIS)

    Lui, A.T.Y.

    2004-01-01

    A new technique is developed to predict auroral activity based on a sample of over 9000 auroral sites identified in global auroral images obtained by an ultraviolet imager on the NASA Polar satellite during a 6-month period. Four attributes of auroral activity sites are utilized in forecasting, namely, the area, the power, and the rates of change in area and power. This new technique is quite accurate, as indicated by the high true skill scores for forecasting three different levels of auroral dissipation during the activity lifetime. The corresponding advanced warning time ranges from 22 to 79 min from low to high dissipation levels

  16. Wind and load forecast error model for multiple geographically distributed forecasts

    Energy Technology Data Exchange (ETDEWEB)

    Makarov, Yuri V.; Reyes-Spindola, Jorge F.; Samaan, Nader; Diao, Ruisheng; Hafen, Ryan P. [Pacific Northwest National Laboratory, Richland, WA (United States)

    2010-07-01

    The impact of wind and load forecast errors on power grid operations is frequently evaluated by conducting multi-variant studies, where these errors are simulated repeatedly as random processes based on their known statistical characteristics. To simulate these errors correctly, we need to reflect their distributions (which do not necessarily follow a known distribution law), standard deviations. auto- and cross-correlations. For instance, load and wind forecast errors can be closely correlated in different zones of the system. This paper introduces a new methodology for generating multiple cross-correlated random processes to produce forecast error time-domain curves based on a transition probability matrix computed from an empirical error distribution function. The matrix will be used to generate new error time series with statistical features similar to observed errors. We present the derivation of the method and some experimental results obtained by generating new error forecasts together with their statistics. (orig.)

  17. Measurement and analysis of noise power spectrum of computerized tomography in images

    International Nuclear Information System (INIS)

    Castro Tejero, P.; Garayoa Roca, J.

    2013-01-01

    This paper examines the implementation of the spectrum of powers of the noise, NPS, as metric to characterize the noise, both in magnitude and in texture, for CT scans. The NPS found show that you for convolution filters that assume a greater softening in the reconstructed image, spectrum is concentrated in the low frequencies, while for filters sharp, the spectrum extends to high frequencies. In the analyzed cases, there is a low frequency component, largely due to the structure-borne noise, which can be a potential negative effect on the detectability of injuries. (Author)

  18. One-dimensional power spectrum and neutrino mass in the spectra of BOSS

    International Nuclear Information System (INIS)

    Borde, Arnaud

    2014-01-01

    The framework of the studies presented in this thesis is the one-dimensional power spectrum of the transmitted flux in the Lyman-alpha forests. The Lyman-alpha forest is an absorption pattern seen in the spectra of high redshift quasars corresponding to the absorption of the quasar light by the hydrogen clouds along the line of sight. It is a powerful cosmological tool as it probes relatively small scales, of the order of a few Mpc. It is also sensible to small non-linear effects such as the one induced by massive neutrinos. First, we have developed two independent methods to measure the one-dimensional power spectrum of the transmitted flux in the Lyman-alpha forest. The first method is based on a Fourier transform, and the second on a maximum likelihood estimator. The two methods are independent and have different systematic uncertainties. The determination of the noise level in the data spectra was subject to a novel treatment, because of its significant impact on the derived power spectrum. We applied the two methods to 13,821 quasar spectra from SDSS-III/BOSS DR9 selected from a larger sample of over 60,000 spectra on the basis of their high quality, large signal-to-noise ratio, and good spectral resolution. The power spectra measured using either approach are in good agreement over all twelve redshift bins from =2.2 to =4.4, and scales from 0.001 (km/s)"-"1 to 0.02 (km/s)"-"1. We carefully determined the methodological and instrumental systematic uncertainties of our measurements. Then, we present a suite of cosmological N-body simulations with cold dark matter, baryons and neutrinos aiming at modeling the low-density regions of the IGM as probed by the Lyman-alpha forests at high redshift. The simulations are designed to match the requirements imposed by the quality of BOSS and eBOSS data. They are made using either 768"3 or 192"3 particles of each type, spanning volumes ranging from (25 Mpc/h)"3 for high-resolution simulations to (100 Mpc/h)"3 for large

  19. Distance Dependent Model for the Delay Power Spectrum of In-room Radio Channels

    DEFF Research Database (Denmark)

    Steinböck, Gerhard; Pedersen, Troels; Fleury, Bernard Henri

    2013-01-01

    A model based on experimental observations of the delay power spectrum in closed rooms is proposed. The model includes the distance between the transmitter and the receiver as a parameter which makes it suitable for range based radio localization. The experimental observations motivate the proposed...... model of the delay power spectrum with a primary (early) component and a reverberant component (tail). The primary component is modeled as a Dirac delta function weighted according to an inverse distance power law (d-n). The reverberant component is an exponentially decaying function with onset equal...... to the propagation time between transmitter and receiver. Its power decays exponentially with distance. The proposed model allows for the prediction of e.g. the path loss, mean delay, root mean squared (rms) delay spread, and kurtosis versus the distance. The model predictions are validated by measurements...

  20. Extreme scenarios: the tightest possible constraints on the power spectrum due to primordial black holes

    Science.gov (United States)

    Cole, Philippa S.; Byrnes, Christian T.

    2018-02-01

    Observational constraints on the abundance of primordial black holes (PBHs) constrain the allowed amplitude of the primordial power spectrum on both the smallest and the largest ranges of scales, covering over 20 decades from 1‑1020/ Mpc. Despite tight constraints on the allowed fraction of PBHs at their time of formation near horizon entry in the early Universe, the corresponding constraints on the primordial power spectrum are quite weak, typically Script PRlesssim 10‑2 assuming Gaussian perturbations. Motivated by recent claims that the evaporation of just one PBH would destabilise the Higgs vacuum and collapse the Universe, we calculate the constraints which follow from assuming there are zero PBHs within the observable Universe. Even if evaporating PBHs do not collapse the Universe, this scenario represents the ultimate limit of observational constraints. Constraints can be extended on to smaller scales right down to the horizon scale at the end of inflation, but where power spectrum constraints already exist they do not tighten significantly, even though the constraint on PBH abundance can decrease by up to 46 orders of magnitude. This shows that no future improvement in observational constraints can ever lead to a significant tightening in constraints on inflation (via the power spectrum amplitude). The power spectrum constraints are weak because an order unity perturbation is required in order to overcome pressure forces. We therefore consider an early matter dominated era, during which exponentially more PBHs form for the same initial conditions. We show this leads to far tighter constraints, which approach Script PRlesssim10‑9, albeit over a smaller range of scales and are very sensitive to when the early matter dominated era ends. Finally, we show that an extended early matter era is incompatible with the argument that an evaporating PBH would destroy the Universe, unless the power spectrum amplitude decreases by up to ten orders of magnitude.

  1. Operational forecasting based on a modified Weather Research and Forecasting model

    Energy Technology Data Exchange (ETDEWEB)

    Lundquist, J; Glascoe, L; Obrecht, J

    2010-03-18

    Accurate short-term forecasts of wind resources are required for efficient wind farm operation and ultimately for the integration of large amounts of wind-generated power into electrical grids. Siemens Energy Inc. and Lawrence Livermore National Laboratory, with the University of Colorado at Boulder, are collaborating on the design of an operational forecasting system for large wind farms. The basis of the system is the numerical weather prediction tool, the Weather Research and Forecasting (WRF) model; large-eddy simulations and data assimilation approaches are used to refine and tailor the forecasting system. Representation of the atmospheric boundary layer is modified, based on high-resolution large-eddy simulations of the atmospheric boundary. These large-eddy simulations incorporate wake effects from upwind turbines on downwind turbines as well as represent complex atmospheric variability due to complex terrain and surface features as well as atmospheric stability. Real-time hub-height wind speed and other meteorological data streams from existing wind farms are incorporated into the modeling system to enable uncertainty quantification through probabilistic forecasts. A companion investigation has identified optimal boundary-layer physics options for low-level forecasts in complex terrain, toward employing decadal WRF simulations to anticipate large-scale changes in wind resource availability due to global climate change.

  2. Third-Order Density Perturbation and One-Loop Power Spectrum in Dark-Energy-Dominated Universe

    OpenAIRE

    Takahashi, Ryuichi

    2008-01-01

    We investigate the third-order density perturbation and the one-loop correction to the linear power spectrum in the dark-energy cosmological model. Our main interest is to understand the dark-energy effect on baryon acoustic oscillations in a quasi-nonlinear regime ($k \\approx 0.1h$/Mpc). Analytical solutions and simple fitting formulae are presented for the dark-energy model with the general time-varying equation of state $w(a)$. It turns out that the power spectrum coincides with the approx...

  3. An Operational Short-Term Forecasting System for Regional Hydropower Management

    Science.gov (United States)

    Gronewold, A.; Labuhn, K. A.; Calappi, T. J.; MacNeil, A.

    2017-12-01

    The Niagara River is the natural outlet of Lake Erie and drains four of the five Great lakes. The river is used to move commerce and is home to both sport fishing and tourism industries. It also provides nearly 5 million kilowatts of hydropower for approximately 3.9 million homes. Due to a complex international treaty and the necessity of balancing water needs for an extensive tourism industry, the power entities operating on the river require detailed and accurate short-term river flow forecasts to maximize power output. A new forecast system is being evaluated that takes advantage of several previously independent components including the NOAA Lake Erie operational Forecast System (LEOFS), a previously developed HEC-RAS model, input from the New York Power Authority(NYPA) and Ontario Power Generation (OPG) and lateral flow forecasts for some of the tributaries provided by the NOAA Northeast River Forecast Center (NERFC). The Corps of Engineers updated the HEC-RAS model of the upper Niagara River to use the output forcing from LEOFS and a planned Grass Island Pool elevation provided by the power entities. The entire system has been integrated at the NERFC; it will be run multiple times per day with results provided to the Niagara River Control Center operators. The new model helps improve discharge forecasts by better accounting for dynamic conditions on Lake Erie. LEOFS captures seiche events on the lake that are often several meters of displacement from still water level. These seiche events translate into flow spikes that HEC-RAS routes downstream. Knowledge of the peak arrival time helps improve operational decisions at the Grass Island Pool. This poster will compare and contrast results from the existing operational flow forecast and the new integrated LEOFS/HEC-RAS forecast. This additional model will supply the Niagara River Control Center operators with multiple forecasts of flow to help improve forecasting under a wider variety of conditions.

  4. Forecasted electric power demands for the Delmarva Power and Light Company. Volume 1 and Volume 2. Documentation manual

    International Nuclear Information System (INIS)

    Estomin, S.L.; Beach, J.E.

    1990-10-01

    The two-volume report presents the results of an econometric forecast of peak load and electric power demands for the Delmarva Power and Light Company (DP ampersand L) through the year 2008. Separate sets of models were estimated for the three jurisdictions served by DP ampersand L: Delaware, Maryland and Virginia. For both Delaware and Maryland, econometric equations were estimated for residential, commercial, industrial, and streetlighting sales. For Virginia, equations were estimated for residential, commercial plus industrial, and streetlighting sales; separate industrial and commercial equations were not estimated for Virginia due to the relatively small size of DP ampersand L's Virginia Industrial load. Wholesale sales were econometrically estimated for the DP ampersand L system as a whole. In addition to the energy sales models, an econometric model of annual (summer) peak demand was estimated for the Company

  5. The Atacama Cosmology Telescope: Cosmological Parameters from the 2008 Power Spectrum

    Science.gov (United States)

    Dunkley, J.; Hlozek, R.; Sievers, J.; Acquaviva, V.; Ade, P. A. R.; Aguirre, P.; Amiri, M.; Appel, J. W.; Barrientos, L. F.; Battistelli, E. S.; hide

    2011-01-01

    We present cosmological parameters derived from the angular power spectrum of the cosmic microwave background (CMB) radiation observed at 148 GHz and 218 GHz over 296 deg(exp 2) with the Atacama Cosmology Telescope (ACT) during its 2008 season. ACT measures fluctuations at scales 500 cosmological parameters from the less contaminated 148 GHz spectrum, marginalizing over SZ and source power. The ACDM cosmological model is a good fit to the data (chi square/dof = 29/46), and ACDM parameters estimated from ACT+Wilkinson Microwave Anisotropy Probe (WMAP) are consistent with the seven-year WMAP limits, with scale invariant n(sub s) = 1 excluded at 99.7% confidence level (CL) (3 sigma). A model with no CMB lensing is disfavored at 2.8 sigma. By measuring the third to seventh acoustic peaks, and probing the Silk damping regime, the ACT data improve limits on cosmological parameters that affect the small-scale CMB power. The ACT data combined with WMAP give a 6 sigma detection of primordial helium, with Y(sub p) = 0.313 +/- 0.044, and a 4 sigma detection of relativistic species, assumed to be neutrinos, with N(sub eff) = 5.3 +/- 1.3 (4.6 +/- 0.8 with BAO+H(sub 0) data). From the CMB alone the running of the spectral index is constrained to be d(sub s) / d ln k = -0,034 +/- 0,018, the limit on the tensor-to-scalar ratio is r < 0,25 (95% CL), and the possible contribution of Nambu cosmic strings to the power spectrum is constrained to string tension G(sub mu) < 1.6 x 10(exp -7) (95% CL),

  6. Turbulent Cloud Structure and Power Spectrum from 23 years of HST Observations

    Science.gov (United States)

    Cosentino, Richard; Simon, Amy; Morales-Juberias, Raul

    2018-01-01

    Images of Jupiter’s clouds show that turbulence is a ubiquitous phenomenon over many orders of scale size. According to Kolmogorov’s theory for turbulence, the frequency/distribution of clouds at various scales can be used to produce an energy power spectrum of a passive tracer. Kolmogorov theory predicts the spectral slopes for “shallow” and “deep” fluids in motion by following how energy is injected and dissipated in the fluid. We are quantifying the turbulent nature of Jupiter’s clouds over 23 years of Hubble Space Telescope (HST) observations using an algorithm first presented in Choi and Showman (2011, Icarus 216). We applied the power spectrum fitting algorithm to a variety of filters from available HST data and tested its sensitivity to free parameters and compare our results to Choi and Showman (2011). We will comment on the evidence for a 2D turbulent regime In Jupiter’s clouds and will report on empirical values found in the spectra and their physical interpretations, such as the Rhines scale. We also will report on the behavior of the passive tracer power spectrum and trends that exist over time for different latitudinal regions, primarily the belts and zones and the north and south equatorial belts.

