WorldWideScience

Sample records for wind radii forecast

  1. IEA Wind Task 36 Forecasting

    Science.gov (United States)

    Giebel, Gregor; Cline, Joel; Frank, Helmut; Shaw, Will; Pinson, Pierre; Hodge, Bri-Mathias; Kariniotakis, Georges; Sempreviva, Anna Maria; Draxl, Caroline

    2017-04-01

    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 Wind Power Forecasting tries to organise international collaboration, among national weather centres with an interest and/or large projects on wind forecast improvements (NOAA, DWD, UK MetOffice, …) and operational forecaster and forecast users. The Task is divided in three work packages: Firstly, a collaboration on the improvement of the scientific basis for the wind predictions themselves. This includes numerical weather prediction model physics, but also widely distributed information on accessible datasets for verification. Secondly, we will be aiming at an international pre-standard (an IEA Recommended Practice) on benchmarking and comparing wind power forecasts, including probabilistic forecasts aiming at industry and forecasters alike. 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, especially probabilistic ones. The Operating Agent is Gregor Giebel of DTU, Co-Operating Agent is Joel Cline of the US Department of Energy. Collaboration in the task is solicited from everyone interested in the forecasting business. We will collaborate with IEA Task 31 Wakebench, which developed the Windbench benchmarking platform, which this task will use for forecasting benchmarks. The task runs for three years, 2016-2018. Main deliverables are an up-to-date list of current projects and main project results, including datasets which can be used by researchers around the world to improve their own models, an IEA Recommended Practice on performance evaluation of probabilistic forecasts, a position paper regarding the use of probabilistic forecasts

  2. Data mining for wind power forecasting

    OpenAIRE

    Fugon, Lionel; Juban, Jérémie; Kariniotakis, Georges

    2008-01-01

    International audience; Short-term forecasting of wind energy production up to 2-3 days ahead is recognized as a major contribution for reliable large-scale wind power integration. Increasing the value of wind generation through the improvement of prediction systems performance is recognised as one of the priorities in wind energy research needs for the coming years. This paper aims to evaluate Data Mining type of models for wind power forecasting. Models that are examined include neural netw...

  3. 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...... Energy tries to organise international collaboration, among national weather centres with an interest and/or large projects on wind forecast improvements (NOAA, DWD, …), operational forecaster and forecast users. The Task is divided in three work packages: Firstly, a collaboration on the improvement...... 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....

  4. 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

  5. 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)

  6. A New Reference for Wind Power Forecasting

    DEFF Research Database (Denmark)

    Nielsen, Torben Skov; Joensen, Alfred K.; Madsen, Henrik

    1998-01-01

    In recent years some research towards developing forecasting models for wind power or energy has been carried out. In order to evaluate the prediction ability of these models, the forecasts are usually compared with those of the persistence forecast model. As shown in this article, however...

  7. 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

  8. Improving weather forecasts for wind energy applications

    Science.gov (United States)

    Kay, Merlinde; MacGill, Iain

    2010-08-01

    Weather forecasts play an important role in the energy industry particularly because of the impact of temperature on electrical demand. Power system operation requires that this variable and somewhat unpredictable demand be precisely met at all times and locations from available generation. As wind generation makes up a growing component of electricity supply around the world, it has become increasingly important to be able to provide useful forecasting for this highly variable and uncertain energy resource. Of particular interest are forecasts of weather events that rapidly change wind energy production from one or more wind farms. In this paper we describe work underway to improve the wind forecasts currently available from standard Numerical Weather Prediction (NWP) through a bias correction methodology. Our study has used the Australian Bureau of Meteorology MesoLAPS 5 km limited domain model over the Victoria/Tasmania region, providing forecasts for the Woolnorth wind farm, situated in Tasmania, Australia. The accuracy of these forecasts has been investigated, concentrating on the key wind speed ranges 5 - 15 ms-1 and around 25 ms-1. A bias correction methodology was applied to the NWP hourly forecasts to help account for systematic issues such as the NWP grid point not being at the exact location of the wind farm. An additional correction was applied for timing issues by using meteorological data from the wind farm. Results to date show a reduction in spread of forecast error for hour ahead forecasts by as much as half using this double correction methodology - a combination of both bias correction and timing correction.

  9. Improving weather forecasts for wind energy applications

    International Nuclear Information System (INIS)

    Kay, Merlinde; MacGill, Iain

    2010-01-01

    Weather forecasts play an important role in the energy industry particularly because of the impact of temperature on electrical demand. Power system operation requires that this variable and somewhat unpredictable demand be precisely met at all times and locations from available generation. As wind generation makes up a growing component of electricity supply around the world, it has become increasingly important to be able to provide useful forecasting for this highly variable and uncertain energy resource. Of particular interest are forecasts of weather events that rapidly change wind energy production from one or more wind farms. In this paper we describe work underway to improve the wind forecasts currently available from standard Numerical Weather Prediction (NWP) through a bias correction methodology. Our study has used the Australian Bureau of Meteorology MesoLAPS 5 km limited domain model over the Victoria/Tasmania region, providing forecasts for the Woolnorth wind farm, situated in Tasmania, Australia. The accuracy of these forecasts has been investigated, concentrating on the key wind speed ranges 5 - 15 ms -1 and around 25 ms -1 . A bias correction methodology was applied to the NWP hourly forecasts to help account for systematic issues such as the NWP grid point not being at the exact location of the wind farm. An additional correction was applied for timing issues by using meteorological data from the wind farm. Results to date show a reduction in spread of forecast error for hour ahead forecasts by as much as half using this double correction methodology - a combination of both bias correction and timing correction.

  10. Forecasting volatility of wind power production

    OpenAIRE

    Zhiwei Shen; Matthias Ritter

    2015-01-01

    Abstract: The increasing share of wind energy in the portfolio of energy sources highlights its uncertainties due to changing weather conditions. To account for the uncertainty in predicting wind power production, this article examines the volatility forecasting abilities of different GARCH-type models for wind power production. Moreover, due to characteristic features of the wind power process, such as heteroscedasticity and nonlinearity, we also investigate the use of a Markov regime-switch...

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

    DEFF Research Database (Denmark)

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

    2016-01-01

    , MetOffice, met.no, DMI,...), operational forecaster and forecast users.The Task is divided in three work packages: Firstly, a collaboration on the improvement of the scientific basis for the wind predictions themselves. This includes numerical weather prediction model physics, but also widely......Bench. Thirdly, we will be engaging end users aiming at dissemination of the best practice in the usage of wind power predictions. As first results, an overview of current issues for research in short-term forecasting of wind power is presented....

  12. Wind forecasting for grid code compliance

    Energy Technology Data Exchange (ETDEWEB)

    Vanitha, V.; Kishore, S.R.N. [Amrita Vishwa Vidyapeetham Univ.. Dept. of Electrical and Electronics Engineering, Coimbatore (India)

    2012-07-01

    This work explores Adaptive Neuro-Fuzzy Inference Systems (ANFIS) to forecast the average hourly wind speed. To determine the characteristics of ANFIS that best suited the target wind speed forecasting system, several ANFIS models were trained, tested and compared. Different types and number of inputs, training and checking sizes, type and number of membership functions and techniques to generate the initial (FIS) were analyzed. Comparisons with other forecasting methods were analyzed the models were given wind speed, direction and air pressure as inputs having the best forecasting accuracy. SCADA system is utilized to obtain the wind speed to the forecasting system in the host computer where ANFIS is present. The SCADA is located in the central room, the substation of the wind farm, or even at a remote off site point. The data obtained from the site is plotted at every instant and the predicted wind speed is displayed and also exported to the excel sheet which will be sent/e-mailed in the form of Graphs and excel sheets to the operator, State load dispatch centre (SLDC) and to the customer. (Author)

  13. 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.

  14. Peak Wind Tool for General Forecasting

    Science.gov (United States)

    Barrett, Joe H., III

    2010-01-01

    The expected peak wind speed of the day is an important forecast element in the 45th Weather Squadron's (45 WS) daily 24-Hour and Weekly Planning Forecasts. The forecasts are used for ground and space launch operations at the Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). The 45 WS also issues wind advisories for KSC/CCAFS when they expect wind gusts to meet or exceed 25 kt, 35 kt and 50 kt thresholds at any level from the surface to 300 ft. The 45 WS forecasters have indicated peak wind speeds are challenging to forecast, particularly in the cool season months of October - April. In Phase I of this task, the Applied Meteorology Unit (AMU) developed a tool to help the 45 WS forecast non-convective winds at KSC/CCAFS for the 24-hour period of 0800 to 0800 local time. The tool was delivered as a Microsoft Excel graphical user interface (GUI). The GUI displayed the forecast of peak wind speed, 5-minute average wind speed at the time of the peak wind, timing of the peak wind and probability the peak speed would meet or exceed 25 kt, 35 kt and 50 kt. For the current task (Phase II ), the 45 WS requested additional observations be used for the creation of the forecast equations by expanding the period of record (POR). Additional parameters were evaluated as predictors, including wind speeds between 500 ft and 3000 ft, static stability classification, Bulk Richardson Number, mixing depth, vertical wind shear, temperature inversion strength and depth and wind direction. Using a verification data set, the AMU compared the performance of the Phase I and II prediction methods. Just as in Phase I, the tool was delivered as a Microsoft Excel GUI. The 45 WS requested the tool also be available in the Meteorological Interactive Data Display System (MIDDS). The AMU first expanded the POR by two years by adding tower observations, surface observations and CCAFS (XMR) soundings for the cool season months of March 2007 to April 2009. The POR was expanded

  15. 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)

  16. 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.