  7. Evaluation of the performance of a meso-scale NWP model to forecast solar irradiance on Reunion Island for photovoltaic power applications

    Science.gov (United States)

    Kalecinski, Natacha; Haeffelin, Martial; Badosa, Jordi; Periard, Christophe

    2013-04-01

    Solar photovoltaic power is a predominant source of electrical power on Reunion Island, regularly providing near 30% of electrical power demand for a few hours per day. However solar power on Reunion Island is strongly modulated by clouds in small temporal and spatial scales. Today regional regulations require that new solar photovoltaic plants be combined with storage systems to reduce electrical power fluctuations on the grid. Hence cloud and solar irradiance forecasting becomes an important tool to help optimize the operation of new solar photovoltaic plants on Reunion Island. Reunion Island, located in the South West of the Indian Ocean, is exposed to persistent trade winds, most of all in winter. In summer, the southward motion of the ITCZ brings atmospheric instabilities on the island and weakens trade winds. This context together with the complex topography of Reunion Island, which is about 60 km wide, with two high summits (3070 and 2512 m) connected by a 1500 m plateau, makes cloudiness very heterogeneous. High cloudiness variability is found between mountain and coastal areas and between the windward, leeward and lateral regions defined with respect to the synoptic wind direction. A detailed study of local dynamics variability is necessary to better understand cloud life cycles around the island. In the presented work, our approach to explore the short-term solar irradiance forecast at local scales is to use the deterministic output from a meso-scale numerical weather prediction (NWP) model, AROME, developed by Meteo France. To start we evaluate the performance of the deterministic forecast from AROME by using meteorological measurements from 21 meteorological ground stations widely spread around the island (and with altitudes from 8 to 2245 m). Ground measurements include solar irradiation, wind speed and direction, relative humidity, air temperature, precipitation and pressure. Secondly we study in the model the local dynamics and thermodynamics that

  8. Power spectrum model of visual masking: simulations and empirical data.

    Science.gov (United States)

    Serrano-Pedraza, Ignacio; Sierra-Vázquez, Vicente; Derrington, Andrew M

    2013-06-01

    In the study of the spatial characteristics of the visual channels, the power spectrum model of visual masking is one of the most widely used. When the task is to detect a signal masked by visual noise, this classical model assumes that the signal and the noise are previously processed by a bank of linear channels and that the power of the signal at threshold is proportional to the power of the noise passing through the visual channel that mediates detection. The model also assumes that this visual channel will have the highest ratio of signal power to noise power at its output. According to this, there are masking conditions where the highest signal-to-noise ratio (SNR) occurs in a channel centered in a spatial frequency different from the spatial frequency of the signal (off-frequency looking). Under these conditions the channel mediating detection could vary with the type of noise used in the masking experiment and this could affect the estimation of the shape and the bandwidth of the visual channels. It is generally believed that notched noise, white noise and double bandpass noise prevent off-frequency looking, and high-pass, low-pass and bandpass noises can promote it independently of the channel's shape. In this study, by means of a procedure that finds the channel that maximizes the SNR at its output, we performed numerical simulations using the power spectrum model to study the characteristics of masking caused by six types of one-dimensional noise (white, high-pass, low-pass, bandpass, notched, and double bandpass) for two types of channel's shape (symmetric and asymmetric). Our simulations confirm that (1) high-pass, low-pass, and bandpass noises do not prevent the off-frequency looking, (2) white noise satisfactorily prevents the off-frequency looking independently of the shape and bandwidth of the visual channel, and interestingly we proved for the first time that (3) notched and double bandpass noises prevent off-frequency looking only when the noise

  9. Dynamics of the spectrum of a self-modulated powerful laser pulse in an underdense plasma

    International Nuclear Information System (INIS)

    Andreev, N.E.; Kirsanov, V.I.; Sakharov, A.S.

    1997-01-01

    The evolution of the spectrum of a powerful laser pulse during its self-modulation in an underdense plasma is studied analytically and numerically. It is shown that, in the early stages of the self-modulation instability, the linear theory gives a qualitatively correct description of the dynamics of the pulse spectrum in most cases. Depending on the parameters of the laser pulse and of the plasma, this spectrum contains either Stocks satellites (downshifted from the fundamental frequency to a value equal to the plasma frequency), or both Stocks and anti-Stocks satellites of the fundamental frequency. When the three-dimensional mechanism for the instability is dominant and the pulse power is close to the critical power for relativistic self-focusing, the numerical calculations show that the intensity of the blue satellite exceeds the intensity of the red one. This specific feature of the spectrum, which does not arise when the instability is one-dimensional, cannot be explained in terms of the linear para-axial theory, and can be used to identify the three-dimensional mechanism for the instability in experiments on the self-modulation of powerful laser pulses. It is shown that the transition to the nonlinear stage of the instability is accompanied by the occurrence of cascades (at frequencies separated from the laser carrier frequency by intervals equal to an integer number of plasma frequencies) in the spectrum of the laser pulse

  10. Power plant site evaluation, electric energy demand forecasts - Douglas Point Site. Volume 3. Final report

    International Nuclear Information System (INIS)

    Wilson, J.W.

    1975-07-01

    This is part of a series of reports containing an evaluation of the proposed Douglas Point nuclear generating station site located on the Potomac River in Maryland 30 miles south of Washington, D.C. This report contains chapters on the Potomac Electric Power Company's market, forecasting future demand, modelling, a residential demand model, a nonresidential demand model, the Southern Maryland Electric Cooperative Model, short term predictive accuracy, and total system requirements

  11. Constraining Primordial non-Gaussianity with Bispectrum and Power Spectum from Upcoming Optical and Radio Surveys

    Science.gov (United States)

    Karagiannis, Dionysios; Lazanu, Andrei; Liguori, Michele; Raccanelli, Alvise; Bartolo, Nicola; Verde, Licia

    2018-04-01

    We forecast constraints on primordial non-Gaussianity (PNG) and bias parameters from measurements of galaxy power spectrum and bispectrum in future radio continuum and optical surveys. In the galaxy bispectrum, we consider a comprehensive list of effects, including the bias expansion for non-Gaussian initial conditions up to second order, redshift space distortions, redshift uncertainties and theoretical errors. These effects are all combined in a single PNG forecast for the first time. Moreover, we improve the bispectrum modelling over previous forecasts, by accounting for trispectrum contributions. All effects have an impact on final predicted bounds, which varies with the type of survey. We find that the bispectrum can lead to improvements up to a factor ˜5 over bounds based on the power spectrum alone, leading to significantly better constraints for local-type PNG, with respect to current limits from Planck. Future radio and photometric surveys could obtain a measurement error of σ (f_{NL}^{loc}) ≈ 0.2. In the case of equilateral PNG, galaxy bispectrum can improve upon present bounds only if significant improvements in the redshift determinations of future, large volume, photometric or radio surveys could be achieved. For orthogonal non-Gaussianity, expected constraints are generally comparable to current ones.

  12. Advancing solar energy forecasting through the underlying physics

    Science.gov (United States)

    Yang, H.; Ghonima, M. S.; Zhong, X.; Ozge, B.; Kurtz, B.; Wu, E.; Mejia, F. A.; Zamora, M.; Wang, G.; Clemesha, R.; Norris, J. R.; Heus, T.; Kleissl, J. P.

    2017-12-01

    As solar power comprises an increasingly large portion of the energy generation mix, the ability to accurately forecast solar photovoltaic generation becomes increasingly important. Due to the variability of solar power caused by cloud cover, knowledge of both the magnitude and timing of expected solar power production ahead of time facilitates the integration of solar power onto the electric grid by reducing electricity generation from traditional ancillary generators such as gas and oil power plants, as well as decreasing the ramping of all generators, reducing start and shutdown costs, and minimizing solar power curtailment, thereby providing annual economic value. The time scales involved in both the energy markets and solar variability range from intra-hour to several days ahead. This wide range of time horizons led to the development of a multitude of techniques, with each offering unique advantages in specific applications. For example, sky imagery provides site-specific forecasts on the minute-scale. Statistical techniques including machine learning algorithms are commonly used in the intra-day forecast horizon for regional applications, while numerical weather prediction models can provide mesoscale forecasts on both the intra-day and days-ahead time scale. This talk will provide an overview of the challenges unique to each technique and highlight the advances in their ongoing development which come alongside advances in the fundamental physics underneath.

  13. Testing Rastall's theory using matter power spectrum

    International Nuclear Information System (INIS)

    Batista, C.E.M.; Fabris, J.C.; Daouda, M.H.

    2010-01-01

    Rastall's theory is a modification of the General Relativity theory leading to a different expression for the conservation law in the matter sector compared with the usual one. It has been argued recently that such a theory may have applications to the dark energy problem, since a pressureless fluid may lead to an accelerated universe. In the present work we confront Rastall's theory with the power spectrum data. The results indicate a configuration that essentially reduces Rastall's theory to General Relativity, unless the non-usual conservation law refers to a scalar field, situation where other configurations are eventually possible.

  14. Energy and electricity demand forecasting for nuclear power planning in developing countries

    International Nuclear Information System (INIS)

    1988-07-01

    This Guidebook is designed to be a reference document to forecast energy and electricity demand. It presents concepts and methodologies that have been developed to make an analytical approach to energy/electricity demand forecasting as part of the planning process. The Guidebook is divided into 6 main chapters: (Energy demand and development, energy demand analysis, electric load curve analysis, energy and electricity demand forecasting, energy and electricity demand forecasting tools used in various organizations, IAEA methodologies for energy and electricity demand forecasting) and 3 appendices (experience with case studies carried out by the IAEA, reference technical data, reference economic data). A bibliography and a glossary complete the Guidebook. Refs, figs and tabs

  15. A new spinning reserve requirement forecast method for deregulated electricity markets

    International Nuclear Information System (INIS)

    Amjady, Nima; Keynia, Farshid

    2010-01-01

    Ancillary services are necessary for maintaining the security and reliability of power systems and constitute an important part of trade in competitive electricity markets. Spinning Reserve (SR) is one of the most important ancillary services for saving power system stability and integrity in response to contingencies and disturbances that continuously occur in the power systems. Hence, an accurate day-ahead forecast of SR requirement helps the Independent System Operator (ISO) to conduct a reliable and economic operation of the power system. However, SR signal has complex, non-stationary and volatile behavior along the time domain and depends greatly on system load. In this paper, a new hybrid forecast engine is proposed for SR requirement prediction. The proposed forecast engine has an iterative training mechanism composed of Levenberg-Marquadt (LM) learning algorithm and Real Coded Genetic Algorithm (RCGA), implemented on the Multi-Layer Perceptron (MLP) neural network. The proposed forecast methodology is examined by means of real data of Pennsylvania-New Jersey-Maryland (PJM) electricity market and the California ISO (CAISO) controlled grid. The obtained forecast results are presented and compared with those of the other SR forecast methods. (author)

  16. A new spinning reserve requirement forecast method for deregulated electricity markets

    Energy Technology Data Exchange (ETDEWEB)

    Amjady, Nima; Keynia, Farshid [Department of Electrical Engineering, Semnan University, Semnan (Iran)

    2010-06-15

    Ancillary services are necessary for maintaining the security and reliability of power systems and constitute an important part of trade in competitive electricity markets. Spinning Reserve (SR) is one of the most important ancillary services for saving power system stability and integrity in response to contingencies and disturbances that continuously occur in the power systems. Hence, an accurate day-ahead forecast of SR requirement helps the Independent System Operator (ISO) to conduct a reliable and economic operation of the power system. However, SR signal has complex, non-stationary and volatile behavior along the time domain and depends greatly on system load. In this paper, a new hybrid forecast engine is proposed for SR requirement prediction. The proposed forecast engine has an iterative training mechanism composed of Levenberg-Marquadt (LM) learning algorithm and Real Coded Genetic Algorithm (RCGA), implemented on the Multi-Layer Perceptron (MLP) neural network. The proposed forecast methodology is examined by means of real data of Pennsylvania-New Jersey-Maryland (PJM) electricity market and the California ISO (CAISO) controlled grid. The obtained forecast results are presented and compared with those of the other SR forecast methods. (author)

  17. Numerical forecast test on local wind fields at Qinshan Nuclear Power Plant

    International Nuclear Information System (INIS)

    Chen Xiaoqiu

    2005-01-01

    Non-hydrostatic, full compressible atmospheric dynamics model is applied to perform numerical forecast test on local wind fields at Qinshan nuclear power plant, and prognostic data are compared with observed data for wind fields. The results show that the prognostic of wind speeds is better than that of wind directions as compared with observed results. As the whole, the results of prognostic wind field are consistent with meteorological observation data, 54% of wind speeds are within a factor of 1.5, about 61% of the deviation of wind direction within the 1.5 azimuth (≤33.75 degrees) in the first six hours. (authors)

  18. Matter power spectrum and the challenge of percent accuracy

    Energy Technology Data Exchange (ETDEWEB)

    Schneider, Aurel; Teyssier, Romain; Potter, Doug; Stadel, Joachim; Reed, Darren S. [Institute for Computational Science, University of Zurich, Winterthurerstrasse 190, 8057 Zurich (Switzerland); Onions, Julian; Pearce, Frazer R. [School of Physics and Astronomy, University of Nottingham, Nottingham NG7 2RD (United Kingdom); Smith, Robert E. [Department of Physics and Astronomy, University of Sussex, Brighton, BN1 9QH (United Kingdom); Springel, Volker [Heidelberger Institut für Theoretische Studien, 69118 Heidelberg (Germany); Scoccimarro, Roman, E-mail: aurel@physik.uzh.ch, E-mail: teyssier@physik.uzh.ch, E-mail: dpotter@physik.uzh.ch, E-mail: stadel@physik.uzh.ch, E-mail: julian.onions@nottingham.ac.uk, E-mail: reed@physik.uzh.ch, E-mail: r.e.smith@sussex.ac.uk, E-mail: volker.springel@h-its.org, E-mail: Frazer.Pearce@nottingham.ac.uk, E-mail: rs123@nyu.edu [Center for Cosmology and Particle Physics, Department of Physics, New York University, NY 10003, New York (United States)

    2016-04-01

    Future galaxy surveys require one percent precision in the theoretical knowledge of the power spectrum over a large range including very nonlinear scales. While this level of accuracy is easily obtained in the linear regime with perturbation theory, it represents a serious challenge for small scales where numerical simulations are required. In this paper we quantify the precision of present-day N -body methods, identifying main potential error sources from the set-up of initial conditions to the measurement of the final power spectrum. We directly compare three widely used N -body codes, Ramses, Pkdgrav3, and Gadget3 which represent three main discretisation techniques: the particle-mesh method, the tree method, and a hybrid combination of the two. For standard run parameters, the codes agree to within one percent at k ≤1 h Mpc{sup −1} and to within three percent at k ≤10 h Mpc{sup −1}. We also consider the bispectrum and show that the reduced bispectra agree at the sub-percent level for k ≤ 2 h Mpc{sup −1}. In a second step, we quantify potential errors due to initial conditions, box size, and resolution using an extended suite of simulations performed with our fastest code Pkdgrav3. We demonstrate that the simulation box size should not be smaller than L =0.5 h {sup −1}Gpc to avoid systematic finite-volume effects (while much larger boxes are required to beat down the statistical sample variance). Furthermore, a maximum particle mass of M {sub p}=10{sup 9} h {sup −1}M{sub ⊙} is required to conservatively obtain one percent precision of the matter power spectrum. As a consequence, numerical simulations covering large survey volumes of upcoming missions such as DES, LSST, and Euclid will need more than a trillion particles to reproduce clustering properties at the targeted accuracy.

  19. EFFECTS OF THE NEUTRINO MASS SPLITTING ON THE NONLINEAR MATTER POWER SPECTRUM

    International Nuclear Information System (INIS)

    Wagner, Christian; Verde, Licia; Jimenez, Raul

    2012-01-01

    We have performed cosmological N-body simulations which include the effect of the masses of the individual neutrino species. The simulations were aimed at studying the effect of different neutrino hierarchies on the matter power spectrum. Compared to the linear theory predictions, we find that nonlinearities enhance the effect of hierarchy on the matter power spectrum at mildly nonlinear scales. The maximum difference between the different hierarchies is about 0.5% for a sum of neutrino masses of 0.1 eV. Albeit this is a small effect, it is potentially measurable from upcoming surveys. In combination with neutrinoless double-β decay experiments, this opens up the possibility of using the sky to determine if neutrinos are Majorana or Dirac fermions.

  20. Forecasting daily political opinion polls using the fractionally cointegrated VAR model

    DEFF Research Database (Denmark)

    Nielsen, Morten Ørregaard; Shibaev, Sergei S.

    We examine forecasting performance of the recent fractionally cointegrated vector autoregressive (FCVAR) model. We use daily polling data of political support in the United Kingdom for 2010-2015 and compare with popular competing models at several forecast horizons. Our findings show that the four...... trend from the model follows the vote share of the UKIP very closely, and we thus interpret it as a measure of Euro-skepticism in public opinion rather than an indicator of the more traditional left-right political spectrum. In terms of prediction of vote shares in the election, forecasts generated...... variants of the FCVAR model considered are generally ranked as the top four models in terms of forecast accuracy, and the FCVAR model significantly outperforms both univariate fractional models and the standard cointegrated VAR (CVAR) model at all forecast horizons. The relative forecast improvement...

  1. Application of Classification Methods for Forecasting Mid-Term Power Load Patterns

    Science.gov (United States)

    Piao, Minghao; Lee, Heon Gyu; Park, Jin Hyoung; Ryu, Keun Ho

    Currently an automated methodology based on data mining techniques is presented for the prediction of customer load patterns in long duration load profiles. The proposed approach in this paper consists of three stages: (i) data preprocessing: noise or outlier is removed and the continuous attribute-valued features are transformed to discrete values, (ii) cluster analysis: k-means clustering is used to create load pattern classes and the representative load profiles for each class and (iii) classification: we evaluated several supervised learning methods in order to select a suitable prediction method. According to the proposed methodology, power load measured from AMR (automatic meter reading) system, as well as customer indexes, were used as inputs for clustering. The output of clustering was the classification of representative load profiles (or classes). In order to evaluate the result of forecasting load patterns, the several classification methods were applied on a set of high voltage customers of the Korea power system and derived class labels from clustering and other features are used as input to produce classifiers. Lastly, the result of our experiments was presented.

  2. Electricity Price Forecasting Based on AOSVR and Outlier Detection

    Institute of Scientific and Technical Information of China (English)

    Zhou Dianmin; Gao Lin; Gao Feng

    2005-01-01

    Electricity price is of the first consideration for all the participants in electric power market and its characteristics are related to both market mechanism and variation in the behaviors of market participants. It is necessary to build a real-time price forecasting model with adaptive capability; and because there are outliers in the price data, they should be detected and filtrated in training the forecasting model by regression method. In view of these points, this paper presents an electricity price forecasting method based on accurate on-line support vector regression (AOSVR) and outlier detection. Numerical testing results show that the method is effective in forecasting the electricity prices in electric power market.

  3. An Electrical Energy Consumption Monitoring and Forecasting System

    Directory of Open Access Journals (Sweden)

    J. L. Rojas-Renteria

    2016-10-01

    Full Text Available Electricity consumption is currently an issue of great interest for power companies that need an as much as accurate profile for controlling the installed systems but also for designing future expansions and alterations. Detailed monitoring has proved to be valuable for both power companies and consumers. Further, as smart grid technology is bound to result to increasingly flexible rates, an accurate forecast is bound to prove valuable in the future. In this paper, a monitoring and forecasting system is investigated. The monitoring system was installed in an actual building and the recordings were used to design and evaluate the forecasting system, based on an artificial neural network. Results show that the system can provide detailed monitoring and also an accurate forecast for a building’s consumption.

  4. Electricity production by hydro power plants: possibilities of forecasting

    International Nuclear Information System (INIS)

    Barkans, J.; Zicmane, I.

    2004-01-01

    Hydro energy accounts for 17% of global electricity production and is the most important source of renewable energies actively used today, being at the same time the least influential ecologically. Its only disadvantages is that this kind of energy is difficult to forecast, which hinders not only the planning of tariffs, year budgets and investments, but also contractual negotiations in particular month. The paper shows that the forecasting of hydro energy production can be linked to certain natural processes, namely, to the cyclic behaviour observed for water flows of the world's rivers. The authors propose a method according to which the forecasting procedure is performed using the data of observations as signals applied to special digital filters transforming the water flow process into integral and differential forms, which after appropriate treatment are expected again in usual water flow units. For this purpose the water flow integral function is to be divided, by means of spectral analysis, into 'low-frequency' (with a semi-period of 44 years) and 'high-frequency' (4-6 year semi-periods) components, which are of different origin. Each of them should be forecasted separately, with the following summation of the results. In the research it is shown that the cyclic fluctuations of world rivers' water flows are directly associated with variations in the Solar activity. (authors)

  5. Load forecasting method considering temperature effect for distribution network

    Directory of Open Access Journals (Sweden)

    Meng Xiao Fang

    2016-01-01

    Full Text Available To improve the accuracy of load forecasting, the temperature factor was introduced into the load forecasting in this paper. This paper analyzed the characteristics of power load variation, and researched the rule of the load with the temperature change. Based on the linear regression analysis, the mathematical model of load forecasting was presented with considering the temperature effect, and the steps of load forecasting were given. Used MATLAB, the temperature regression coefficient was calculated. Using the load forecasting model, the full-day load forecasting and time-sharing load forecasting were carried out. By comparing and analyzing the forecast error, the results showed that the error of time-sharing load forecasting method was small in this paper. The forecasting method is an effective method to improve the accuracy of load forecasting.

  6. A Public-Private-Academic Partnership to Advance Solar Power Forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Marquis, Melinda [National Oceanic and Atmospheric Administration (NOAA), Boulder, CO (United States). Earth System Research Lab; Benjamin, Stan [National Oceanic and Atmospheric Administration (NOAA), Boulder, CO (United States). Earth System Research Lab; James, Eric [National Oceanic and Atmospheric Administration (NOAA), Boulder, CO (United States). Earth System Research Lab; Univ. of Colorado, Boulder, CO (United States); Lantz, kathy [National Oceanic and Atmospheric Administration (NOAA), Boulder, CO (United States). Earth System Research Lab; Univ. of Colorado, Boulder, CO (United States); Molling, Christine [National Oceanic and Atmospheric Administration (NOAA), Boulder, CO (United States). Earth System Research Lab; Univ. of Wisconsin, Madison, WI (United States)

    2015-04-30

    . Ground Observations In the ground-based measurement effort, NOAA's main objectives are to provide high quality radiation products for validation and verification of short-term to day-ahead solar forecasts. More specifically for the three year project, our goals include (1) Maintaining and providing data from our 7 SURFRAD and 7 ISIS; (2) Update ISIS radiation measurements from 3 min to 1 min data: (3) Purchase and install new pyrheliometers for direct solar irradiance measurements at the 7 SURFRAD sites; (4) Building, testing, and deploying two mobile SURFRAD stations at two utility plants in collaboration with DOE sponsored partners, and includes ongoing maintenance and processing of the data at the mobile sites; (5) Upgrading the data acquisition and communications at 7 SURFRAD sites and 7 ISIS sites; (6) Providing radiation data at the 7 SURFRAD sites in near real-time; (7) Develop and provide aerosol optical depth and cloud images and cloud fraction at our two mobile sites; (8) Provide data recovery rates each year; (9) Provide temporally and spatially averaged radiation products for comparison to HRRR and RAP solar forecasts and advanced satellite products; (10) Provide a data-set for analysis of conversion of direct and diffuse to sloped surfaces; (11) and as time permits develop and provide spectral solar irradiance, cloud optical depth and spectral albedo from the mobile sites. Milestones this year include working with the DOE sponsored teams to find locations to deploy two mobile SURFRAD stations. One existing unit was deployed at a 30MW PV facility in the San Luis Valley in collaboration with Xcel and the NCAR team in August, 2014. The second unit was built and tested at our facilities in Boulder, CO and deployed near Green Mountain Power's Education Center in Rutland, VT in collaboration with Green Mountain Power and the IBM Team in October, 2014. Data processing was implemented and the radiation data from these two mobile sites have been made

  7. Impact of Public Aggregate Wind Forecasts on Electricity Market Outcomes

    DEFF Research Database (Denmark)

    Exizidis, Lazaros; Kazempour, Jalal; Pinson, Pierre

    2017-01-01

    Following a call to foster a transparent and more competitive market, member states of the European transmission system operator are required to publish, among other information, aggregate wind power forecasts. The publication of the latter information is expected to benefit market participants...... by offering better knowledge of the market operation, leading subsequently to a more competitive energy market. Driven by the above regulation, we consider an equilibrium study to address how public information of aggregate wind power forecasts can potentially affect market results, social welfare as well...... as the profits of participating power producers. We investigate, therefore, a joint day-ahead energy and reserve auction, where producers offer their conventional power strategically based on a complementarity approach and their wind power at generation cost based on a forecast. In parallel, an iterative game...

  8. Concerning the justiciability of demand forecasts

    International Nuclear Information System (INIS)

    Nierhaus, M.

    1977-01-01

    This subject plays at present in particular a role in the course of judicial examinations of immediately enforceable orders for the partial construction licences of nuclear power plants. The author distinguishes beween three kinds of forecast decisions: 1. Appraising forecast decisions with standards of judgment taken mainly from the fields of the art, culture, morality, religion are, according to the author, only legally verifyable to a limited extent. 2. With regard to forecast decisions not arguable, e.g. where the future behaviour of persons is concerned, the same should be applied basically. 3. In contrast to this, the following is applicable for programmatic, proceedingslike, or creative forecast decisions, in particular in economics: 'An administrative estimation privilege in a prognostic sense with the consequence that the court has to accept the forecast decision which lies within the forecast margins and which cannot be disproved, and that the court may not replace this forecast decision by its own probability judgment. In these cases, administration has the right to create its own forecast standards.' Judicial control in these cases was limited to certain substantive and procedural mistakes made by the administration in the course of forecast decision finding. (orig./HP) [de

  9. Concerning the justiciability of demand forecasts

    Energy Technology Data Exchange (ETDEWEB)

    Nierhaus, M [Koeln Univ. (Germany, F.R.)

    1977-01-01

    This subject plays at present in particular a role in the course of judicial examinations of immediately enforceable orders for the partial construction licences of nuclear power plants. The author distinguishes beween three kinds of forecast decisions: 1. Appraising forecast decisions with standards of judgment taken mainly from the fields of the art, culture, morality, religion are, according to the author, only legally verifyable to a limited extent. 2. With regard to forecast decisions not arguable, e.g. where the future behaviour of persons is concerned, the same should be applied basically. 3. In contrast to this, the following is applicable for programmatic, proceedingslike, or creative forecast decisions, in particular in economics: 'An administrative estimation privilege in a prognostic sense with the consequence that the court has to accept the forecast decision which lies within the forecast margins and which cannot be disproved, and that the court may not replace this forecast decision by its own probability judgment. In these cases, administration has the right to create its own forecast standards.' Judicial control in these cases was limited to certain substantive and procedural mistakes made by the administration in the course of forecast decision finding.

  10. Reconstruction of the primordial power spectrum of curvature perturbations using multiple data sets

    DEFF Research Database (Denmark)

    Hunt, Paul; Sarkar, Subir

    2014-01-01

    Detailed knowledge of the primordial power spectrum of curvature perturbations is essential both in order to elucidate the physical mechanism (`inflation') which generated it, and for estimating the cosmological parameters from observations of the cosmic microwave background and large-scale struc......Detailed knowledge of the primordial power spectrum of curvature perturbations is essential both in order to elucidate the physical mechanism (`inflation') which generated it, and for estimating the cosmological parameters from observations of the cosmic microwave background and large...... content of the universe. Moreover the deconvolution problem is ill-conditioned so a regularisation scheme must be employed to control error propagation. We demonstrate that `Tikhonov regularisation' can robustly reconstruct the primordial spectrum from multiple cosmological data sets, a significant...... advantage being that both its uncertainty and resolution are then quantified. Using Monte Carlo simulations we investigate several regularisation parameter selection methods and find that generalised cross-validation and Mallow's Cp method give optimal results. We apply our inversion procedure to data from...

  11. Photovoltaics (PV System Energy Forecast on the Basis of the Local Weather Forecast: Problems, Uncertainties and Solutions

    Directory of Open Access Journals (Sweden)

    Kristijan Brecl

    2018-05-01

    Full Text Available When integrating a photovoltaic system into a smart zero-energy or energy-plus building, or just to lower the electricity bill by rising the share of the self-consumption in a private house, it is very important to have a photovoltaic power energy forecast for the next day(s. While the commercially available forecasting services might not meet the household prosumers interests due to the price or complexity we have developed a forecasting methodology that is based on the common weather forecast. Since the forecasted meteorological data does not include the solar irradiance information, but only the weather condition, the uncertainty of the results is relatively high. However, in the presented approach, irradiance is calculated from discrete weather conditions and with correlation of forecasted meteorological data, an RMS error of 65%, and a R2 correlation factor of 0.85 is feasible.

  12. Day-ahead price forecasting in restructured power systems using artificial neural networks

    International Nuclear Information System (INIS)

    Vahidinasab, V.; Jadid, S.; Kazemi, A.

    2008-01-01

    Over the past 15 years most electricity supply companies around the world have been restructured from monopoly utilities to deregulated competitive electricity markets. Market participants in the restructured electricity markets find short-term electricity price forecasting (STPF) crucial in formulating their risk management strategies. They need to know future electricity prices as their profitability depends on them. This research project classifies and compares different techniques of electricity price forecasting in the literature and selects artificial neural networks (ANN) as a suitable method for price forecasting. To perform this task, market knowledge should be used to optimize the selection of input data for an electricity price forecasting tool. Then sensitivity analysis is used in this research to aid in the selection of the optimum inputs of the ANN and fuzzy c-mean (FCM) algorithm is used for daily load pattern clustering. Finally, ANN with a modified Levenberg-Marquardt (LM) learning algorithm are implemented for forecasting prices in Pennsylvania-New Jersey-Maryland (PJM) market. The forecasting results were compared with the previous works and showed that the results are reasonable and accurate. (author)

  13. Synergizing two NWP models to improve hub-height wind speed forecasts

    Energy Technology Data Exchange (ETDEWEB)

    Liu, H. [Ortech International, Mississauga, ON (Canada); Taylor, P. [York Univ., Toronto, ON (Canada)

    2010-07-01

    This PowerPoint presentation discussed some of the methods used to optimize hub-height wind speed forecasts. Statistical and physical forecast paradigms were considered. Forecast errors are often dictated by phase error, while refined NWP modelling is limited by data availability. A nested meso-scale NWP model was combined with a physical model to predict wind and power forecasts. Maps of data sources were included as well as equations used to derive predictions. Data from meteorological masts located near the Great Lakes were used to demonstrate the model. The results were compared with other modelling prediction methods. Forecasts obtained using the modelling approach can help operators in scheduling and trading procedures. Further research is being conducted to determine if the model can be used to improve ramp forecasts. tabs., figs.