  17. Utility operating strategy and requirements for wind power forecast

    Science.gov (United States)

    Dub, W.; Pape, H.

    1983-06-01

    The commitment of a generation system including wind energy conversion systems will be based on wind speed and wind power forecasts. Forecasts for time spans of equal length with the startup/shutdown times of conventional units will be of great importance. The paper discusses forecast horizons up to 3 hours and 6 hours respectively. In addition, the problem of getting good wind speed forecasts is investigated by fitting time series models to wind speed data. Finally, the impact of hypothetical perfect forecasts on the commitment of intermediate load units is demonstrated by means of the wind power variations within spans up to 3 hours.

  18. The Wind Forecast Improvement Project (WFIP). A Public-Private Partnership Addressing Wind Energy Forecast Needs

    Energy Technology Data Exchange (ETDEWEB)

    Wilczak, James M. [NOAA, Boulder, CO (United States); Finley, Cathy [WindLogics, Inc., St. Paul, MN (United States); Freedman, Jeff [AWS Truepower, Albany, NY (United States); Cline, Joel [USDOE Office of Energy Efficiency and Renewable Energy, Washington, DC (United States); Bianco, L. [Univ. of Colorado, Boulder, CO (United States); Olson, J. [Univ. of Colorado, Boulder, CO (United States); Djalaova, I. [Univ. of Colorado, Boulder, CO (United States); Sheridan, L. [WindLogics, Inc., St. Paul, MN (United States); Ahlstrom, M. [WindLogics, Inc., St. Paul, MN (United States); Manobianco, J. [Meso, Inc., Troy, NY (United States); Zack, J. [Meso, Inc., Troy, NY (United States); Carley, J. [National Oceanic and Atmospheric Administration (NOAA), College Park, MD (United States); Benjamin, S. [NOAA, Boulder, CO (United States); Coulter, R. L. [Argonne National Lab. (ANL), Lemont, IL (United States); Berg, Larry K. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Mirocha, Jeff D. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Clawson, K. [National Oceanic and Atmospheric Administration (NOAA), Idaho Falls, ID (United States); Natenberg, E. [Meso, Inc., Troy, NY (United States); Marquis, M. [NOAA, Boulder, CO (United States)

    2015-10-30

    The Wind Forecast Improvement Project (WFIP) is a public-private research program, the goals of which are to improve the accuracy of short-term (0-6 hr) wind power forecasts for the wind energy industry and then to quantify the economic savings that accrue from more efficient integration of wind energy into the electrical grid. WFIP was sponsored by the U.S. Department of Energy (DOE), with partners that include the National Oceanic and Atmospheric Administration (NOAA), private forecasting companies (WindLogics and AWS Truepower), DOE national laboratories, grid operators, and universities. WFIP employed two avenues for improving wind power forecasts: first, through the collection of special observations to be assimilated into forecast models to improve model initial conditions; and second, by upgrading NWP forecast models and ensembles. The new observations were collected during concurrent year-long field campaigns in two high wind energy resource areas of the U.S. (the upper Great Plains, and Texas), and included 12 wind profiling radars, 12 sodars, 184 instrumented tall towers and over 400 nacelle anemometers (provided by private industry), lidar, and several surface flux stations. Results demonstrate that a substantial improvement of up to 14% relative reduction in power root mean square error (RMSE) was achieved from the combination of improved NOAA numerical weather prediction (NWP) models and assimilation of the new observations. Data denial experiments run over select periods of time demonstrate that up to a 6% relative improvement came from the new observations. The use of ensemble forecasts produced even larger forecast improvements. Based on the success of WFIP, DOE is planning follow-on field programs.

  19. 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

  20. Scour Forecasting for Offshore Wind Parks

    DEFF Research Database (Denmark)

    Hartvig, Peres Akrawi

    , scour forecasts facilitate the comparison between a scour design based on either deployment of scour-protection or enhanced structural design. The broad goal is to develop a method that produces accurate scour forecasts for offshore wind parks. The present research investigates more specifically which...... two legacies that deal with these two research questions in dialectical ways. The first legacy is a framework for the scour geometry based on epistemological considerations, theoretical concepts and model scale experiments. Relevant parameters are reviewed, defined and discussed. The combined use...

  1. 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 decisi...

  2. 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...

  3. Direct Interval Forecasting of Wind Power

    DEFF Research Database (Denmark)

    Wan, Can; Xu, Zhao; Pinson, Pierre

    2013-01-01

    This letter proposes a novel approach to directly formulate the prediction intervals of wind power generation based on extreme learning machine and particle swarm optimization, where prediction intervals are generated through direct optimization of both the coverage probability and sharpness, wit......, without the prior knowledge of forecasting errors. The proposed approach has been proved to be highly efficient and reliable through preliminary case studies using real-world wind farm data, indicating a high potential of practical application.......This letter proposes a novel approach to directly formulate the prediction intervals of wind power generation based on extreme learning machine and particle swarm optimization, where prediction intervals are generated through direct optimization of both the coverage probability and sharpness...

  4. Multistep Wind Speed Forecasting Based on Wavelet and Gaussian Processes

    Directory of Open Access Journals (Sweden)

    Niya Chen

    2013-01-01

    Full Text Available Accurate wind speed forecasts are necessary for the safety and economy of the renewable energy utilization. The wind speed forecasts can be obtained by statistical model based on historical data. In this paper, a novel W-GP model (wavelet decomposition based Gaussian process learning paradigm is proposed for short-term wind speed forecasting. The nonstationary and nonlinear original wind speed series is first decomposed into a set of better-behaved constitutive subseries by wavelet decomposition. Then these sub-series are forecasted respectively by GP method, and the forecast results are summed to formulate an ensemble forecast for original wind speed series. Therefore, the previous process which obtains wind speed forecast result is named W-GP model. Finally, the proposed model is applied to short-term forecasting of the mean hourly and daily wind speed for a wind farm located in southern China. The prediction results indicate that the proposed W-GP model, which achieves a mean 13.34% improvement in RMSE (Root Mean Square Error compared to persistence method for mean hourly data and a mean 7.71% improvement for mean daily wind speed data, shows the best forecasting accuracy among several forecasting models.

  5. 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

  6. Effective Short-term Forecasting of Wind Farms Power

    Directory of Open Access Journals (Sweden)

    Elżbieta Bogalecka

    2015-09-01

    Full Text Available Forecasting a specific wind farm’s (WF generation capacity within a 24 hour perspective requires both a reliable forecast of wind, as well as supporting tools. This tool is a dedicated model of wind farm power. This model should include not only general rules of wind to mechanical energy conversion, but also the farm’s specific features. There are many factors that influence a farm’s generation capacity, and any forecast of it, even with an accurate weather forecast, carries error. This paper presents analytical, statistical, and neuron models of wind farm power. The study is based on data from a real wind farm. Most attention is paid to the neuron models, due to a neuron network’s capability to restore farm-specific details. The research aims to answer the headline question: whether and to what extent a wind farm’s power can be forecast short-term?

  7. Wind Power Forecasting Error Distributions: An International Comparison; Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Hodge, B. M.; Lew, D.; Milligan, M.; Holttinen, H.; Sillanpaa, S.; Gomez-Lazaro, E.; Scharff, R.; Soder, L.; Larsen, X. G.; Giebel, G.; Flynn, D.; Dobschinski, J.

    2012-09-01

    Wind power forecasting is expected to be an important enabler for greater penetration of wind power into electricity systems. Because no wind forecasting system is perfect, a thorough understanding of the errors that do occur can be critical to 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.

  8. 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....

  9. 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.

  10. Probabilistic Wind Speed Forecasting using Ensembles and Bayesian Model Averaging

    National Research Council Canada - National Science Library

    Sloughter, J. M; Gneiting, Tilmann; Raftery, Adrian E

    2008-01-01

    Probabilistic forecasts of wind speed are becoming critical as interest grows in wind as a clean and renewable source of energy, in addition to a wide range of other uses, from aviation to recreational boating...

  11. Probabilistic Wind Power Forecasting with Hybrid Artificial Neural Networks

    DEFF Research Database (Denmark)

    Wan, Can; Song, Yonghua; Xu, Zhao

    2016-01-01

    The uncertainty of wind power generation imposes significant challenges to optimal operation and control of electricity networks with increasing wind power penetration. To effectively address the uncertainties in wind power forecasts, probabilistic forecasts that can quantify the associated...... 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....