  14. Uncertainties in Forecasting Streamflow using Entropy Theory

    Science.gov (United States)

    Cui, H.; Singh, V. P.

    2017-12-01

    Streamflow forecasting is essential in river restoration, reservoir operation, power generation, irrigation, navigation, and water management. However, there is always uncertainties accompanied in forecast, which may affect the forecasting results and lead to large variations. Therefore, uncertainties must be considered and be assessed properly when forecasting streamflow for water management. The aim of our work is to quantify the uncertainties involved in forecasting streamflow and provide reliable streamflow forecast. Despite that streamflow time series are stochastic, they exhibit seasonal and periodic patterns. Therefore, streamflow forecasting entails modeling seasonality, periodicity, and its correlation structure, and assessing uncertainties. This study applies entropy theory to forecast streamflow and measure uncertainties during the forecasting process. To apply entropy theory for streamflow forecasting, spectral analysis is combined to time series analysis, as spectral analysis can be employed to characterize patterns of streamflow variation and identify the periodicity of streamflow. That is, it permits to extract significant information for understanding the streamflow process and prediction thereof. Application of entropy theory for streamflow forecasting involves determination of spectral density, determination of parameters, and extension of autocorrelation function. The uncertainties brought by precipitation input, forecasting model and forecasted results are measured separately using entropy. With information theory, how these uncertainties transported and aggregated during these processes will be described.

  15. Short-term Probabilistic Load Forecasting with the Consideration of Human Body Amenity

    Directory of Open Access Journals (Sweden)

    Ning Lu

    2013-02-01

    Full Text Available Load forecasting is the basis of power system planning and design. It is important for the economic operation and reliability assurance of power system. However, the results of load forecasting given by most existing methods are deterministic. This study aims at probabilistic load forecasting. First, the support vector machine regression is used to acquire the deterministic results of load forecasting with the consideration of human body amenity. Then the probabilistic load forecasting at a certain confidence level is given after the analysis of error distribution law corresponding to certain heat index interval. The final simulation shows that this probabilistic forecasting method is easy to implement and can provide more information than the deterministic forecasting results, and thus is helpful for decision-makers to make reasonable decisions.

  16. On the market impact of wind energy forecasts

    International Nuclear Information System (INIS)

    Jonsson, Tryggvi; Pinson, Pierre; Madsen, Henrik

    2010-01-01

    This paper presents an analysis of how day-ahead electricity spot prices are affected by day-ahead wind power forecasts. Demonstration of this relationship is given as a test case for the Western Danish price area of the Nord Pool's Elspot market. Impact on the average price behaviour is investigated as well as that on the distributional properties of the price. By using a non-parametric regression model to assess the effects of wind power forecasts on the average behaviour, the non-linearities and time variations in the relationship are captured well and the effects are shown to be quite substantial. Furthermore, by evaluating the distributional properties of the spot prices under different scenarios, the impact of the wind power forecasts on the price distribution is proved to be considerable. The conditional price distribution is moreover shown to be non-Gaussian. This implies that forecasting models for electricity spot prices for which parameters are estimated by a least squares techniques will not have Gaussian residuals. Hence the widespread assumption of Gaussian residuals from electricity spot price models is shown to be inadequate for these model types. The revealed effects are likely to be observable and qualitatively similar in other day-ahead electricity markets significantly penetrated by wind power. (author)

  17. A New Approach to Forecasting Exchange Rates

    OpenAIRE

    Kenneth W Clements; Yihui Lan

    2006-01-01

    Building on purchasing power parity theory, this paper proposes a new approach to forecasting exchange rates using the Big Mac data from The Economist magazine. Our approach is attractive in three aspects. Firstly, it uses easily-available Big Mac prices as input. These prices avoid several serious problems associated with broad price indexes, such as the CPI, that are used in conventional PPP studies. Secondly, this approach provides real-time exchange-rate forecasts at any forecast horizon....

  18. Climatological attribution of wind power ramp events in East Japan and their probabilistic forecast based on multi-model ensembles downscaled by analog ensemble using self-organizing maps

    Science.gov (United States)

    Ohba, Masamichi; Nohara, Daisuke; Kadokura, Shinji

    2016-04-01

    Severe storms or other extreme weather events can interrupt the spin of wind turbines in large scale that cause unexpected "wind ramp events". In this study, we present an application of self-organizing maps (SOMs) for climatological attribution of the wind ramp events and their probabilistic prediction. The SOM is an automatic data-mining clustering technique, which allows us to summarize a high-dimensional data space in terms of a set of reference vectors. The SOM is applied to analyze and connect the relationship between atmospheric patterns over Japan and wind power generation. SOM is employed on sea level pressure derived from the JRA55 reanalysis over the target area (Tohoku region in Japan), whereby a two-dimensional lattice of weather patterns (WPs) classified during the 1977-2013 period is obtained. To compare with the atmospheric data, the long-term wind power generation is reconstructed by using a high-resolution surface observation network AMeDAS (Automated Meteorological Data Acquisition System) in Japan. Our analysis extracts seven typical WPs, which are linked to frequent occurrences of wind ramp events. Probabilistic forecasts to wind power generation and ramps are conducted by using the obtained SOM. The probability are derived from the multiple SOM lattices based on the matching of output from TIGGE multi-model global forecast to the WPs on the lattices. Since this method effectively takes care of the empirical uncertainties from the historical data, wind power generation and ramp is probabilistically forecasted from the forecasts of global models. The predictability skill of the forecasts for the wind power generation and ramp events show the relatively good skill score under the downscaling technique. It is expected that the results of this study provides better guidance to the user community and contribute to future development of system operation model for the transmission grid operator.

  19. Short-Term Photovoltaic Power Generation Forecasting Based on Multivariable Grey Theory Model with Parameter Optimization

    Directory of Open Access Journals (Sweden)

    Zhifeng Zhong

    2017-01-01

    Full Text Available Owing to the environment, temperature, and so forth, photovoltaic power generation volume is always fluctuating and subsequently impacts power grid planning and operation seriously. Therefore, it is of great importance to make accurate prediction of the power generation of photovoltaic (PV system in advance. In order to improve the prediction accuracy, in this paper, a novel particle swarm optimization algorithm based multivariable grey theory model is proposed for short-term photovoltaic power generation volume forecasting. It is highlighted that, by integrating particle swarm optimization algorithm, the prediction accuracy of grey theory model is expected to be highly improved. In addition, large amounts of real data from two separate power stations in China are being employed for model verification. The experimental results indicate that, compared with the conventional grey model, the mean relative error in the proposed model has been reduced from 7.14% to 3.53%. The real practice demonstrates that the proposed optimization model outperforms the conventional grey model from both theoretical and practical perspectives.

  20. Planck intermediate results LI. Features in the cosmic microwave background temperature power spectrum and shifts in cosmological parameters

    DEFF Research Database (Denmark)

    Aghanim, N.; Akrami, Y.; Ashdown, M.

    2017-01-01

    The six parameters of the standard ΛCDM model have best-fit values derived from the Planck temperature power spectrum that are shifted somewhat from the best-fit values derived from WMAP data. These shifts are driven by features in the Planck temperature power spectrum at angular scales that had ...

  1. Local region power spectrum-based unfocused ship detection method in synthetic aperture radar images

    Science.gov (United States)

    Wei, Xiangfei; Wang, Xiaoqing; Chong, Jinsong

    2018-01-01

    Ships on synthetic aperture radar (SAR) images will be severely defocused and their energy will disperse into numerous resolution cells under long SAR integration time. Therefore, the image intensity of ships is weak and sometimes even overwhelmed by sea clutter on SAR image. Consequently, it is hard to detect the ships from SAR intensity images. A ship detection method based on local region power spectrum of SAR complex image is proposed. Although the energies of the ships are dispersed on SAR intensity images, their spectral energies are rather concentrated or will cause the power spectra of local areas of SAR images to deviate from that of sea surface background. Therefore, the key idea of the proposed method is to detect ships via the power spectra distortion of local areas of SAR images. The local region power spectrum of a moving target on SAR image is analyzed and the way to obtain the detection threshold through the probability density function (pdf) of the power spectrum is illustrated. Numerical P- and L-band airborne SAR ocean data are utilized and the detection results are also illustrated. Results show that the proposed method can well detect the unfocused ships, with a detection rate of 93.6% and a false-alarm rate of 8.6%. Moreover, by comparing with some other algorithms, it indicates that the proposed method performs better under long SAR integration time. Finally, the applicability of the proposed method and the way of parameters selection are also discussed.

  2. A Wind Forecasting System for Energy Application

    Science.gov (United States)

    Courtney, Jennifer; Lynch, Peter; Sweeney, Conor

    2010-05-01

    Accurate forecasting of available energy is crucial for the efficient management and use of wind power in the national power grid. With energy output critically dependent upon wind strength there is a need to reduce the errors associated wind forecasting. The objective of this research is to get the best possible wind forecasts for the wind energy industry. To achieve this goal, three methods are being applied. First, a mesoscale numerical weather prediction (NWP) model called WRF (Weather Research and Forecasting) is being used to predict wind values over Ireland. Currently, a gird resolution of 10km is used and higher model resolutions are being evaluated to establish whether they are economically viable given the forecast skill improvement they produce. Second, the WRF model is being used in conjunction with ECMWF (European Centre for Medium-Range Weather Forecasts) ensemble forecasts to produce a probabilistic weather forecasting product. Due to the chaotic nature of the atmosphere, a single, deterministic weather forecast can only have limited skill. The ECMWF ensemble methods produce an ensemble of 51 global forecasts, twice a day, by perturbing initial conditions of a 'control' forecast which is the best estimate of the initial state of the atmosphere. This method provides an indication of the reliability of the forecast and a quantitative basis for probabilistic forecasting. The limitation of ensemble forecasting lies in the fact that the perturbed model runs behave differently under different weather patterns and each model run is equally likely to be closest to the observed weather situation. Models have biases, and involve assumptions about physical processes and forcing factors such as underlying topography. Third, Bayesian Model Averaging (BMA) is being applied to the output from the ensemble forecasts in order to statistically post-process the results and achieve a better wind forecasting system. BMA is a promising technique that will offer calibrated

  3. Project 'WINDBANK mittleres Aaretal' - Analysis, Diagnosis and Forecast of Wind Fields around the Nuclear Power Plant Goesgen

    International Nuclear Information System (INIS)

    Graber, W. K.; Tinguely, M.

    2002-07-01

    An emergency decision support system for accidental releases of radioactivity into the atmosphere providing regional wind field information is presented. This system is based on intensive meteorological field campaigns each lasting 3-4 months in the regions around the Swiss nuclear power plants. The wind data from temporary and permanent stations are analysed to evaluate the typical wind field patterns occurring in these regions. A cluster analysis for these data-sets lead to 12 different wind field classes with a high separation quality. In the present report, it is demonstrated that an on-line acquisition of meteorological data from existing permanent stations is enough to diagnose the recent wind field class in a region with a radius of 25 km around the nuclear power station of Goesgen with a probability of 95% to hit the correct class. Furthermore, a method is presented to use a high resolution weather prediction model to forecast the future wind field classes. An average probability of 76% to hit the correct class for a forecast time of 24 hours is evaluated. Finally, a method for parameterization of turbulence providing input for dispersion models from standard meteorological online data is presented. (author)

  4. Wind-Farm Forecasting Using the HARMONIE Weather Forecast Model and Bayes Model Averaging for Bias Removal.

    Science.gov (United States)

    O'Brien, Enda; McKinstry, Alastair; Ralph, Adam

    2015-04-01

    Building on previous work presented at EGU 2013 (http://www.sciencedirect.com/science/article/pii/S1876610213016068 ), more results are available now from a different wind-farm in complex terrain in southwest Ireland. The basic approach is to interpolate wind-speed forecasts from an operational weather forecast model (i.e., HARMONIE in the case of Ireland) to the precise location of each wind-turbine, and then use Bayes Model Averaging (BMA; with statistical information collected from a prior training-period of e.g., 25 days) to remove systematic biases. Bias-corrected wind-speed forecasts (and associated power-generation forecasts) are then provided twice daily (at 5am and 5pm) out to 30 hours, with each forecast validation fed back to BMA for future learning. 30-hr forecasts from the operational Met Éireann HARMONIE model at 2.5km resolution have been validated against turbine SCADA observations since Jan. 2014. An extra high-resolution (0.5km grid-spacing) HARMONIE configuration has been run since Nov. 2014 as an extra member of the forecast "ensemble". A new version of HARMONIE with extra filters designed to stabilize high-resolution configurations has been run since Jan. 2015. Measures of forecast skill and forecast errors will be provided, and the contributions made by the various physical and computational enhancements to HARMONIE will be quantified.

  5. Just enough inflation. Power spectrum modifications at large scales

    International Nuclear Information System (INIS)

    Cicoli, Michele; Downes, Sean

    2014-07-01

    We show that models of 'just enough' inflation, where the slow-roll evolution lasted only 50-60 e-foldings, feature modifications of the CMB power spectrum at large angular scales. We perform a systematic and model-independent analysis of any possible non-slow-roll background evolution prior to the final stage of slow-roll inflation. We find a high degree of universality since most common backgrounds like fast-roll evolution, matter or radiation-dominance give rise to a power loss at large angular scales and a peak together with an oscillatory behaviour at scales around the value of the Hubble parameter at the beginning of slow-roll inflation. Depending on the value of the equation of state parameter, different pre-inflationary epochs lead instead to an enhancement of power at low-l, and so seem disfavoured by recent observational hints for a lack of CMB power at l< or similar 40. We also comment on the importance of initial conditions and the possibility to have multiple pre-inflationary stages.

  6. Non-parametric probabilistic forecasts of wind power: required properties and evaluation

    DEFF Research Database (Denmark)

    Pinson, Pierre; Nielsen, Henrik Aalborg; Møller, Jan Kloppenborg

    2007-01-01

    of a single or a set of quantile forecasts. The required and desirable properties of such probabilistic forecasts are defined and a framework for their evaluation is proposed. This framework is applied for evaluating the quality of two statistical methods producing full predictive distributions from point...

  7. Matter power spectrum and the challenge of percent accuracy

    OpenAIRE

    Schneider, Aurel; Teyssier, Romain; Potter, Doug; Stadel, Joachim; Onions, Julian; Reed, Darren S.; Smith, Robert E.; Springel, Volker; Pearce, Frazer R.; Scoccimarro, Roman

    2015-01-01

    Future galaxy surveys require one percent precision in the theoretical knowledge of the power spectrum over a large range including very nonlinear scales. While this level of accuracy is easily obtained in the linear regime with perturbation theory, it represents a serious challenge for small scales where numerical simulations are required. In this paper we quantify the precision of present-day $N$-body methods, identifying main potential error sources from the set-up of initial conditions to...

  8. Geothermal wells: a forecast of drilling activity

    Energy Technology Data Exchange (ETDEWEB)

    Brown, G.L.; Mansure, A.J.; Miewald, J.N.

    1981-07-01

    Numbers and problems for geothermal wells expected to be drilled in the United States between 1981 and 2000 AD are forecasted. The 3800 wells forecasted for major electric power projects (totaling 6 GWe of capacity) are categorized by type (production, etc.), and by location (The Geysers, etc.). 6000 wells are forecasted for direct heat projects (totaling 0.02 Quads per year). Equations are developed for forecasting the number of wells, and data is presented. Drilling and completion problems in The Geysers, The Imperial Valley, Roosevelt Hot Springs, the Valles Caldera, northern Nevada, Klamath Falls, Reno, Alaska, and Pagosa Springs are discussed. Likely areas for near term direct heat projects are identified.

  9. Unbiased contaminant removal for 3D galaxy power spectrum measurements

    Science.gov (United States)

    Kalus, B.; Percival, W. J.; Bacon, D. J.; Samushia, L.

    2016-11-01

    We assess and develop techniques to remove contaminants when calculating the 3D galaxy power spectrum. We separate the process into three separate stages: (I) removing the contaminant signal, (II) estimating the uncontaminated cosmological power spectrum and (III) debiasing the resulting estimates. For (I), we show that removing the best-fitting contaminant (mode subtraction) and setting the contaminated components of the covariance to be infinite (mode deprojection) are mathematically equivalent. For (II), performing a quadratic maximum likelihood (QML) estimate after mode deprojection gives an optimal unbiased solution, although it requires the manipulation of large N_mode^2 matrices (Nmode being the total number of modes), which is unfeasible for recent 3D galaxy surveys. Measuring a binned average of the modes for (II) as proposed by Feldman, Kaiser & Peacock (FKP) is faster and simpler, but is sub-optimal and gives rise to a biased solution. We present a method to debias the resulting FKP measurements that does not require any large matrix calculations. We argue that the sub-optimality of the FKP estimator compared with the QML estimator, caused by contaminants, is less severe than that commonly ignored due to the survey window.

  10. Structured, Physically Inspired (Gray Box) Models Versus Black Box Modeling for Forecasting the Output Power of Photovoltaic Plants

    Czech Academy of Sciences Publication Activity Database

    Paulescu, M.; Brabec, Marek; Boata, R.; Badescu, V.

    2017-01-01

    Roč. 121, 15 February (2017), s. 792-802 ISSN 0360-5442 Grant - others:European Cooperation in Science and Technology(XE) COST ES1002 Institutional support: RVO:67985807 Keywords : photovoltaic plant * output power * forecasting * fuzzy model * generalized additive model Subject RIV: BB - Applied Statistics, Operational Research OBOR OECD: Statistics and probability Impact factor: 4.520, year: 2016

  11. Electrical Load Survey and Forecast for a Decentralized Hybrid ...

    African Journals Online (AJOL)

    Electrical Load Survey and Forecast for a Decentralized Hybrid Power System at Elebu, Kwara State, Nigeria. ... Nigerian Journal of Technology ... The paper reports the results of electrical load demand and forecast for Elebu rural community ...

  12. The matter power spectrum in redshift space using effective field theory

    Science.gov (United States)

    Fonseca de la Bella, Lucía; Regan, Donough; Seery, David; Hotchkiss, Shaun

    2017-11-01

    The use of Eulerian 'standard perturbation theory' to describe mass assembly in the early universe has traditionally been limited to modes with k lesssim 0.1 h/Mpc at z=0. At larger k the SPT power spectrum deviates from measurements made using N-body simulations. Recently, there has been progress in extending the reach of perturbation theory to larger k using ideas borrowed from effective field theory. We revisit the computation of the redshift-space matter power spectrum within this framework, including for the first time the full one-loop time dependence. We use a resummation scheme proposed by Vlah et al. to account for damping of baryonic acoustic oscillations due to large-scale random motions and show that this has a significant effect on the multipole power spectra. We renormalize by comparison to a suite of custom N-body simulations matching the MultiDark MDR1 cosmology. At z=0 and for scales k lesssim 0.4 h/Mpc we find that the EFT furnishes a description of the real-space power spectrum up to ~ 2%, for the l = 0 mode up to ~ 5%, and for the l = 2, 4 modes up to ~ 25%. We argue that, in the MDR1 cosmology, positivity of the l=0 mode gives a firm upper limit of k ≈ 0.74 h/Mpc for the validity of the one-loop EFT prediction in redshift space using only the lowest-order counterterm. We show that replacing the one-loop growth factors by their Einstein-de Sitter counterparts is a good approximation for the l=0 mode, but can induce deviations as large as 2% for the l=2, 4 modes. An accompanying software bundle, distributed under open source licenses, includes Mathematica notebooks describing the calculation, together with parallel pipelines capable of computing both the necessary one-loop SPT integrals and the effective field theory counterterms.

  13. A fuzzy inference model for short-term load forecasting

    International Nuclear Information System (INIS)

    Mamlook, Rustum; Badran, Omar; Abdulhadi, Emad

    2009-01-01

    This paper is concerned with the short-term load forecasting (STLF) in power system operations. It provides load prediction for generation scheduling and unit commitment decisions, and therefore precise load forecasting plays an important role in reducing the generation cost and the spinning reserve capacity. Short-term electricity demand forecasting (i.e., the prediction of hourly loads (demand)) is one of the most important tools by which an electric utility/company plans, dispatches the loading of generating units in order to meet system demand. The accuracy of the dispatching system, which is derived from the accuracy of the forecasting algorithm used, will determine the economics of the operation of the power system. The inaccuracy or large error in the forecast simply means that load matching is not optimized and consequently the generation and transmission systems are not being operated in an efficient manner. In the present study, a proposed methodology has been introduced to decrease the forecasted error and the processing time by using fuzzy logic controller on an hourly base. Therefore, it predicts the effect of different conditional parameters (i.e., weather, time, historical data, and random disturbances) on load forecasting in terms of fuzzy sets during the generation process. These parameters are chosen with respect to their priority and importance. The forecasted values obtained by fuzzy method were compared with the conventionally forecasted ones. The results showed that the STLF of the fuzzy implementation have more accuracy and better outcomes

  14. Satellite fixed communications service: A forecast of potential domestic demand through the year 2000. Volume 3: Appendices

    Science.gov (United States)

    Kratochvil, D.; Bowyer, J.; Bhushan, C.; Steinnagel, K.; Kaushal, D.; Al-Kinani, G.

    1983-09-01

    Voice applications, data applications, video applications, impacted baseline forecasts, market distribution model, net long haul forecasts, trunking earth station definition and costs, trunking space segment cost, trunking entrance/exit links, trunking network costs and crossover distances with terrestrial tariffs, net addressable forecasts, capacity requirements, improving spectrum utilization, satellite system market development, and the 30/20 net accessible market are considered.

  15. The full-sky relativistic correlation function and power spectrum of galaxy number counts. Part I: theoretical aspects

    Science.gov (United States)

    Tansella, Vittorio; Bonvin, Camille; Durrer, Ruth; Ghosh, Basundhara; Sellentin, Elena

    2018-03-01

    We derive an exact expression for the correlation function in redshift shells including all the relativistic contributions. This expression, which does not rely on the distant-observer or flat-sky approximation, is valid at all scales and includes both local relativistic corrections and integrated contributions, like gravitational lensing. We present two methods to calculate this correlation function, one which makes use of the angular power spectrum Cl(z1,z2) and a second method which evades the costly calculations of the angular power spectra. The correlation function is then used to define the power spectrum as its Fourier transform. In this work theoretical aspects of this procedure are presented, together with quantitative examples. In particular, we show that gravitational lensing modifies the multipoles of the correlation function and of the power spectrum by a few percent at redshift z=1 and by up to 30% and more at z=2. We also point out that large-scale relativistic effects and wide-angle corrections generate contributions of the same order of magnitude and have consequently to be treated in conjunction. These corrections are particularly important at small redshift, z=0.1, where they can reach 10%. This means in particular that a flat-sky treatment of relativistic effects, using for example the power spectrum, is not consistent.

  16. Review on Forecast Methods for Photovoltaic Power Generation%太阳能光伏发电量预报方法的发展

    Institute of Scientific and Technical Information of China (English)

    李芬; 陈正洪; 成驰; 段善旭

    2011-01-01

    Solar photovoltaic technology is becoming one of the hot issues in the field of renewable energy generation.In future, China's large-scale grid-connected photovoltaic power generation system will be continuously in rapid development. But, so far, the exploring of photovoltaic power generation forecasting is still weak, and there are few methods available to meet the practical needs ofphotovoltaic power generation prediction in China. Photovoltaic power generation prediction means to accurately predict solar irradiances at first, and then in combination with the analysis of the historic power generation data of photovoltaic power station, to further forecast photovoltaic power.In this paper, we briefly introduce and classify several types ofphotovoltaic power generation forecasting models,such as the simulation-prediction method based on global solar radiation prediction and photovoltaic simulator, the physical prediction method based on global solar radiation prediction and photoelectric conversion efficiency model,the statistic-dynamic method based on the meteorological data and photoelectric power generation data processing and numerical weather prediction. Meanwhile, we also simply introduce photovoltaic power generation forecasting platform's construction abroad.%太阳能光伏发电技术成为当今世界可再生能源发电领域的一个研究热点.在未来,我国大规模的并网光伏发电系统将持续快速发展,但目前我国对太阳能光伏发电量预报方法的研究还很薄弱,几乎没有可满足实际太阳能光伏发电量预报需求的方法和系统.太阳能光伏发电量预报,主要是通过太阳总辐射的准确预报,结合光伏电站历史发电量数据分析,进而得到光伏发电量预报.通过对国内外太阳能光伏发电量预报方法的介绍和分类,以及对国际上太阳能光伏发电量预报系统建设的介绍,希望对我国太阳能光伏发电量预报系统发展起到一定的促进和推动作用.

  17. arXiv Neutrino masses and cosmology with Lyman-alpha forest power spectrum

    CERN Document Server

    Palanque-Delabrouille, Nathalie; Baur, Julien; Magneville, Christophe; Rossi, Graziano; Lesgourgues, Julien; Borde, Arnaud; Burtin, Etienne; LeGoff, Jean-Marc; Rich, James; Viel, Matteo; Weinberg, David

    2015-11-06

    We present constraints on neutrino masses, the primordial fluctuation spectrum from inflation, and other parameters of the $\\Lambda$CDM model, using the one-dimensional Ly$\\alpha$-forest power spectrum measured by Palanque-Delabrouille et al. (2013) from SDSS-III/BOSS, complemented by Planck 2015 cosmic microwave background (CMB) data and other cosmological probes. This paper improves on the previous analysis by Palanque-Delabrouille et al. (2015) by using a more powerful set of calibrating hydrodynamical simulations that reduces uncertainties associated with resolution and box size, by adopting a more flexible set of nuisance parameters for describing the evolution of the intergalactic medium, by including additional freedom to account for systematic uncertainties, and by using Planck 2015 constraints in place of Planck 2013. Fitting Ly$\\alpha$ data alone leads to cosmological parameters in excellent agreement with the values derived independently from CMB data, except for a weak tension on the scalar index ...

  18. Spatial correlation in 3D MIMO channels using fourier coefficients of power spectrums

    KAUST Repository

    Nadeem, Qurrat-Ul-Ain; Kammoun, Abla; Debbah, Mé rouane; Alouini, Mohamed-Slim

    2015-01-01

    for arbitrary angular distributions and antenna patterns. The resulting expression depends on the underlying angular distributions and antenna patterns through the Fourier Series (FS) coefficients of power azimuth and elevation spectrums. The novelty

  19. Forecasting metal prices: Do forecasters herd?

    DEFF Research Database (Denmark)

    Pierdzioch, C.; Rulke, J. C.; Stadtmann, G.

    2013-01-01

    We analyze more than 20,000 forecasts of nine metal prices at four different forecast horizons. We document that forecasts are heterogeneous and report that anti-herding appears to be a source of this heterogeneity. Forecaster anti-herding reflects strategic interactions among forecasters...

  20. Short-term load forecasting by a neuro-fuzzy based approach

    Energy Technology Data Exchange (ETDEWEB)

    Ruey-Hsun Liang; Ching-Chi Cheng [National Yunlin University of Science and Technology (China). Dept. of Electrical Engineering

    2002-02-01

    An approach based on an artificial neural network (ANN) combined with a fuzzy system is proposed for short-term load forecasting. This approach was developed in order to reach the desired short-term load forecasting in an efficient manner. Over the past few years, ANNs have attained the ability to manage a great deal of system complexity and are now being proposed as powerful computational tools. In order to select the appropriate load as the input for the desired forecasting, the Pearson analysis method is first applied to choose two historical record load patterns that are similar to the forecasted load pattern. These two load patterns and the required weather parameters are then fuzzified and input into a neural network for training or testing the network. The back-propagation (BP) neural network is applied to determine the preliminary forecasted load. In addition, the rule base for the fuzzy inference machine contains important linguistic membership function terms with knowledge in the form of fuzzy IF-THEN rules. This produces the load correction inference from the historical information and past forecasted load errors to obtain an inferred load error. Adding the inferred load error to the preliminary forecasted load, we can obtain the finial forecasted load. The effectiveness of the proposed approach to the short-term load-forecasting problem is demonstrated using practical data from the Taiwan Power Company (TPC). (Author)

  1. Valuing hydrological forecasts for a pumped storage assisted hydro facility

    Science.gov (United States)

    Zhao, Guangzhi; Davison, Matt

    2009-07-01

    SummaryThis paper estimates the value of a perfectly accurate short-term hydrological forecast to the operator of a hydro electricity generating facility which can sell its power at time varying but predictable prices. The expected value of a less accurate forecast will be smaller. We assume a simple random model for water inflows and that the costs of operating the facility, including water charges, will be the same whether or not its operator has inflow forecasts. Thus, the improvement in value from better hydrological prediction results from the increased ability of the forecast using facility to sell its power at high prices. The value of the forecast is therefore the difference between the sales of a facility operated over some time horizon with a perfect forecast, and the sales of a similar facility operated over the same time horizon with similar water inflows which, though governed by the same random model, cannot be forecast. This paper shows that the value of the forecast is an increasing function of the inflow process variance and quantifies how much the value of this perfect forecast increases with the variance of the water inflow process. Because the lifetime of hydroelectric facilities is long, the small increase observed here can lead to an increase in the profitability of hydropower investments.

  2. Slow-roll inflation and BB-mode angular power spectrum of CMB

    Energy Technology Data Exchange (ETDEWEB)

    Malsawmtluangi, N.; Suresh, P.K. [University of Hyderabad, School of Physics, Hyderabad (India)

    2016-05-15

    The BB-mode correlation angular power spectrum of CMB is obtained by considering the primordial gravitational waves in the squeezed vacuum state for various inflationary models and results are compared with the joint analysis of the BICEP2/Keck Array and Planck 353 GHz data. The present results may constrain several models of inflation. (orig.)