  12. 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

  13. Wind field forecast for accidental release of radiative materials

    International Nuclear Information System (INIS)

    Kang Ling; Chen Jiayi; Cai Xuhui

    2003-01-01

    A meso-scale wind field forecast model was designed for emergency environmental assessment in case of accidental release of radiative materials from a nuclear power station. Actual practice of the model showed that it runs fast, has wind field prediction function, and the result given is accurate. With meteorological data collected from weather stations, and pre-treated by a wind field diagnostic model, the initial wind fields at different times were inputted as initial values and assimilation fields for the forecasting model. The model, in turn, worked out to forecast meso-scale wind field of 24 hours in a horizontal domain of 205 km x 205 km. And then, the diagnostic model was employed again with the forecasting data to obtain more detail information of disturbed wind field by local terrain in a smaller domain of 20.5 km x 20.5 km, of which the nuclear power station is at the center. Using observation data in January, April, July and October of 1996 over the area of Hangzhou Bay, wind fields in these 4 months were simulated by different assimilation time and number of the weather stations for a sensitive test. Results indicated that the method used here has increased accuracy of the forecasted wind fields. And incorporating diagnostic method with the wind field forecast model has greatly increased efficiency of the wind field forecast for the smaller domain. This model and scheme have been used in Environmental Consequence Assessment System of Nuclear Accident in Qinshan Area

  14. 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

    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...... 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...

  15. On probabilistic forecasting of wind power time-series

    DEFF Research Database (Denmark)

    Pinson, Pierre

    Wind power generation is a nonlinear and bounded variable, partly owing to the power curve that converts wind to electric power, and partly owing to the very stochastic nature of wind itself. Predictive densities of wind power generation should account for that effect. Such densities are clearly...... 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....

  16. 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.

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

    DEFF Research Database (Denmark)

    Wan, Can; Xu, Zhao; Pinson, Pierre

    2014-01-01

    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...... 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...... 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...

  18. 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...

  19. 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

  20. Hydro-Quebec and Environment Canada wind energy forecasting project

    Energy Technology Data Exchange (ETDEWEB)

    Forcione, A.; Roberge, G. [Hydro-Quebec, Saguenay, PQ (Canada). IREQ; Petrucci, F.; Yu, W. [Environment Canada, Gatineau, PQ (Canada)

    2008-07-01

    This presentation outlined a joint wind energy forecasting project currently being conducted by Hydro-Quebec and Environment Canada. The aim of the project is to provide high quality next day hourly forecasts to permit optimal planning and scheduling of wind balancing needs. The quality of next day hourly forecasts depends on the availability of high quality numerical weather prediction modelling output. The model currently being developed by the researchers has been designed to provide 48-hour high resolution, hourly wind forecasts. The Systeme de Provision Eolienne (SPEO) uses mesoscale and microscale operational forecast components from Environment Canada as well as local meteorological observations to provide the wind forecast. Operational forecasts are generated using a global environmental multi-scale model (GEM). Digital elevation models are used to provide high resolution physical data. Case studies of wind forecasts made using the model were provided, as well as flow charts describing a chronology of processes used by the model. It was concluded that the model provides accurate next day wind forecasts. tabs., figs.

  1. Wind Energy: Forecasting Challenges for its Operational Management

    DEFF Research Database (Denmark)

    Pinson, Pierre

    2013-01-01

    in a probabilistic framework. Even though,eventually, the forecaster may only communicate single-valued predictions.The existing approaches to wind power forecasting are subsequently described, with focus on single-valued predictions, predictive marginal densities and space-time trajectories. Upcoming challenges...... related to generating improved and new types of forecasts, as well as their verification and value to forecast users, are finally discussed. ½...

  2. Evaluating winds and vertical wind shear from Weather Research and Forecasting model forecasts using seven planetary boundary layer schemes

    DEFF Research Database (Denmark)

    Draxl, Caroline; Hahmann, Andrea N.; Pena Diaz, Alfredo

    2014-01-01

    with different PBL parameterizations at one coastal site over western Denmark. The evaluation focuses on determining which PBL parameterization performs best for wind energy forecasting, and presenting a validation methodology that takes into account wind speed at different heights. Winds speeds at heights...... regarding wind energy at these levels partly depends on the formulation and implementation of planetary boundary layer (PBL) parameterizations in these models. This study evaluates wind speeds and vertical wind shears simulated by theWeather Research and Forecasting model using seven sets of simulations...

  3. The Application of TAPM for Site Specific Wind Energy Forecasting

    Directory of Open Access Journals (Sweden)

    Merlinde Kay

    2016-02-01

    Full Text Available The energy industry uses weather forecasts for determining future electricity demand variations due to the impact of weather, e.g., temperature and precipitation. However, as a greater component of electricity generation comes from intermittent renewable sources such as wind and solar, weather forecasting techniques need to now also focus on predicting renewable energy supply, which means adapting our prediction models to these site specific resources. This work assesses the performance of The Air Pollution Model (TAPM, and demonstrates that significant improvements can be made to only wind speed forecasts from a mesoscale Numerical Weather Prediction (NWP model. For this study, a wind farm site situated in North-west Tasmania, Australia was investigated. I present an analysis of the accuracy of hourly NWP and bias corrected wind speed forecasts over 12 months spanning 2005. This extensive time frame allows an in-depth analysis of various wind speed regimes of importance for wind-farm operation, as well as extreme weather risk scenarios. A further correction is made to the basic bias correction to improve the forecast accuracy further, that makes use of real-time wind-turbine data and a smoothing function to correct for timing-related issues. With full correction applied, a reduction in the error in the magnitude of the wind speed by as much as 50% for “hour ahead” forecasts specific to the wind-farm site has been obtained.

  4. Doppler Lidar in the Wind Forecast Improvement Projects

    Directory of Open Access Journals (Sweden)

    Pichugina Yelena

    2016-01-01

    Full Text Available This paper will provide an overview of some projects in support of Wind Energy development involving Doppler lidar measurement of wind flow profiles. The high temporal and vertical resolution of these profiles allows the uncertainty of Numerical Weather Prediction models to be evaluated in forecasting dynamic processes and wind flow phenomena in the layer of rotor-blade operation.

  5. 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

  6. 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)

  7. Day-Ahead Wind Speed Forecasting Using Relevance Vector Machine

    Directory of Open Access Journals (Sweden)

    Guoqiang Sun

    2014-01-01

    Full Text Available With the development of wind power technology, the security of the power system, power quality, and stable operation will meet new challenges. So, in this paper, we propose a recently developed machine learning technique, relevance vector machine (RVM, for day-ahead wind speed forecasting. We combine Gaussian kernel function and polynomial kernel function to get mixed kernel for RVM. Then, RVM is compared with back propagation neural network (BP and support vector machine (SVM for wind speed forecasting in four seasons in precision and velocity; the forecast results demonstrate that the proposed method is reasonable and effective.

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

    DEFF Research Database (Denmark)

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

    2007-01-01

    This report is written in fulfilment of Task 2.3 in the TradeWind project (EU sponsored, under the Intelligent Energy Europe initiative): Wind Power Integration and Exchange in the Trans-European Power Market. The Task description is as follows: Task 2.3: Forecast error of aggregated wind power...... 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...

  9. An improved market penetration model for wind energy technology forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Lund, P.D. [Helsinki Univ. of Technology, Espoo (Finland). Advanced Energy Systems

    1995-12-31

    An improved market penetration model with application to wind energy forecasting is presented. In the model, a technology diffusion model and manufacturing learning curve are combined. Based on a 85% progress ratio that was found for European wind manufactures and on wind market statistics, an additional wind power capacity of ca 4 GW is needed in Europe to reach a 30 % price reduction. A full breakthrough to low-cost utility bulk power markets could be achieved at a 24 GW level. (author)

  10. An improved market penetration model for wind energy technology forecasting

    International Nuclear Information System (INIS)

    Lund, P.D.

    1995-01-01

    An improved market penetration model with application to wind energy forecasting is presented. In the model, a technology diffusion model and manufacturing learning curve are combined. Based on a 85% progress ratio that was found for European wind manufactures and on wind market statistics, an additional wind power capacity of ca 4 GW is needed in Europe to reach a 30 % price reduction. A full breakthrough to low-cost utility bulk power markets could be achieved at a 24 GW level. (author)

  11. 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

  12. Adaptive calibration of (u,v)‐wind ensemble forecasts

    DEFF Research Database (Denmark)

    Pinson, Pierre

    2012-01-01

    Ensemble forecasts of (u,v)‐wind are of crucial importance for a number of decision‐making problems related to e.g. air traffic control, ship routeing and energy management. The skill of these ensemble forecasts as generated by NWP‐based models can be maximised by correcting for their lack of suf...

  13. Quantile Forecasting of Wind Power Using Variability Indices

    Directory of Open Access Journals (Sweden)

    Patrick McSharry

    2013-02-01

    Full Text Available Wind power forecasting techniques have received substantial attention recently due to the increasing penetration of wind energy in national power systems. While the initial focus has been on point forecasts, the need to quantify forecast uncertainty and communicate the risk of extreme ramp events has led to an interest in producing probabilistic forecasts. Using four years of wind power data from three wind farms in Denmark, we develop quantile regression models to generate short-term probabilistic forecasts from 15 min up to six hours ahead. More specifically, we investigate the potential of using various variability indices as explanatory variables in order to include the influence of changing weather regimes. These indices are extracted from the same wind power series and optimized specifically for each quantile. The forecasting performance of this approach is compared with that of appropriate benchmark models. Our results demonstrate that variability indices can increase the overall skill of the forecasts and that the level of improvement depends on the specific quantile.

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

    OpenAIRE

    Haixiang Zang; Lei Fan; Mian Guo; Zhinong Wei; Guoqiang Sun; Li Zhang

    2016-01-01

    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 (EE...

  15. 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)

  16. Latin America wind market assessment. Forecast 2013-2022

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2013-10-15

    Wind Power Activities by Country: Developers/Owners, Wind Plant Sizes, Wind Turbines Deployed, Commissioning Dates, Market Share, and Capacity Forecasts Latin American markets are a subject of intense interest from the global wind industry. Wind plant construction across Latin America is modest compared to the more established markets like the United States, Europe, and China, but it is an emerging market that is taking off at a rapid pace. The region has become the hottest alternative growth market for the wind energy industry at a time when growth rates in other markets are flat due to a variety of policy and macroeconomic challenges. Globalization is driving sustainable economic growth in most Latin American countries, resulting in greater energy demand. Wind is increasingly viewed as a valuable and essential answer to increasing electricity generation across most markets in Latin America. Strong wind resources, coupled with today's sophisticated wind turbines, are providing cost-effective generation that is competitive with fossil fuel generation. Most Latin American countries also rely heavily on hydroelectricity, which balances well with variable wind generation. Navigant Research forecasts that if most wind plants under construction with planned commissioning go online as scheduled, annual wind power installations in Latin America will grow from nearly 2.2 GW in 2013 to 4.3 GW by 2022. This Navigant Research report provides a comprehensive view of the wind energy market dynamics at play in Latin America. It offers a country-by-country analysis, outlining the key energy policies and development opportunities and barriers and identifying which companies own operational wind plants and which wind turbine vendors supplied those projects. Market forecasts for wind power installations, capacity, and market share in Latin America, segmented by country and company, extend through 2022. The report also offers an especially close analysis of Brazil and Mexico

  17. 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

  18. Results of verification and investigation of wind velocity field forecast. Verification of wind velocity field forecast model

    International Nuclear Information System (INIS)

    Ogawa, Takeshi; Kayano, Mitsunaga; Kikuchi, Hideo; Abe, Takeo; Saga, Kyoji

    1995-01-01

    In Environmental Radioactivity Research Institute, the verification and investigation of the wind velocity field forecast model 'EXPRESS-1' have been carried out since 1991. In fiscal year 1994, as the general analysis, the validity of weather observation data, the local features of wind field, and the validity of the positions of monitoring stations were investigated. The EXPRESS which adopted 500 m mesh so far was improved to 250 m mesh, and the heightening of forecast accuracy was examined, and the comparison with another wind velocity field forecast model 'SPEEDI' was carried out. As the results, there are the places where the correlation with other points of measurement is high and low, and it was found that for the forecast of wind velocity field, by excluding the data of the points with low correlation or installing simplified observation stations to take their data in, the forecast accuracy is improved. The outline of the investigation, the general analysis of weather observation data and the improvements of wind velocity field forecast model and forecast accuracy are reported. (K.I.)

  19. 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.

  20. Interval forecasts of a novelty hybrid model for wind speeds

    Directory of Open Access Journals (Sweden)

    Shanshan Qin

    2015-11-01

    Full Text Available The utilization of wind energy, as a booming technology in the field of renewable energies, has been highly regarded around the world. Quantification of uncertainties associated with accurate wind speed forecasts is essential for regulating wind power generation and integration. However, it remains difficult work primarily due to the stochastic and nonlinear characteristics of wind speed series. Traditional models for wind speed forecasting mostly focus on generating certain predictive values, which cannot properly handle uncertainties. For quantifying potential uncertainties, a hybrid model constructed by the Cuckoo Search Optimization (CSO-based Back Propagation Neural Network (BPNN is proposed to establish wind speed interval forecasts (IFs by estimating the lower and upper bounds. The quality of IFs is assessed quantitatively using IFs coverage probability (IFCP and IFs normalized average width (IFNAW. Moreover, to assess the overall quality of IFs comprehensively, a tradeoff between informativeness (IFNAW and validity (IFCP of IFs is examined by coverage width-based criteria (CWC. As an applicative study, wind speeds from the Xinjiang Region in China are used to validate the proposed hybrid model. The results demonstrate that the proposed model can construct higher quality IFs for short-term wind speed forecasts.

  1. 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

  2. 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...

  3. Improving Wind-Ramp Forecasts in the Stable Boundary Layer

    Science.gov (United States)

    Jahn, David E.; Takle, Eugene S.; Gallus, William A.

    2017-06-01

    The viability of wind-energy generation is dependent on highly accurate numerical wind forecasts, which are impeded by inaccuracies in model representation of boundary-layer processes. This study revisits the basic theory of the Mellor, Yamada, Nakanishi, and Niino (MYNN) planetary boundary-layer parametrization scheme, focusing on the onset of wind-ramp events related to nocturnal low-level jets. Modifications to the MYNN scheme include: (1) calculation of new closure parameters that determine the relative effects of turbulent energy production, dissipation, and redistribution; (2) enhanced mixing in the stable boundary layer when the mean wind speed exceeds a specified threshold; (3) explicit accounting of turbulent potential energy in the energy budget. A mesoscale model is used to generate short-term (24 h) wind forecasts for a set of 15 cases from both the U.S.A. and Germany. Results show that the new set of closure parameters provides a marked forecast improvement only when used in conjunction with the new mixing length formulation and only for cases that are originally under- or over-forecast (10 of the 15 cases). For these cases, the mean absolute error (MAE) of wind forecasts at turbine-hub height is reduced on average by 17%. A reduction in MAE values on average by 26% is realized for these same cases when accounting for the turbulent potential energy together with the new mixing length. This last method results in an average reduction by at least 13% in MAE values across all 15 cases.

  4. Wind Resource Assessment and Forecast Planning with Neural Networks

    Directory of Open Access Journals (Sweden)

    Nicolus K. Rotich

    2014-06-01

    Full Text Available In this paper we built three types of artificial neural networks, namely: Feed forward networks, Elman networks and Cascade forward networks, for forecasting wind speeds and directions. A similar network topology was used for all the forecast horizons, regardless of the model type. All the models were then trained with real data of collected wind speeds and directions over a period of two years in the municipal of Puumala, Finland. Up to 70th percentile of the data was used for training, validation and testing, while 71–85th percentile was presented to the trained models for validation. The model outputs were then compared to the last 15% of the original data, by measuring the statistical errors between them. The feed forward networks returned the lowest errors for wind speeds. Cascade forward networks gave the lowest errors for wind directions; Elman networks returned the lowest errors when used for short term forecasting.

  5. 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...

  6. On the Sizes of the North Atlantic Basin Tropical Cyclones Based on 34- and 64-kt Wind Radii Data, 2004-2013

    Science.gov (United States)

    Wilson, Robert M.

    2014-01-01

    At end of the 2012 hurricane season the National Hurricane Center retired the original HURDAT dataset and replaced it with the newer version HURDAT2, which reformatted the original data and included additional information, in particular, estimates of the 34-, 50, and 64-kt wind radii for the interval 2004-2013. During the brief 10-year interval, some 164 tropical cyclones are noted to have formed in the North Atlantic basin, with 77 becoming hurricanes. Hurricane Sandy (2012) stands out as being the largest individual storm that occurred in the North Atlantic basin during the 2004 -2013 timeframe, both in terms of its 34- and 64-kt wind radii and wind areas, having maximum 34- and 64-kt wind radii, maximum wind areas, and average wind areas each more than 2 standard deviations larger than the corresponding means. In terms of the largest yearly total 34-kt wind area (i.e., the sum of all individual storm 34-kt wind areas during the year), the year 2010 stands out as being the largest (about 423 × 10(exp 6) nmi(exp 2)), compared to the mean of about 174 × 10(exp 6) nmi(exp 2)), surpassing the year 2005 (353 x 10(exp 6) nmi(exp 2)) that had the largest number of individual storms (28). However, in terms of the largest yearly total 64-kt wind area, the year 2005 was the largest (about 9 × 10(exp 6) nmi(exp 2)), compared to the mean of about 3 × 106 nmi(exp 2)). Interesting is that the ratio of total 64-kt wind area to total 34-kt wind area has decreased over time, from 0.034 in 2004 to 0.008 in 2013.