  3. Price forecasting of day-ahead electricity markets using a hybrid forecast method

    International Nuclear Information System (INIS)

    Shafie-khah, M.; Moghaddam, M. Parsa; Sheikh-El-Eslami, M.K.

    2011-01-01

    Research highlights: → A hybrid method is proposed to forecast the day-ahead prices in electricity market. → The method combines Wavelet-ARIMA and RBFN network models. → PSO method is applied to obtain optimum RBFN structure for avoiding over fitting. → One of the merits of the proposed method is lower need to the input data. → The proposed method has more accurate behavior in compare with previous methods. -- Abstract: Energy price forecasting in a competitive electricity market is crucial for the market participants in planning their operations and managing their risk, and it is also the key information in the economic optimization of the electric power industry. However, price series usually have a complex behavior due to their nonlinearity, nonstationarity, and time variancy. In this paper, a novel hybrid method to forecast day-ahead electricity price is proposed. This hybrid method is based on wavelet transform, Auto-Regressive Integrated Moving Average (ARIMA) models and Radial Basis Function Neural Networks (RBFN). The wavelet transform provides a set of better-behaved constitutive series than price series for prediction. ARIMA model is used to generate a linear forecast, and then RBFN is developed as a tool for nonlinear pattern recognition to correct the estimation error in wavelet-ARIMA forecast. Particle Swarm Optimization (PSO) is used to optimize the network structure which makes the RBFN be adapted to the specified training set, reducing computation complexity and avoiding overfitting. The proposed method is examined on the electricity market of mainland Spain and the results are compared with some of the most recent price forecast methods. The results show that the proposed hybrid method could provide a considerable improvement for the forecasting accuracy.

  4. Price forecasting of day-ahead electricity markets using a hybrid forecast method

    Energy Technology Data Exchange (ETDEWEB)

    Shafie-khah, M., E-mail: miadreza@gmail.co [Tarbiat Modares University, Tehran (Iran, Islamic Republic of); Moghaddam, M. Parsa, E-mail: parsa@modares.ac.i [Tarbiat Modares University, Tehran (Iran, Islamic Republic of); Sheikh-El-Eslami, M.K., E-mail: aleslam@modares.ac.i [Tarbiat Modares University, Tehran (Iran, Islamic Republic of)

    2011-05-15

    Research highlights: {yields} A hybrid method is proposed to forecast the day-ahead prices in electricity market. {yields} The method combines Wavelet-ARIMA and RBFN network models. {yields} PSO method is applied to obtain optimum RBFN structure for avoiding over fitting. {yields} One of the merits of the proposed method is lower need to the input data. {yields} The proposed method has more accurate behavior in compare with previous methods. -- Abstract: Energy price forecasting in a competitive electricity market is crucial for the market participants in planning their operations and managing their risk, and it is also the key information in the economic optimization of the electric power industry. However, price series usually have a complex behavior due to their nonlinearity, nonstationarity, and time variancy. In this paper, a novel hybrid method to forecast day-ahead electricity price is proposed. This hybrid method is based on wavelet transform, Auto-Regressive Integrated Moving Average (ARIMA) models and Radial Basis Function Neural Networks (RBFN). The wavelet transform provides a set of better-behaved constitutive series than price series for prediction. ARIMA model is used to generate a linear forecast, and then RBFN is developed as a tool for nonlinear pattern recognition to correct the estimation error in wavelet-ARIMA forecast. Particle Swarm Optimization (PSO) is used to optimize the network structure which makes the RBFN be adapted to the specified training set, reducing computation complexity and avoiding overfitting. The proposed method is examined on the electricity market of mainland Spain and the results are compared with some of the most recent price forecast methods. The results show that the proposed hybrid method could provide a considerable improvement for the forecasting accuracy.

  5. ON THE CLUSTER PHYSICS OF SUNYAEV-ZEL'DOVICH AND X-RAY SURVEYS. II. DECONSTRUCTING THE THERMAL SZ POWER SPECTRUM

    International Nuclear Information System (INIS)

    Battaglia, N.; Bond, J. R.; Pfrommer, C.; Sievers, J. L.

    2012-01-01

    Secondary anisotropies in the cosmic microwave background are a treasure-trove of cosmological information. Interpreting current experiments probing them are limited by theoretical uncertainties rather than by measurement errors. Here we focus on the secondary anisotropies resulting from the thermal Sunyaev-Zel'dovich (tSZ) effect; the amplitude of which depends critically on the average thermal pressure profile of galaxy groups and clusters. To this end, we use a suite of hydrodynamical TreePM-SPH simulations that include radiative cooling, star formation, supernova feedback, and energetic feedback from active galactic nuclei. We examine in detail how the pressure profile depends on cluster radius, mass, and redshift and provide an empirical fitting function. We employ three different approaches for calculating the tSZ power spectrum: an analytical approach that uses our pressure profile fit, a semianalytical method of pasting our pressure fit onto simulated clusters, and a direct numerical integration of our simulated volumes. We demonstrate that the detailed structure of the intracluster medium and cosmic web affect the tSZ power spectrum. In particular, the substructure and asphericity of clusters increase the tSZ power spectrum by 10%-20% at l ∼ 2000-8000, with most of the additional power being contributed by substructures. The contributions to the power spectrum from radii larger than R 500 is ∼20% at l = 3000, thus clusters interiors (r 500 ) dominate the power spectrum amplitude at these angular scales.

  6. Measurements in support of wind farm simulations and power forecasts: The Crop/Wind-energy Experiments (CWEX)

    International Nuclear Information System (INIS)

    Takle, E S; Rajewski, D A; Lundquist, J K; Gallus, W A Jr; Sharma, A

    2014-01-01

    The Midwest US currently is experiencing a large build-out of wind turbines in areas where the nocturnal low-level jet (NLLJ) is a prominent and frequently occurring feature. We describe shear characteristics of the NLLJ and their influence on wind power production. Reports of individual turbine power production and concurrent measurements of near-surface thermal stratification are used to turbine wake interactions and turbine interaction with the overlying atmosphere. Progress in forecasting conditions such as wind ramps and shear are discussed. Finally, the pressure perturbation introduced by a line of turbines produces surface flow convergence that may create a vertical velocity and hence a mesoscale influence on cloud formation by a wind farm

  7. Visibility-based angular power spectrum estimation in low-frequency radio interferometric observations

    NARCIS (Netherlands)

    Choudhuri, Samir; Bharadwaj, Somnath; Ghosh, Abhik; Ali, Sk. Saiyad

    2014-01-01

    We present two estimators to quantify the angular power spectrum of the sky signal directly from the visibilities measured in radio interferometric observations. This is relevant for both the foregrounds and the cosmological 21-cm signal buried therein. The discussion here is restricted to the

  8. Prediction of speech intelligibility based on a correlation metric in the envelope power spectrum domain

    DEFF Research Database (Denmark)

    Relano-Iborra, Helia; May, Tobias; Zaar, Johannes

    A powerful tool to investigate speech perception is the use of speech intelligibility prediction models. Recently, a model was presented, termed correlation-based speechbased envelope power spectrum model (sEPSMcorr) [1], based on the auditory processing of the multi-resolution speech-based Envel...

  9. Spectrum resolving power of hearing: measurements, baselines, and influence of maskers

    Directory of Open Access Journals (Sweden)

    Alexander Ya. Supin

    2011-06-01

    Full Text Available Contemporary methods of measurement of frequency tuning in the auditory system are reviewed. Most of them are based on the frequency-selective masking paradigm and require multi-point measurements (a number of masked thresholds should be measured to obtain a single frequency-tuning estimate. Therefore, they are rarely used for practical needs. As an alternative approach, frequency-selective properties of the auditory system may be investigated using probes with complex frequency spectrum patterns, in particular, rippled noise that is characterized by a spectrum with periodically alternating maxima and minima. The maximal ripple density discriminated by the auditory system is  a convenient measure of the spectrum resolving power (SRP. To find the highest resolvable ripple density, a phase-reversal test has been suggested. Using this technique, normal SRP, its dependence on probe center frequency, spectrum contrast, and probe level were measured. The results were not entirely predictable by frequency-tuning data obtained by masking methods. SRP is influenced by maskers, with on- and off-frequency maskers influencing SRP very differently. Dichotic separation of the probe and masker results in almost complete release of SRP from influence of maskers.

  10. The Wind Forecast Improvement Project (WFIP): A Public/Private Partnership for Improving Short Term Wind Energy Forecasts and Quantifying the Benefits of Utility Operations. The Southern Study Area, Final Report

    Energy Technology Data Exchange (ETDEWEB)

    Freedman, Jeffrey M. [AWS Truepower, LLC, Albany, NY (United States); Manobianco, John [MESO, Inc., Troy, NY (United States); Schroeder, John [Texas Tech Univ., Lubbock, TX (United States). National Wind Inst.; Ancell, Brian [Texas Tech Univ., Lubbock, TX (United States). Atmospheric Science Group; Brewster, Keith [Univ. of Oklahoma, Norman, OK (United States). Center for Analysis and Prediction of Storms; Basu, Sukanta [North Carolina State Univ., Raleigh, NC (United States). Dept. of Marine, Earth, and Atmospheric Sciences; Banunarayanan, Venkat [ICF International (United States); Hodge, Bri-Mathias [National Renewable Energy Lab. (NREL), Golden, CO (United States); Flores, Isabel [Electricity Reliability Council of Texas (United States)

    2014-04-30

    This Final Report presents a comprehensive description, findings, and conclusions for the Wind Forecast Improvement Project (WFIP) -- Southern Study Area (SSA) work led by AWS Truepower (AWST). This multi-year effort, sponsored by the Department of Energy (DOE) and National Oceanographic and Atmospheric Administration (NOAA), focused on improving short-term (15-minute - 6 hour) wind power production forecasts through the deployment of an enhanced observation network of surface and remote sensing instrumentation and the use of a state-of-the-art forecast modeling system. Key findings from the SSA modeling and forecast effort include: 1. The AWST WFIP modeling system produced an overall 10 - 20% improvement in wind power production forecasts over the existing Baseline system, especially during the first three forecast hours; 2. Improvements in ramp forecast skill, particularly for larger up and down ramps; 3. The AWST WFIP data denial experiments showed mixed results in the forecasts incorporating the experimental network instrumentation; however, ramp forecasts showed significant benefit from the additional observations, indicating that the enhanced observations were key to the model systems’ ability to capture phenomena responsible for producing large short-term excursions in power production; 4. The OU CAPS ARPS simulations showed that the additional WFIP instrument data had a small impact on their 3-km forecasts that lasted for the first 5-6 hours, and increasing the vertical model resolution in the boundary layer had a greater impact, also in the first 5 hours; and 5. The TTU simulations were inconclusive as to which assimilation scheme (3DVAR versus EnKF) provided better forecasts, and the additional observations resulted in some improvement to the forecasts in the first 1 - 3 hours.

  11. Real-time data processing and inflow forecasting

    International Nuclear Information System (INIS)

    Olason, T.; Lafreniere, M.

    1998-01-01

    One of the key inputs into the short-term scheduling of hydroelectric generation is inflow forecasting which is needed for natural or unregulated inflows into various lakes, reservoirs and river sections. The forecast time step and time horizon are determined by the time step and the scheduling horizon. Acres International Ltd. has developed the Vista Decision Support System (DSS) in which the time step is one hour and the scheduling can be done up to two weeks into the future. This paper presents the basis of the operational flow-forecasting module of the Vista DSS software and its application to flow forecasting for 16 basins within Nova Scotia Power's hydroelectric system. Among the tasks performed by the software are collection and treatment of data (in real time) regarding meteorological forecasts, reviews and monitoring of hydro-meteorological data, updating of the state variables in the module, and the review and adjustment of sub-watershed forecasts

  12. Ensemble forecasting for renewable energy applications - status and current challenges for their generation and verification

    Science.gov (United States)

    Pinson, Pierre

    2016-04-01

    The operational management of renewable energy generation in power systems and electricity markets requires forecasts in various forms, e.g., deterministic or probabilistic, continuous or categorical, depending upon the decision process at hand. Besides, such forecasts may also be necessary at various spatial and temporal scales, from high temporal resolutions (in the order of minutes) and very localized for an offshore wind farm, to coarser temporal resolutions (hours) and covering a whole country for day-ahead power scheduling problems. As of today, weather predictions are a common input to forecasting methodologies for renewable energy generation. Since for most decision processes, optimal decisions can only be made if accounting for forecast uncertainties, ensemble predictions and density forecasts are increasingly seen as the product of choice. After discussing some of the basic approaches to obtaining ensemble forecasts of renewable power generation, it will be argued that space-time trajectories of renewable power production may or may not be necessitate post-processing ensemble forecasts for relevant weather variables. Example approaches and test case applications will be covered, e.g., looking at the Horns Rev offshore wind farm in Denmark, or gridded forecasts for the whole continental Europe. Eventually, we will illustrate some of the limitations of current frameworks to forecast verification, which actually make it difficult to fully assess the quality of post-processing approaches to obtain renewable energy predictions.

  13. Determination of the number of Vertical Axis Wind Turbine blades based on power spectrum

    Directory of Open Access Journals (Sweden)

    Fedak Waldemar

    2017-01-01

    Full Text Available Technology of wind exploitation has been applied widely all over the world and has already reached the level in which manufacturers want to maximize the yield with the minimum investment outlays. The main objective of this paper is the determination of the optimal number of blades in the Cup-Bladed Vertical Axis Wind Turbine. Optimizing the size of the Vertical Axis Wind Turbine allows the reduction of costs. The maximum power of the rotor is selected as the performance target. The optimum number of Vertical Axis Wind Turbine blades evaluation is based on analysis of a single blade simulation and its superposition for the whole rotor. The simulation of working blade was done in MatLab environment. Power spectrum graphs were prepared and compared throughout superposition of individual blades in the Vertical Axis Wind Turbine rotor. The major result of this research is the Vertical Axis Wind Turbine power characteristic. On the basis of the analysis of the power spectra, optimum number of the blades was specified for the analysed rotor. Power spectrum analysis of wind turbine enabled the specification of the optimal number of blades, and can be used regarding investment outlays and power output of the Vertical Axis Wind Turbine.

  14. Determination of the number of Vertical Axis Wind Turbine blades based on power spectrum

    Science.gov (United States)

    Fedak, Waldemar; Anweiler, Stanisław; Gancarski, Wojciech; Ulbrich, Roman

    2017-10-01

    Technology of wind exploitation has been applied widely all over the world and has already reached the level in which manufacturers want to maximize the yield with the minimum investment outlays. The main objective of this paper is the determination of the optimal number of blades in the Cup-Bladed Vertical Axis Wind Turbine. Optimizing the size of the Vertical Axis Wind Turbine allows the reduction of costs. The maximum power of the rotor is selected as the performance target. The optimum number of Vertical Axis Wind Turbine blades evaluation is based on analysis of a single blade simulation and its superposition for the whole rotor. The simulation of working blade was done in MatLab environment. Power spectrum graphs were prepared and compared throughout superposition of individual blades in the Vertical Axis Wind Turbine rotor. The major result of this research is the Vertical Axis Wind Turbine power characteristic. On the basis of the analysis of the power spectra, optimum number of the blades was specified for the analysed rotor. Power spectrum analysis of wind turbine enabled the specification of the optimal number of blades, and can be used regarding investment outlays and power output of the Vertical Axis Wind Turbine.