  7. High resolution probabilistic forecasting for wind energy applications.

    Science.gov (United States)

    Courtney, J.; Sweeney, C.; Lynch, P.

    2012-04-01

    This project aims to produce the best possible wind speed forecasts for the wind energy industry by using an optimal combination of well-established forecasting and post-processing methods. We start with the ECMWF 51 member ensemble prediction system (EPS) and produce a more accurate forecast than the ensemble mean. The 51 members are clustered to 8 weighted representative members (RMs) using a clustering technique. The 8 RMs are chosen to minimize the within-cluster spread, while maximizing the inter-cluster spread. The forecasts are then downscaled using two limited area models, WRF and COSMO, at two resolutions, 14km and 3km. Numerical weather prediction is far from perfect and each of the ensemble member forecasts contains errors, both systematic and chaotic. Systematic errors can be minimized with statistical post-processing. We apply four adaptive post-processing methods to each forecast which require only a short training period. The weighted ensemble mean of the post-processed ensembles is used as the input to a Bayesian Model Averaging (BMA) system. Each ensemble forecast probability density function (PDF) is weighted based on how well it has performed over a training period. The weighted PDFs are then summed to form the BMA PDF which represents the probability of all possible wind speeds and has been proven to outperform the ensemble mean. We present a detailed description of the above process and detail some preliminary results.

  8. Incorporating geostrophic wind information for improved space–time short-term wind speed forecasting

    KAUST Repository

    Zhu, Xinxin

    2014-09-01

    Accurate short-term wind speed forecasting is needed for the rapid development and efficient operation of wind energy resources. This is, however, a very challenging problem. Although on the large scale, the wind speed is related to atmospheric pressure, temperature, and other meteorological variables, no improvement in forecasting accuracy was found by incorporating air pressure and temperature directly into an advanced space-time statistical forecasting model, the trigonometric direction diurnal (TDD) model. This paper proposes to incorporate the geostrophic wind as a new predictor in the TDD model. The geostrophic wind captures the physical relationship between wind and pressure through the observed approximate balance between the pressure gradient force and the Coriolis acceleration due to the Earth’s rotation. Based on our numerical experiments with data from West Texas, our new method produces more accurate forecasts than does the TDD model using air pressure and temperature for 1to 6-hour-ahead forecasts based on three different evaluation criteria. Furthermore, forecasting errors can be further reduced by using moving average hourly wind speeds to fit the diurnal pattern. For example, our new method obtains between 13.9% and 22.4% overall mean absolute error reduction relative to persistence in 2-hour-ahead forecasts, and between 5.3% and 8.2% reduction relative to the best previous space-time methods in this setting.

  9. Short-Term Wind Speed Hybrid Forecasting Model Based on Bias Correcting Study and Its Application

    OpenAIRE

    Mingfei Niu; Shaolong Sun; Jie Wu; Yuanlei Zhang

    2015-01-01

    The accuracy of wind speed forecasting is becoming increasingly important to improve and optimize renewable wind power generation. In particular, reliable short-term wind speed forecasting can enable model predictive control of wind turbines and real-time optimization of wind farm operation. However, due to the strong stochastic nature and dynamic uncertainty of wind speed, the forecasting of wind speed data using different patterns is difficult. This paper proposes a novel combination bias c...

  10. 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...... 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....... Despite the fact that increasing accuracy in spot forecasts may reduce penalties, this paper shows that, if such forecasts are accompanied with information on their uncertainty, i.e., in the form of predictive distributions, then this can be the basis for defining advanced strategies for market...

  11. 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

  12. 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.

  13. Forecasting for Wind Power Plants in Quebec, Canada

    Science.gov (United States)

    Dyck, Sarah

    2010-05-01

    The SPEO project (Système de Prévisions ÉOlienne) is a collaborative research effort between Environment Canada (EC) and Hydro-Québec (HQ) and has been in operation since May of 2007. It provides a 48 hour high resolution (2.5 km) wind forecast, four times daily, in order to assist in the successful management of wind power from plants in the Gaspé Region of Québec, Canada. Early in 2010, the number of forecasts increased to four times a day. The Canadian Global Environmental Multiscale-Limited Area Model (GEM-LAM), at the heart of this system, is driven by the operational regional forecasts at 15 km resolution from the Canadian Meteorological Centre. This system has been evaluated using observations from EC meteorological stations and special masts installed at wind power plants and the results will be discussed. Specifically, an effort was made to examine the predictability of rare events critical in the operations of wind power plants such as strong winds and high atmospheric turbulence, which can force wind turbines to shut down. Future research for the improvement of this forecasting system will also be presented.

  14. Use of ground-based wind profiles in mesoscale forecasting

    Science.gov (United States)

    Schlatter, Thomas W.

    1985-01-01

    A brief review is presented of recent uses of ground-based wind profile data in mesoscale forecasting. Some of the applications are in real time, and some are after the fact. Not all of the work mentioned here has been published yet, but references are given wherever possible. As Gage and Balsley (1978) point out, sensitive Doppler radars have been used to examine tropospheric wind profiles since the 1970's. It was not until the early 1980's, however, that the potential contribution of these instruments to operational forecasting and numerical weather prediction became apparent. Profiler winds and radiosonde winds compare favorably, usually within a few m/s in speed and 10 degrees in direction (see Hogg et al., 1983), but the obvious advantage of the profiler is its frequent (hourly or more often) sampling of the same volume. The rawinsonde balloon is launched only twice a day and drifts with the wind. In this paper, I will: (1) mention two operational uses of data from a wind profiling system developed jointly by the Wave Propagation and Aeronomy Laboratories of NOAA; (2) describe a number of displays of these same data on a workstation for mesoscale forecasting developed by the Program for Regional Observing and Forecasting Services (PROFS); and (3) explain some interesting diagnostic calculations performed by meteorologists of the Wave Propagation Laboratory.

  15. 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...... to substantial deployment of generating capacities and increased temporal resolution, it may now be possible to build large models accounting for all space-time dependencies. This will eventually allow to significantly improve the quality of short-term renewable power forecasts. However, in practice, it is often...... to large datasets in Australia (22 wind farms) and France (85 wind farms) are used to illustrate the interest and performance of our proposal....

  16. 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...

  17. The impact of scatterometer wind data on global weather forecasting

    Science.gov (United States)

    Atlas, D.; Baker, W. E.; Kalnay, E.; Halem, M.; Woiceshyn, P. M.; Peteherych, S.

    1984-01-01

    The impact of SEASAT-A scatterometer (SASS) winds on coarse resolution atmospheric model forecasts was assessed. The scatterometer provides high resolution winds, but each wind can have up to four possible directions. One wind direction is correct; the remainder are ambiguous or "aliases'. In general, the effect of objectively dealiased-SASS data was found to be negligible in the Northern Hemisphere. In the Southern Hemisphere, the impact was larger and primarily beneficial when vertical temperature profile radiometer (VTPR) data was excluded. However, the inclusion of VTPR data eliminates the positive impact, indicating some redundancy between the two data sets.

  18. 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

  19. Wind Speed Forecasting Using Hybrid Wavelet Transform—ARMA Techniques

    Directory of Open Access Journals (Sweden)

    Diksha Kaur

    2015-01-01

    Full Text Available The objective of this paper is to develop a novel wind speed forecasting technique, which produces more accurate prediction. The Wavelet Transform (WT along with the Auto Regressive Moving Average (ARMA is chosen to form a hybrid whose combination is expected to give minimum Mean Absolute Prediction Error (MAPE. A simulation study has been conducted by comparing the forecasting results using the Wavelet-ARMA with the ARMA and Artificial Neural Network (ANN-Ensemble Kalman Filter (EnKF hybrid technique to verify the effectiveness of the proposed hybrid method. Results of the proposed hybrid show significant improvements in the forecasting error.

  20. Wind Speed Forecasting by Wavelet Neural Networks: A Comparative Study

    Directory of Open Access Journals (Sweden)

    Chuanan Yao

    2013-01-01

    Full Text Available Due to the environmental degradation and depletion of conventional energy, much attention has been devoted to wind energy in many countries. The intermittent nature of wind power has had a great impact on power grid security. Accurate forecasting of wind speed plays a vital role in power system stability. This paper presents a comparison of three wavelet neural networks for short-term forecasting of wind speed. The first two combined models are two types of basic combinations of wavelet transform and neural network, namely, compact wavelet neural network (CWNN and loose wavelet neural network (LWNN in this study, and the third model is a new hybrid method based on the CWNN and LWNN models. The efficiency of the combined models has been evaluated by using actual wind speed from two test stations in North China. The results show that the forecasting performances of the CWNN and LWNN models are unstable and are affected by the test stations selected; the third model is far more accurate than the other forecasting models in spite of the drawback of lower computational efficiency.

  1. 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.

  2. 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)

  3. 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)

  4. 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...

  5. Forecast of icing events at a wind farm in Sweden

    DEFF Research Database (Denmark)

    Davis, Neil; Hahmann, Andrea N.; Clausen, Niels-Erik

    2014-01-01

    This paper introduces a method for identifying icing events using a physical icing model, driven by atmospheric data from the Weather Research and Forecasting (WRF) model, and applies it to a wind park in Sweden. Observed wind park icing events were identified by deviation from an idealized power...... curve and observed temperature. The events were modeled using a physical icing model with equations for both accretion and ablation mechanisms (iceBlade). The accretion model is based on the Makkonen model but was modified to make it applicable to the blades of a wind turbine rather than a static...

  6. Inclusion of routine wind and turbulence forecasts in the Savannah River Plant's emergency response capabilities

    International Nuclear Information System (INIS)

    Pendergast, M.M.; Gilhousen, D.B.

    1980-01-01

    The Savannah River Plant's emergency response computer system was improved by the implementation of automatic forecasts of wind and turbulence for periods up to 30 hours. The forecasts include wind direction, wind speed, and horizontal and vertical turbulence intensity at 10, 91, and 243 m above ground for the SRP area, and were obtained by using the Model Output Statistics (MOS) technique. A technique was developed and tested to use the 30-hour MOS forecasts of wind and turbulence issued twice daily from the National Weather Service at Suitland, Maryland, into SRP's emergency response program. The technique for combining MOS forecasts, persistence, and adjusted-MOS forecast is used to generate good forecasts any time of day. Wind speed and turbulence forecasts have been shown to produce smaller root mean square errors (RMSE) than forecasts of persistence for time periods over about two hours. For wind direction, the adjusted-MOS forecasts produce smaller RMSE than persistence for times greater than four hours

  7. Forecasting Production Losses at a Swedish Wind Farm

    DEFF Research Database (Denmark)

    Production loss due to icing has been identified as a problem both when siting turbines in cold climates, and when making forecasts of energy production for wind park management and energy markets. The Makkonen icing model (Makkonen, 2000), driven by output from the WRF mesoscale model, has been...... shown to predict periods of icing at a wind farm in northern Sweden (Davis et al, 2012) with improved skill compared to persistence and threshold models. Based on these results, we have developed a statistical model to estimate the loss of production at the wind park due to these icing periods. We...... compared this statistical model with a simpler method that does not rely on a physical icing model. In that method meteorological icing is identified as periods when WRF forecasts clouds and the temperature is below freezing. During these periods it is assumed that there is no production from the turbines...

  8. Advanced mesoscale forecasts of icing events for Gaspe wind farms

    International Nuclear Information System (INIS)

    Gayraud, A.; Benoit, R.; Camion, A.

    2009-01-01

    Atmospheric icing includes every event which causes ice accumulations of various shapes on different structures. In terms of its effects on wind farms, atmospheric icing can decrease the aerodynamic performance, cause structure overloading, and add vibrations leading to failure and breaking. This presentation discussed advanced mesoscale forecasts of icing events for Gaspe wind farms. The context of the study was discussed with particular reference to atmospheric icing; effects on wind farms; and forecast objectives. The presentation also described the models and results of the study. These included MC2, a compressible community model, as well as a Milbrandt and Yau condensation scheme. It was shown that the study has provided good estimates of the duration of events as well as reliable precipitation categories. tabs., figs.

  9. Pollutant forecasting error based on persistence of wind direction

    International Nuclear Information System (INIS)

    Cooper, R.E.

    1978-01-01

    The purpose of this report is to provide a means of estimating the reliability of forecasts of downwind pollutant concentrations from atmospheric puff releases. These forecasts are based on assuming the persistence of wind direction as determined at the time of release. This initial forecast will be used to deploy survey teams, to predict population centers that may be affected, and to estimate the amount of time available for emergency response. Reliability of forecasting is evaluated by developing a cumulative probability distribution of error as a function of lapsed time following an assumed release. The cumulative error is determined by comparing the forecast pollutant concentration with the concentration measured by sampling along the real-time meteorological trajectory. It may be concluded that the assumption of meteorological persistence for emergency response is not very good for periods longer than 3 hours. Even within this period, the possibiity for large error exists due to wind direction shifts. These shifts could affect population areas totally different from those areas first indicated

  10. Simulation of Wind-Battery Microgrid Based on Short-Term Wind Power Forecasting

    Directory of Open Access Journals (Sweden)

    Konstantinos N. Genikomsakis

    2017-11-01

    Full Text Available The inherently intermittent and highly variable nature of wind necessitates the use of wind power forecasting tools in order to facilitate the integration of wind turbines in microgrids, among others. In this direction, the present paper describes the development of a short-term wind power forecasting model based on artificial neural network (ANN clustering, which uses statistical feature parameters in the input vector, as well as an enhanced version of this approach that adjusts the ANN output with the probability of lower misclassification (PLM method. Moreover, it employs the Monte Carlo simulation to represent the stochastic variation of wind power production and assess the impact of energy management decisions in a residential wind-battery microgrid using the proposed wind power forecasting models. The results indicate that there are significant benefits for the microgrid when compared to the naïve approach that is used for benchmarking purposes, while the PLM adjustment method provides further improvements in terms of forecasting accuracy.

  11. 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.

  12. 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......-varying regression model. In a second step, time-series models, i.e., ARMA and Holt–Winters, are applied to account for residual autocorrelation and seasonal dynamics. Empirical results are presented for out-of-sample forecasts of day-ahead prices in the Western Danish price area of Nord Pool's Elspot, during a two...

  13. Solar and wind forecasting by NARX neural networks

    Directory of Open Access Journals (Sweden)

    Di Piazza Annalisa

    2016-01-01

    Full Text Available The nonlinear autoregressive network with exogenous input (NARX is used to perform hourly solar irradiation and wind speed forecasting, according to a multi-step ahead approach. Temperature has been considered as the exogenous variable. The NARX topology selection is supported by a combined use of two techniques: (1 a genetic algorithm (GA-based optimization technique and (2 a method that determines the optimal network architecture by pruning (optimal brain surgeon (OBS strategy. The considered variables are observed at hourly scale in a seven year dataset and the forecasting is done for several time horizons in the range from 8 to 24 h ahead.

  14. 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.)

  15. Learning to forecast wind at remote sites for wind energy applications. Final report

    Energy Technology Data Exchange (ETDEWEB)

    Notis, C.; Trettel, D.W.; Aquino, J.T.; Piazza, T.R.; Taylor, L.E.; Trask, D.C.; Wegley, H.L.; Miller, A.H.

    1983-01-01

    Observed wind patterns are correlated with synoptic or mescoscale weather systems. Six sites selected for analysis include Montauk Point, New York; Boone, North Carolina; Ludington, Michigan; Clayton, New Mexico; Amarillo, Texas; and San Gorgonio Pass, California. Objectives of the analysis are: to identify synoptic and/or mesoscale weather patterns that are associated with recognizable wind events at the sites; to define a set of criteria that uniquely describes such weather patterns; to estimate the reliability (accuracy) of forecasting rules derived from the association of weather patterns and site winds; and to attempt to separate any mesoscale effects of local topography from the synoptic-scale effects. One-to-one mapping of wind regimes onto synoptic types was not found. It was concluded that four factors should be examined when stratifying wind regimes: synoptic situation, descriptive climatology, pressure gradient vector, and winds aloft. The wind forecasting approach developed was intended for forecasting hourly average winds out to the 24 hour or possibly 36 hour time horizon. (LEW)

  16. 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.

  17. 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 art......? 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.......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...

  18. Wind power forecasting for a real onshore wind farm on complex terrain using WRF high resolution simulations.

    Science.gov (United States)

    Ángel Prósper Fernández, Miguel; Casal, Carlos Otero; Canoura Fernández, Felipe; Miguez-Macho, Gonzalo

    2017-04-01

    Regional meteorological models are becoming a generalized tool for forecasting wind resource, due to their capacity to simulate local flow dynamics impacting wind farm production. This study focuses on the production forecast and validation of a real onshore wind farm using high horizontal and vertical resolution WRF (Weather Research and Forecasting) model simulations. The wind farm is located in Galicia, in the northwest of Spain, in a complex terrain region with high wind resource. Utilizing the Fitch scheme, specific for wind farms, a period of one year is simulated with a daily operational forecasting set-up. Power and wind predictions are obtained and compared with real data provided by the management company. Results show that WRF is able to yield good wind power operational predictions for this kind of wind farms, due to a good representation of the planetary boundary layer behaviour of the region and the good performance of the Fitch scheme under these conditions.