  15. Demand forecast of turbines in the offshore wind power industry

    DEFF Research Database (Denmark)

    Martinez-Neri, Ivan

    2014-01-01

    How important is it for a manufacturing company to be able to predict the demand of their products? How much will it lose in inventory costs due to a bad forecasting technique? And what if the product in question is composed of more than 100,000 parts and costs millions of euros a piece......? This article summarises the reasoning followed by a European manufacturer to determine the demand curve of finished offshore wind turbines and how to forecast it for the purpose of production planning....

  16. 基于HS-Clustering的风电场机组分组功率预测%Wind Power Forecasting for Clustering Wind Turbines Based on HS-Clustering

    Institute of Scientific and Technical Information of China (English)

    高小力; 张智博; 田启明; 刘永前

    2017-01-01

    为了寻求风电场功率预测精度和计算效率二者的平衡,提出了一种基于霍普金斯统计量与聚类算法(HS-Clustering)的风电场机组分组功率预测方法,该方法将霍普金斯统计量与聚类算法的优势有效结合,采用霍普金斯统计量确定场内机组分组个数,通过聚类算法识别不同机组的相似性将风电场分成不同的机组群,然后对每组机群分别建立功率预测模型,从而叠加得到整场输出功率;另外以实测风速、实测功率及二者组合作为机组分组模型输入,分析其对预测精度的影响程度.实例分析表明基于HS-Clustering的分组预测方法可以显著提高预测精度,同时保证较高的计算效率;风速是影响分组效果的主要因素,对于某些分组模型,功率又可以作为风速的重要补充.%In order to balance the forecast accuracy and computational efficiency, a wind power forecasting method for clustering wind turbines is proposed based on effective combination of Hopkins statistics (HS) and clustering methods, in which Hopkins Statistics is used to determine the clustering number of a wind farm, and wind turbines in a wind farm are clustered into several groups according to the identifying of similar characteristics by clustering method.Then power forecasting model of each clustering group is built separately, whose power output is added to obtain whole power output of the wind farm.In addition, the real-time monitoring wind speed, power output and their combination are taken as the inputs for clustered group model, and their influences on the accuracy of clustering forecast model are analyzed.The case analysis shows that the HS-Clustering based forecasting method can effectively forecast the output power of the whole wind farm with better accuracy and higher computational efficiency, wind speed is the main factor affecting clustering results, and wind power can be regarded as an important additional factor as to certain

  17. Power Flow Simulations of a More Renewable California Grid Utilizing Wind and Solar Insolation Forecasting

    Science.gov (United States)

    Hart, E. K.; Jacobson, M. Z.; Dvorak, M. J.

    2008-12-01

    Time series power flow analyses of the California electricity grid are performed with extensive addition of intermittent renewable power. The study focuses on the effects of replacing non-renewable and imported (out-of-state) electricity with wind and solar power on the reliability of the transmission grid. Simulations are performed for specific days chosen throughout the year to capture seasonal fluctuations in load, wind, and insolation. Wind farm expansions and new wind farms are proposed based on regional wind resources and time-dependent wind power output is calculated using a meteorological model and the power curves of specific wind turbines. Solar power is incorporated both as centralized and distributed generation. Concentrating solar thermal plants are modeled using local insolation data and the efficiencies of pre-existing plants. Distributed generation from rooftop PV systems is included using regional insolation data, efficiencies of common PV systems, and census data. The additional power output of these technologies offsets power from large natural gas plants and is balanced for the purposes of load matching largely with hydroelectric power and by curtailment when necessary. A quantitative analysis of the effects of this significant shift in the electricity portfolio of the state of California on power availability and transmission line congestion, using a transmission load-flow model, is presented. A sensitivity analysis is also performed to determine the effects of forecasting errors in wind and insolation on load-matching and transmission line congestion.

  18. Analyzing Effect of System Inertia on Grid Frequency Forecasting Usnig Two Stage Neuro-Fuzzy System

    Science.gov (United States)

    Chourey, Divyansh R.; Gupta, Himanshu; Kumar, Amit; Kumar, Jitesh; Kumar, Anand; Mishra, Anup

    2018-04-01

    Frequency forecasting is an important aspect of power system operation. The system frequency varies with load-generation imbalance. Frequency variation depends upon various parameters including system inertia. System inertia determines the rate of fall of frequency after the disturbance in the grid. Though, inertia of the system is not considered while forecasting the frequency of power system during planning and operation. This leads to significant errors in forecasting. In this paper, the effect of inertia on frequency forecasting is analysed for a particular grid system. In this paper, a parameter equivalent to system inertia is introduced. This parameter is used to forecast the frequency of a typical power grid for any instant of time. The system gives appreciable result with reduced error.

  19. Short-Term Wind Power Forecasting Based on Clustering Pre-Calculated CFD Method

    Directory of Open Access Journals (Sweden)

    Yimei Wang

    2018-04-01

    Full Text Available To meet the increasing wind power forecasting (WPF demands of newly built wind farms without historical data, physical WPF methods are widely used. The computational fluid dynamics (CFD pre-calculated flow fields (CPFF-based WPF is a promising physical approach, which can balance well the competing demands of computational efficiency and accuracy. To enhance its adaptability for wind farms in complex terrain, a WPF method combining wind turbine clustering with CPFF is first proposed where the wind turbines in the wind farm are clustered and a forecasting is undertaken for each cluster. K-means, hierarchical agglomerative and spectral analysis methods are used to establish the wind turbine clustering models. The Silhouette Coefficient, Calinski-Harabaz index and within-between index are proposed as criteria to evaluate the effectiveness of the established clustering models. Based on different clustering methods and schemes, various clustering databases are built for clustering pre-calculated CFD (CPCC-based short-term WPF. For the wind farm case studied, clustering evaluation criteria show that hierarchical agglomerative clustering has reasonable results, spectral clustering is better and K-means gives the best performance. The WPF results produced by different clustering databases also prove the effectiveness of the three evaluation criteria in turn. The newly developed CPCC model has a much higher WPF accuracy than the CPFF model without using clustering techniques, both on temporal and spatial scales. The research provides supports for both the development and improvement of short-term physical WPF systems.

  20. Impact of large-scale tides on cosmological distortions via redshift-space power spectrum

    Science.gov (United States)

    Akitsu, Kazuyuki; Takada, Masahiro

    2018-03-01

    Although large-scale perturbations beyond a finite-volume survey region are not direct observables, these affect measurements of clustering statistics of small-scale (subsurvey) perturbations in large-scale structure, compared with the ensemble average, via the mode-coupling effect. In this paper we show that a large-scale tide induced by scalar perturbations causes apparent anisotropic distortions in the redshift-space power spectrum of galaxies in a way depending on an alignment between the tide, wave vector of small-scale modes and line-of-sight direction. Using the perturbation theory of structure formation, we derive a response function of the redshift-space power spectrum to large-scale tide. We then investigate the impact of large-scale tide on estimation of cosmological distances and the redshift-space distortion parameter via the measured redshift-space power spectrum for a hypothetical large-volume survey, based on the Fisher matrix formalism. To do this, we treat the large-scale tide as a signal, rather than an additional source of the statistical errors, and show that a degradation in the parameter is restored if we can employ the prior on the rms amplitude expected for the standard cold dark matter (CDM) model. We also discuss whether the large-scale tide can be constrained at an accuracy better than the CDM prediction, if the effects up to a larger wave number in the nonlinear regime can be included.

  1. Measurement of Gamma Spectrum at domestic Nuclear Power Plant with CZT Semiconductor Detector

    Energy Technology Data Exchange (ETDEWEB)

    Kon, Kang Seo; Yoon, Kang Hwa; Lee, Byoung Il; Kim, Jeong In [KHNP, Radiation Health Research Institute, Seoul (Korea, Republic of)

    2013-10-15

    In this study we monitored gamma spectrum for young S/G to see difference of the detected nuclides between old and young S/G. The detected source terms were the same for all measurement points. There is not comparison of quantity among the nuclides. The program which analyzes gamma spectrum to calculate activity and dose rate is under developing. We expect it will be done by end of this year. In this study we could see the difference of detected nuclides between old and new S/G for the first time whereas last measurement has significant meaning in that the measurement was taken for the first time all over country. Monitoring sources terms at Nuclear Power Plant(NPP) is important to aggressive ALARA activities and evaluation of exposure of workers. EDF (Electricite de France) and AEP (American Electric Power) conduct monitoring source terms using by CZT semiconductor detector. CZT is different from HPGe in that it does not need any cooling system at room temperature, it has good energy resolution and it can be made portable type easily. For these reason CZT is used in various fields commercially to measure gamma ray and therefore KHNP(Korea Hydro and Nuclear Power Co., LTD) RHRI(Radiation Health Research Institute) has been measuring gamma spectrum at domestic NPP last spring. We had have presented the first result through the last Transactions of the Korean Nuclear Society Spring Meeting for old S/G(Steam Generator)

  2. Neutrino masses and cosmological parameters from a Euclid-like survey: Markov Chain Monte Carlo forecasts including theoretical errors

    CERN Document Server

    Audren, Benjamin; Bird, Simeon; Haehnelt, Martin G.; Viel, Matteo

    2013-01-01

    We present forecasts for the accuracy of determining the parameters of a minimal cosmological model and the total neutrino mass based on combined mock data for a future Euclid-like galaxy survey and Planck. We consider two different galaxy surveys: a spectroscopic redshift survey and a cosmic shear survey. We make use of the Monte Carlo Markov Chains (MCMC) technique and assume two sets of theoretical errors. The first error is meant to account for uncertainties in the modelling of the effect of neutrinos on the non-linear galaxy power spectrum and we assume this error to be fully correlated in Fourier space. The second error is meant to parametrize the overall residual uncertainties in modelling the non-linear galaxy power spectrum at small scales, and is conservatively assumed to be uncorrelated and to increase with the ratio of a given scale to the scale of non-linearity. It hence increases with wavenumber and decreases with redshift. With these two assumptions for the errors and assuming further conservat...

  3. Daily Peak Load Forecasting of Next Day using Weather Distribution and Comparison Value of Each Nearby Date Data

    Science.gov (United States)

    Ito, Shigenobu; Yukita, Kazuto; Goto, Yasuyuki; Ichiyanagi, Katsuhiro; Nakano, Hiroyuki

    By the development of industry, in recent years; dependence to electric energy is growing year by year. Therefore, reliable electric power supply is in need. However, to stock a huge amount of electric energy is very difficult. Also, there is a necessity to keep balance between the demand and supply, which changes hour after hour. Consequently, to supply the high quality and highly dependable electric power supply, economically, and with high efficiency, there is a need to forecast the movement of the electric power demand carefully in advance. And using that forecast as the source, supply and demand management plan should be made. Thus load forecasting is said to be an important job among demand investment of electric power companies. So far, forecasting method using Fuzzy logic, Neural Net Work, Regression model has been suggested for the development of forecasting accuracy. Those forecasting accuracy is in a high level. But to invest electric power in higher accuracy more economically, a new forecasting method with higher accuracy is needed. In this paper, to develop the forecasting accuracy of the former methods, the daily peak load forecasting method using the weather distribution of highest and lowest temperatures, and comparison value of each nearby date data is suggested.

  4. Account of the uncertainty factor in forecasting nuclear power development

    International Nuclear Information System (INIS)

    Chernavskij, S.Ya.

    1979-01-01

    Minimization of total discounted costs for linear constraints is commonly used in forecasting nuclear energy growth. This approach is considered inadequate due to the uncertainty of exogenous variables of the model. A method of forecasting that takes into account the presence of uncertainty is elaborated. An example that demonstrates the expediency of the method and its advantage over the conventional approximation method used for taking uncertainty into account is given. In the framework of the example, the optimal strategy for nuclear energy growth over period of 500 years is determined

  5. Demand forecasting: methodology used to electric power consumers for irrigation

    International Nuclear Information System (INIS)

    Gangi, R.D.; Atmann, J.L.

    1989-01-01

    The utilization of load curves on the evaluation of systems behaviour, consumers and in the owners and users brought a new subsidy for the performance of forecast techniques. This paper shows how we can use these forecasting techniques and load curves in a specify situation joined to Guaira Substation, where the predominance is rural consumers with large activities in irrigation. The main objective of this study is bring by load curve modulation and the expansion of consumer market, a optimized view of load for the future years. (C.G.C.)

  6. Towards Energy Efficiency: Forecasting Indoor Temperature via Multivariate Analysis

    Directory of Open Access Journals (Sweden)

    Juan Pardo

    2013-09-01

    Full Text Available The small medium large system (SMLsystem is a house built at the Universidad CEU Cardenal Herrera (CEU-UCH for participation in the Solar Decathlon 2013 competition. Several technologies have been integrated to reduce power consumption. One of these is a forecasting system based on artificial neural networks (ANNs, which is able to predict indoor temperature in the near future using captured data by a complex monitoring system as the input. A study of the impact on forecasting performance of different covariate combinations is presented in this paper. Additionally, a comparison of ANNs with the standard statistical forecasting methods is shown. The research in this paper has been focused on forecasting the indoor temperature of a house, as it is directly related to HVAC—heating, ventilation and air conditioning—system consumption. HVAC systems at the SMLsystem house represent 53:89% of the overall power consumption. The energy used to maintain temperature was measured to be 30%–38:9% of the energy needed to lower it. Hence, these forecasting measures allow the house to adapt itself to future temperature conditions by using home automation in an energy-efficient manner. Experimental results show a high forecasting accuracy and therefore, they might be used to efficiently control an HVAC system.

  7. 1991 Pacific Northwest loads and resources study, Pacific Northwest economic and electricity use forecast

    International Nuclear Information System (INIS)

    1992-01-01

    This publication provides detailed documentation of the load forecast scenarios and assumptions used in preparing BPA's 1991 Pacific Northwest Loads and Resources Study (the Study). This is one of two technical appendices to the Study; the other appendix details the utility-specific loads and resources used in the Study. The load forecasts and assumption were developed jointly by Bonneville Power Administration (BPA) and Northwest Power Planning Council (Council) staff. This forecast is also used in the Council's 1991 Northwest Conservation and Electric Power Plan (1991 Plan)

  8. Research on light rail electric load forecasting based on ARMA model

    Science.gov (United States)

    Huang, Yifan

    2018-04-01

    The article compares a variety of time series models and combines the characteristics of power load forecasting. Then, a light load forecasting model based on ARMA model is established. Based on this model, a light rail system is forecasted. The prediction results show that the accuracy of the model prediction is high.