  19. Validation of Model Forecasts of the Ambient Solar Wind

    Science.gov (United States)

    Macneice, P. J.; Hesse, M.; Kuznetsova, M. M.; Rastaetter, L.; Taktakishvili, A.

    2009-01-01

    Independent and automated validation is a vital step in the progression of models from the research community into operational forecasting use. In this paper we describe a program in development at the CCMC to provide just such a comprehensive validation for models of the ambient solar wind in the inner heliosphere. We have built upon previous efforts published in the community, sharpened their definitions, and completed a baseline study. We also provide first results from this program of the comparative performance of the MHD models available at the CCMC against that of the Wang-Sheeley-Arge (WSA) model. An important goal of this effort is to provide a consistent validation to all available models. Clearly exposing the relative strengths and weaknesses of the different models will enable forecasters to craft more reliable ensemble forecasting strategies. Models of the ambient solar wind are developing rapidly as a result of improvements in data supply, numerical techniques, and computing resources. It is anticipated that in the next five to ten years, the MHD based models will supplant semi-empirical potential based models such as the WSA model, as the best available forecast models. We anticipate that this validation effort will track this evolution and so assist policy makers in gauging the value of past and future investment in modeling support.

  20. Evaluation of Dynamical Downscaling Resolution Effect on Wind Energy Forecast Value for a Wind Farm in Central Sweden

    DEFF Research Database (Denmark)

    Rosgaard, Martin Haubjerg; Hahmann, Andrea N.; Nielsen, Torben Skov

    2014-01-01

    For any energy system relying on wind power, accurate forecasts of wind fluctuations are essential for efficient integration into the power grid. Increased forecast precision allows end-users to plan day-ahead operation with reduced risk of penalties which in turn supports the feasibility of wind...... energy. The present study aims to quantify value added to wind energy forecasts in the 12-48 hour leadtime by downscaling global numerical weather prediction (NWP) data from the National Centers for Environmental Prediction Global Forecast System (GFS) using the limited-area NWP model described...

  1. 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.

  2. 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

    In most operational wind power forecasting systems statistical methods are applied to map wind forecasts from numerical weather prediction (NWP) models into wind power forecasts. NWP models are complex mathematical models of the atmosphere that divide the earth’s surface into a grid. The spatial...... 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...

  3. Potential of Offshore Wind Energy and Extreme Wind Speed Forecasting on the West Coast of Taiwan

    Directory of Open Access Journals (Sweden)

    Pei-Chi Chang

    2015-02-01

    Full Text Available It is of great importance and urgency for Taiwan to develop offshore wind power. However, relevant data on offshore wind energy resources are limited. This study imported wind speeds measured by a tidal station and a buoy into the software WAsP to estimate the high-altitude wind speeds in the two areas. A light detection and ranging (Lidar system was set up near the tidal station and buoy. High-altitude wind speeds measured by the Lidar system were compared with the WAsP-estimated values, and it was discovered that the two data sets were consistent. Then, long-term wind speed data observed by buoys and tidal stations at various locations were imported into WAsP to forecast wind speeds at heights of 55–200 m on the west coast of Taiwan. The software WAsP Engineering was used to analyze the extreme wind speeds in the same areas. The results show that wind speeds at 100 m are approximately 9.32–11.24 m/s, which means that the coastal areas of west Taiwan are rich in wind energy resources. When a long-term 10-min average wind speed is used, the extreme wind speed on the west coast is estimated to be between 36.4 and 55.3 m/s.

  4. Different Models for Forecasting Wind Power Generation: Case Study

    Directory of Open Access Journals (Sweden)

    David Barbosa de Alencar

    2017-11-01

    Full Text Available Generation of electric energy through wind turbines is one of the practically inexhaustible alternatives of generation. It is considered a source of clean energy, but still needs a lot of research for the development of science and technologies that ensures uniformity in generation, providing a greater participation of this source in the energy matrix, since the wind presents abrupt variations in speed, density and other important variables. In wind-based electrical systems, it is essential to predict at least one day in advance the future values of wind behavior, in order to evaluate the availability of energy for the next period, which is relevant information in the dispatch of the generating units and in the control of the electrical system. This paper develops ultra-short, short, medium and long-term prediction models of wind speed, based on computational intelligence techniques, using artificial neural network models, Autoregressive Integrated Moving Average (ARIMA and hybrid models including forecasting using wavelets. For the application of the methodology, the meteorological variables of the database of the national organization system of environmental data (SONDA, Petrolina station, from 1 January 2004 to 31 March 2017, were used. A comparison among results by different used approaches is also done and it is also predicted the possibility of power and energy generation using a certain kind of wind generator.

  5. The Forecasting Procedure for Long-Term Wind Speed in the Zhangye Area

    OpenAIRE

    Guo, Zhenhai; Dong, Yao; Wang, Jianzhou; Lu, Haiyan

    2010-01-01

    Energy crisis has made it urgent to find alternative energy sources for sustainable energy supply; wind energy is one of the attractive alternatives. Within a wind energy system, the wind speed is one key parameter; accurately forecasting of wind speed can minimize the scheduling errors and in turn increase the reliability of the electric power grid and reduce the power market ancillary service costs. This paper proposes a new hybrid model for long-term wind speed forecasting based on the fir...

  6. Forecasting wind-driven wildfires using an inverse modelling approach

    Directory of Open Access Journals (Sweden)

    O. Rios

    2014-06-01

    Full Text Available A technology able to rapidly forecast wildfire dynamics would lead to a paradigm shift in the response to emergencies, providing the Fire Service with essential information about the ongoing fire. This paper presents and explores a novel methodology to forecast wildfire dynamics in wind-driven conditions, using real-time data assimilation and inverse modelling. The forecasting algorithm combines Rothermel's rate of spread theory with a perimeter expansion model based on Huygens principle and solves the optimisation problem with a tangent linear approach and forward automatic differentiation. Its potential is investigated using synthetic data and evaluated in different wildfire scenarios. The results show the capacity of the method to quickly predict the location of the fire front with a positive lead time (ahead of the event in the order of 10 min for a spatial scale of 100 m. The greatest strengths of our method are lightness, speed and flexibility. We specifically tailor the forecast to be efficient and computationally cheap so it can be used in mobile systems for field deployment and operativeness. Thus, we put emphasis on producing a positive lead time and the means to maximise it.

  7. A high resolution WRF model for wind energy forecasting

    Science.gov (United States)

    Vincent, Claire Louise; Liu, Yubao

    2010-05-01

    The increasing penetration of wind energy into national electricity markets has increased the demand for accurate surface layer wind forecasts. There has recently been a focus on forecasting the wind at wind farm sites using both statistical models and numerical weather prediction (NWP) models. Recent advances in computing capacity and non-hydrostatic NWP models means that it is possible to nest mesoscale models down to Large Eddy Simulation (LES) scales over the spatial area of a typical wind farm. For example, the WRF model (Skamarock 2008) has been run at a resolution of 123 m over a wind farm site in complex terrain in Colorado (Liu et al. 2009). Although these modelling attempts indicate a great hope for applying such models for detailed wind forecasts over wind farms, one of the obvious challenges of running the model at this resolution is that while some boundary layer structures are expected to be modelled explicitly, boundary layer eddies into the inertial sub-range can only be partly captured. Therefore, the amount and nature of sub-grid-scale mixing that is required is uncertain. Analysis of Liu et al. (2009) modelling results in comparison to wind farm observations indicates that unrealistic wind speed fluctuations with a period of around 1 hour occasionally occurred during the two day modelling period. The problem was addressed by re-running the same modelling system with a) a modified diffusion constant and b) two-way nesting between the high resolution model and its parent domain. The model, which was run with horizontal grid spacing of 370 m, had dimensions of 505 grid points in the east-west direction and 490 points in the north-south direction. It received boundary conditions from a mesoscale model of resolution 1111 m. Both models had 37 levels in the vertical. The mesoscale model was run with a non-local-mixing planetary boundary layer scheme, while the 370 m model was run with no planetary boundary layer scheme. It was found that increasing the

  8. Time-consistent calibration of short-term regional wind power ensemble forecasts

    Directory of Open Access Journals (Sweden)

    Stephan Späth

    2015-04-01

    Full Text Available With increasing wind power capacity, accurate uncertainty forecasts get more and more important for grid integration. The uncertainty of forecasts can be quantified by ensemble forecasts. We use ensemble forecasts from the COSMO-DE EPS to generate short-term ensemble forecasts of regionally aggregated wind power. The wind power forecasts are generated by an optimised regional power curve model that is based on minimum score estimation and leads to wind power forecasts with small deterministic errors. Remaining bias and dispersion errors in the wind power forecasts are removed by statistical post-processing (also called calibration with ensemble model output statistics and the temporal rank correlation of the raw ensemble is maintained by ensemble copula coupling. The verification of raw and calibrated ensembles shows both strong improvements by calibration and the benefit of ensuring time consistency with ensemble copula coupling. The improvements are indicated by the multivariate energy score as well as in a proposed univariate verification approach that is based on integrated wind power forecast and measurement trajectories. Slight deficits in time consistency of the forecasts remain because the theoretical assumptions of ensemble copula coupling are not always fulfilled as the COSMO-DE EPS is based on distinguishable ensemble members. The more training days are used for calibration against measurements of regionally aggregated wind power, the lower is the improvement by calibration which contradicts former results for different variables like wind speed.