  9. The Atacama Cosmology Telescope: A Measurement of the Cosmic Microwave Background Power Spectrum at 148 AND 218 GHz from the 2008 Southern Survey

    Science.gov (United States)

    Das, Sudeep; Marriage, Tobias A.; Ade, Peter A. R.; Aguirre, Paula; Amiri, Mandana; Appel, John W.; Barrientos, L. Felipe; Battistelli, Elia A.; Bond, J. Richard; Brown, Ben; hide

    2010-01-01

    We present measurements of the cosmic microwave background (CMB) power spectrum made by the Atacama Cosmology Telescope at 148 GHz and 218 GHz, as well as the cross-frequency spectrum between the two channels. Our results dearly show the second through the seventh acoustic peaks in the CMB power spectrum. The measurements of these higher-order peaks provide an additional test of the ACDM cosmological model. At l > 3000, we detect power in excess of the primary anisotropy spectrum of the CMB. At lower multipoles 500 < l < 3000, we find evidence for gravitational lensing of the CMB in the power spectrum at the 2.8(sigma) level. We also detect a low level of Galactic dust in our maps, which demonstrates that we can recover known faint, diffuse signals.

  10. Some lemma on spectrum of eigen value regarding power method

    Science.gov (United States)

    Jamali, A. R. M. Jalal Uddin; Alam, Md. Sah

    2017-04-01

    Eigen value problems arise in almost all science and engineering fields. There exist some smart methods in literature in which most of them are able to find only Eigen values but could not find corresponding Eigen vectors. There exist many engineering as well as scientific fields in which both largest as well as smallest Eigen pairs are required. Power method is very simple but a powerful tool for finding largest Eigen value and corresponding Eigen vector (Eigen-pair). Again Inverse Power method is applied to find out smallest Eigen-pair and/or desire Eigen-pairs. But it is known that Inverse Power method is computationally very costly. On the other hand by using shifting property, Power method can find further Eigen-pairs. But the position of this Eigen value in the set of spectrum of the Eigen values is not identified. In this regard we proposed four lemma associate with Modified Power method. Each Lemma is proved ornately. The Modified Power method is implemented and illustrates an example for the verification of the Lemma. By using lemma the modified power algorithm is able to find out both largest and smallest Eigen-pairs successfully and efficiently in some cases. Moreover by the help of the Lemma, algorithm is able to detect the nature (positive and negative) of the Eigen values.

  11. Analysis of Highly Wind Power Integrated Power System model performance during Critical Weather conditions

    DEFF Research Database (Denmark)

    Basit, Abdul; Hansen, Anca Daniela; Sørensen, Poul Ejnar

    2014-01-01

    , is provided by the hour-ahead power balancing model, i.e. Simulation power Balancing model (SimBa. The regulating power plan is prepared from day-ahead power production plan and hour-ahead wind power forecast. The wind power (forecasts and available) are provided by the Correlated Wind power fluctuations (Cor......Wind) model, where the wind turbine storm controllers are also implemented....

  12. Short-term Probabilistic Forecasting of Wind Speed Using Stochastic Differential Equations

    DEFF Research Database (Denmark)

    Iversen, Jan Emil Banning; Morales González, Juan Miguel; Møller, Jan Kloppenborg

    2016-01-01

    and uncertain nature. In this paper, we propose a modeling framework for wind speed that is based on stochastic differential equations. We show that stochastic differential equations allow us to naturally capture the time dependence structure of wind speed prediction errors (from 1 up to 24 hours ahead) and......It is widely accepted today that probabilistic forecasts of wind power production constitute valuable information for both wind power producers and power system operators to economically exploit this form of renewable energy, while mitigating the potential adverse effects related to its variable......, most importantly, to derive point and quantile forecasts, predictive distributions, and time-path trajectories (also referred to as scenarios or ensemble forecasts), all by one single stochastic differential equation model characterized by a few parameters....

  13. Third-Order Density Perturbation and One-Loop Power Spectrum in Dark-Energy-Dominated Universe(Astrophysics and Cosmology)

    OpenAIRE

    Ryuichi, TAKAHASHI; Department of Physics and Astrophysics, Nagoya University

    2008-01-01

    We investigate the third-order density perturbation and the one-loop correction to the linear power spectrum in the dark-energy cosmological model. Our main interest is to understand the dark-energy effect on baryon acoustic oscillations in a quasi-nonlinear regime (k≈0.1h/Mpc). Analytical solutions and simple fitting formulae are presented for the dark-energy model with the general time-varying equation of state w(a). It turns out that the power spectrum coincides with the approximate res...

  14. ON THE CLUSTER PHYSICS OF SUNYAEV-ZEL'DOVICH AND X-RAY SURVEYS. II. DECONSTRUCTING THE THERMAL SZ POWER SPECTRUM

    Energy Technology Data Exchange (ETDEWEB)

    Battaglia, N. [Department of Astronomy and Astrophysics, University of Toronto, 50 St George, Toronto, ON M5S 3H4 (Canada); Bond, J. R.; Pfrommer, C.; Sievers, J. L. [Canadian Institute for Theoretical Astrophysics, 60 St George, Toronto, ON M5S 3H8 (Canada)

    2012-10-20

    Secondary anisotropies in the cosmic microwave background are a treasure-trove of cosmological information. Interpreting current experiments probing them are limited by theoretical uncertainties rather than by measurement errors. Here we focus on the secondary anisotropies resulting from the thermal Sunyaev-Zel'dovich (tSZ) effect; the amplitude of which depends critically on the average thermal pressure profile of galaxy groups and clusters. To this end, we use a suite of hydrodynamical TreePM-SPH simulations that include radiative cooling, star formation, supernova feedback, and energetic feedback from active galactic nuclei. We examine in detail how the pressure profile depends on cluster radius, mass, and redshift and provide an empirical fitting function. We employ three different approaches for calculating the tSZ power spectrum: an analytical approach that uses our pressure profile fit, a semianalytical method of pasting our pressure fit onto simulated clusters, and a direct numerical integration of our simulated volumes. We demonstrate that the detailed structure of the intracluster medium and cosmic web affect the tSZ power spectrum. In particular, the substructure and asphericity of clusters increase the tSZ power spectrum by 10%-20% at l {approx} 2000-8000, with most of the additional power being contributed by substructures. The contributions to the power spectrum from radii larger than R {sub 500} is {approx}20% at l = 3000, thus clusters interiors (r < R {sub 500}) dominate the power spectrum amplitude at these angular scales.

  15. Ambiguities in the deduction of rest frame fluctuation spectrums from spectrums computed in moving frames

    International Nuclear Information System (INIS)

    Fredericks, R.W.; Coroniti, F.V.

    1976-01-01

    The problem of interpretation of power spectrums computed by Fourier analysis of data time series taken in frames moving with respect to the medium containing the fluctuations is examined. It is found that no unique connection exists between the rest frame power spectrum as a function of scale length and the derived power spectrum as a function 'frequency' computed from the time series data taken in the moving frame. This caused by a complex Doppler-shifting phenomenon that leads to a basically aliased frequency spectrum in the moving frame. Examples of nonuniqueness are given for various types of rest frame density or wave turbulence that lead to the same frequency dependence of the power spectrum computed in the moving frame. This has implications for the past interpretations of power spectrums of density or magnetic field fluctuations from satellites or interplanetary probes

  16. Using the CMB angular power spectrum to study Dark Matter-photon interactions

    International Nuclear Information System (INIS)

    Wilkinson, Ryan J.; Boehm, Céline; Lesgourgues, Julien

    2014-01-01

    In this paper, we explore the impact of Dark Matter-photon interactions on the CMB angular power spectrum. Using the one-year data release of the Planck satellite, we derive an upper bound on the Dark Matter-photon elastic scattering cross section of σ DM−γ ≤ 8 × 10 −31 (m DM /GeV) cm 2 (68% CL) if the cross section is constant and a present-day value of σ DM−γ ≤ 6 × 10 −40 (m DM /GeV) cm 2 (68% CL) if it scales as the temperature squared. For such a limiting cross section, both the B-modes and the TT angular power spectrum are suppressed with respect to ΛCDM predictions for ℓ∼>500 and ℓ∼>3000 respectively, indicating that forthcoming data from CMB polarisation experiments and Planck could help to constrain and characterise the physics of the dark sector. This essentially initiates a new type of dark matter search that is independent of whether dark matter is annihilating, decaying or asymmetric. Thus, any CMB experiment with the ability to measure the temperature and/or polarisation power spectra at high ℓ should be able to investigate the potential interactions of dark matter and contribute to our fundamental understanding of its nature

  17. KL Estimation of the Power Spectrum Parameters from the Angular Distribution of Galaxies in Early SDSS Data

    CERN Document Server

    Szalay, Alexander S.; Matsubara, Takahiko; Scranton, Ryan; Vogeley, Michael S.; Connolly, Andrew; Dodelson, Scott; Eisenstein, Daniel; Frieman, Joshua A.; Gunn, James E.; Hui, Lam; Johnston, David; Kent, Stephen M.; Kerscher, Martin; Loveday, Jon; Meiksin, Avery; Narayanan, Vijay; Nichol, Robert C.; O'Connell, Liam; Pope, Adrian; Scoccimarro, Roman; Sheth, Ravi K.; Stebbins, Albert; Strauss, Michael A.; Szapudi, Istvan; Tegmark, Max; Zehavi, Idit; Annis, James; Bahcall, Neta A.; Brinkmann, Jon; Csabai, Istvan; Fukugita, Masataka; Hennessy, Greg; Hogg, David W.; Ivezic, Zeljko; Knapp, Gillian R.; Kunszt, Peter Z.; Lamb, Don Q.; Lee, Brian C.; Lupton, Robert H.; Munn, Jeffrey R.; Peoples, John; Pier, Jeffrey R.; Rockosi, Constance; Schlegel, David; Stoughton, Christopher; Tucker, Douglas L.; Yanny, Brian; York, Donald G.; Szalay, Alexander S.; Jain, Bhuvnesh; Matsubara, Takahiko; Scranton, Ryan; Vogeley, Michael S.; Connolly, Andrew; Dodelson, Scott; Eisenstein, Daniel; Frieman, Joshua A.; Gunn, James E.; Hui, Lam; Johnston, David; Kent, Stephen; Kerscher, Martin; Loveday, Jon; Meiksin, Avery; Narayanan, Vijay; Nichol, Robert C.; Connell, Liam O'; Pope, Adrian; Scoccimarro, Roman; Sheth, Ravi K.; Stebbins, Albert; Strauss, Michael A.; Szapudi, Istvan; Tegmark, Max; Zehavi, Idit

    2002-01-01

    We present measurements of parameters of the 3-dimensional power spectrum of galaxy clustering from 222 square degrees of early imaging data in the Sloan Digital Sky Survey. The projected galaxy distribution on the sky is expanded over a set of Karhunen-Loeve eigenfunctions, which optimize the signal-to-noise ratio in our analysis. A maximum likelihood analysis is used to estimate parameters that set the shape and amplitude of the 3-dimensional power spectrum. Our best estimates are Gamma=0.188 +/- 0.04 and sigma_8L = 0.915 +/- 0.06 (statistical errors only), for a flat Universe with a cosmological constant. We demonstrate that our measurements contain signal from scales at or beyond the peak of the 3D power spectrum. We discuss how the results scale with systematic uncertainties, like the radial selection function. We find that the central values satisfy the analytically estimated scaling relation. We have also explored the effects of evolutionary corrections, various truncations of the KL basis, seeing, sam...

  18. A Hybrid Method Based on Singular Spectrum Analysis, Firefly Algorithm, and BP Neural Network for Short-Term Wind Speed Forecasting

    Directory of Open Access Journals (Sweden)

    Yuyang Gao

    2016-09-01

    Full Text Available With increasing importance being attached to big data mining, analysis, and forecasting in the field of wind energy, how to select an optimization model to improve the forecasting accuracy of the wind speed time series is not only an extremely challenging problem, but also a problem of concern for economic forecasting. The artificial intelligence model is widely used in forecasting and data processing, but the individual back-propagation artificial neural network cannot always satisfy the time series forecasting needs. Thus, a hybrid forecasting approach has been proposed in this study, which consists of data preprocessing, parameter optimization and a neural network for advancing the accuracy of short-term wind speed forecasting. According to the case study, in which the data are collected from Peng Lai, a city located in China, the simulation results indicate that the hybrid forecasting method yields better predictions compared to the individual BP, which indicates that the hybrid method exhibits stronger forecasting ability.

  19. The Forecasting Power of the Yield Curve, a Supervised Factor Model Approach

    DEFF Research Database (Denmark)

    Boldrini, Lorenzo; Hillebrand, Eric Tobias

    loadings have the Nelson and Siegel (1987) structure and we consider one forecast target at a time. We compare the forecasting performance of our specification to benchmark models such as principal components regression, partial least squares, and ARMA(p,q) processes. We use the yield curve data from G...

  20. Effect of the accuracy of price forecasting on profit in a Price Based Unit Commitment

    International Nuclear Information System (INIS)

    Delarue, Erik; Van Den Bosch, Pieterjan; D'haeseleer, William

    2010-01-01

    This paper discusses and quantifies the so-called loss of profit (i.e., the sub-optimality of profit) that can be expected in a Price Based Unit Commitment (PBUC), when incorrect price forecasts are used. For this purpose, a PBUC model has been developed and utilized, using Mixed Integer Linear Programming (MILP). Simulations are used to determine the relationship between the Mean Absolute Percentage Error (MAPE) of a certain price forecast and the loss of profit, for four different types of power plants. A Combined Cycle (CC) power plant and a pumped storage unit show highest sensitivity to incorrect forecasts. A price forecast with a MAPE of 15%, on average, yields 13.8% and 12.1% profit loss, respectively. A classic thermal power plant (coal fired) and cascade hydro unit are less affected by incorrect forecasts, with only 2.4% and 2.0% profit loss, respectively, at the same price forecast MAPE. This paper further demonstrates that if price forecasts show an average bias (upward or downward), using the MAPE as measure of the price forecast might not be sufficient to quantify profit loss properly. Profit loss in this case has been determined as a function of both shift and MAPE of the price forecast. (author)

  1. A method for short term electricity spot price forecasting

    International Nuclear Information System (INIS)

    Koreneff, G.; Seppaelae, A.; Lehtonen, M.; Kekkonen, V.; Laitinen, E.; Haekli, J.; Antila, E.

    1998-01-01

    In Finland, the electricity market was de-regulated in November 1995. For the electricity purchase of power companies this has caused big changes, since the old tariff based contracts of bulk power supply have been replaced by negotiated bilateral short term contracts and by power purchase from the spot market. In the spot market, in turn, there are at the present two strong actors: The electricity exchange of Finland and the Nordic power pool which is run by the Swedish and Norwegian companies. Today, the power companies in Finland have short term trade with both of the electricity exchanges. The aim of this chapter is to present methods for spot price forecasting in the electricity exchange. The main focus is given to the Finnish circumstances. In the beginning of the presentation, the practices of the electricity exchange of Finland are described, and a brief presentation is given on the different contracts, or electricity products, available in the spot market. For comparison, the practices of the Nordic electr