  9. 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.

  10. 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

  11. Evaluation of Dynamical Downscaling Resolution Effect on Wind Energy Forecast Value for a Wind Farm in Central Sweden

    DEFF Research Database (Denmark)

    Rosgaard, Martin Haubjerg; Hahmann, Andrea N.; Nielsen, Torben Skov

    For any energy system relying on wind power, accurate forecasts of wind fluctuations are essential for efficient utilisation in the power grid. Statistical wind power prediction tools [1] use numerical weather prediction (NWP) model data along with measurements and can correct magnitude errors op...... the two time series. Results on limited-area NWP model performance, with focus on the 12th to 48th forecast hour horizon relevant for Elspot auction bidding on the Nord Pool Spot market [2], are presented....

  12. Evaluation of Dynamical Downscaling Resolution Effect on Wind Energy Forecast Value for a Wind Farm in Central Sweden

    DEFF Research Database (Denmark)

    Rosgaard, Martin Haubjerg; Hahmann, Andrea N.; Nielsen, Torben Skov

    For any energy system relying on wind power, accurate forecasts of wind fluctuations are essential for efficient utilisation in the power grid. Statistical wind power prediction tools [1] use numerical weather prediction (NWP) model data along with measurements and can correct magnitude errors...... the two time series. Results on limited-area NWP model performance, with focus on the 12th to 48th forecast hour horizon relevant for Elspot auction bidding on the Nord Pool Spot market [2], are presented....

  13. 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...

  14. The Forecasting Procedure for Long-Term Wind Speed in the Zhangye Area

    Directory of Open Access Journals (Sweden)

    Zhenhai Guo

    2010-01-01

    Full Text Available Energy crisis has made it urgent to find alternative energy sources for sustainable energy supply; wind energy is one of the attractive alternatives. Within a wind energy system, the wind speed is one key parameter; accurately forecasting of wind speed can minimize the scheduling errors and in turn increase the reliability of the electric power grid and reduce the power market ancillary service costs. This paper proposes a new hybrid model for long-term wind speed forecasting based on the first definite season index method and the Autoregressive Moving Average (ARMA models or the Generalized Autoregressive Conditional Heteroskedasticity (GARCH forecasting models. The forecasting errors are analyzed and compared with the ones obtained from the ARMA, GARCH model, and Support Vector Machine (SVM; the simulation process and results show that the developed method is simple and quite efficient for daily average wind speed forecasting of Hexi Corridor in China.

  15. “Section to Point” Correction Method for Wind Power Forecasting Based on Cloud Theory

    Directory of Open Access Journals (Sweden)

    Dunnan Liu

    2015-01-01

    Full Text Available As an intermittent energy, wind power has the characteristics of randomness and uncontrollability. It is of great significance to improve the accuracy of wind power forecasting. Currently, most models for wind power forecasting are based on wind speed forecasting. However, it is stuck in a dilemma called “garbage in, garbage out,” which means it is difficult to improve the forecasting accuracy without improving the accuracy of input data such as the wind speed. In this paper, a new model based on cloud theory is proposed. It establishes a more accurate relational model between the wind power and wind speed, which has lots of catastrophe points. Then, combined with the trend during adjacent time and the laws of historical data, the forecasting value will be corrected by the theory of “section to point” correction. It significantly improves the stability of forecasting accuracy and reduces significant forecasting errors at some particular points. At last, by analyzing the data of generation power and historical wind speed in Inner Mongolia, China, it is proved that the proposed method can effectively improve the accuracy of wind speed forecasting.

  16. 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....

  17. On practical challenges of decomposition-based hybrid forecasting algorithms for wind speed and solar irradiation

    International Nuclear Information System (INIS)

    Wang, Yamin; Wu, Lei

    2016-01-01

    This paper presents a comprehensive analysis on practical challenges of empirical mode decomposition (EMD) based algorithms on wind speed and solar irradiation forecasts that have been largely neglected in literature, and proposes an alternative approach to mitigate such challenges. Specifically, the challenges are: (1) Decomposed sub-series are very sensitive to the original time series data. That is, sub-series of the new time series, consisting of the original one plus a limit number of new data samples, may significantly differ from those used in training forecasting models. In turn, forecasting models established by original sub-series may not be suitable for newly decomposed sub-series and have to be trained more frequently; and (2) Key environmental factors usually play a critical role in non-decomposition based methods for forecasting wind speed and solar irradiation. However, it is difficult to incorporate such critical environmental factors into forecasting models of individual decomposed sub-series, because the correlation between the original data and environmental factors is lost after decomposition. Numerical case studies on wind speed and solar irradiation forecasting show that the performance of existing EMD-based forecasting methods could be worse than the non-decomposition based forecasting model, and are not effective in practical cases. Finally, the approximated forecasting model based on EMD is proposed to mitigate the challenges and achieve better forecasting results than existing EMD-based forecasting algorithms and the non-decomposition based forecasting models on practical wind speed and solar irradiation forecasting cases. - Highlights: • Two challenges of existing EMD-based forecasting methods are discussed. • Significant changes of sub-series in each step of the rolling forecast procedure. • Difficulties in incorporating environmental factors into sub-series forecasting models. • The approximated forecasting method is proposed to

  18. Early warnings of extreme winds using the ECMWF Extreme Forecast Index

    OpenAIRE

    Petroliagis, Thomas I.; Pinson, Pierre

    2014-01-01

    The European FP7 SafeWind Project aims at developing research towards a European vision of wind power forecasting, which requires advanced meteorological support concerning extreme wind events. This study is focused mainly on early warnings of extreme winds in the early medium-range. Three synoptic stations (airports) of North Germany (Bremen, Hamburg and Hannover) were considered for the construction of time series of daily maximum wind speeds. All daily wind extremes were found to be linked...

  19. 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.

  20. Spatio-temporal modelling for short term wind power forecasts. Why, when and how.

    Science.gov (United States)

    Lenzi, Amanda; Steinsland, Ingelin; Pinson, Pierre

    2017-04-01

    This study is based on a case study of 349 wind farms in Western Denmark with available energy production every 15 minutes for 6 years. Our aim is to do short term forecasting up to 5 hours ahead based on previous observations. We want sharp and calibrated probabilistic forecasts for both individual wind farms and for aggregated energy production, for example the energy production in the whole region. To obtain this we propose two Bayesian spatio-temporal models, and obtain full probabilistic forecasts of wind power. The models are based on the stochastic partial differential equation (SPDE) approach to spatial-temporal modelling which enables fast inference using integrated nested Laplace approximations (INLA) as well as dimension reduction. We provide detailed analysis on the forecast performances on the individual and aggregated level based on appropriate metrics tailored for probability forecasts for both the spatial temporal models as well as for temporal models for individual wind farms. The case study as well as simulation studies demonstrate that forecasts that are individually reliable do not need to produce an aggregated forecasts that are reliable. Indeed, the case study shows that even when all individual forecasts are calibrated can the aggregated forecasts be so uncalibrated that less that 20% of the observations fall within the 95% forecast interval. T he results and methodology are both relevant for wind power forecasts in other regions as well as for spatial-temporal modeling and decisions in general.

  1. 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...

  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. A Peak Wind Probability Forecast Tool for Kennedy Space Center and Cape Canaveral Air Force Station

    Science.gov (United States)

    Crawford, Winifred; Roeder, William

    2008-01-01

    This conference abstract describes the development of a peak wind forecast tool to assist forecasters in determining the probability of violating launch commit criteria (LCC) at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) in east-central Florida. The peak winds are an important forecast element for both the Space Shuttle and Expendable Launch Vehicle (ELV) programs. The LCC define specific peak wind thresholds for each launch operation that cannot be exceeded in order to ensure the safety of the vehicle. The 45th Weather Squadron (45 WS) has found that peak winds are a challenging parameter to forecast, particularly in the cool season months of October through April. Based on the importance of forecasting peak winds, the 45 WS tasked the Applied Meteorology Unit (AMU) to develop a short-range peak-wind forecast tool to assist in forecasting LCC violatioas.The tool will include climatologies of the 5-minute mean end peak winds by month, hour, and direction, and probability distributions of the peak winds as a function of the 5-minute mean wind speeds.

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

    DEFF Research Database (Denmark)

    Trombe, Pierre-Julien

    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...... of high and low variability. They also yield substantial gains in probabilistic forecast accuracy for lead times of a few minutes. However, these models only integrate historical and local measurements of wind power and thus have a limited ability for notifying regime changes for larger lead times....... For that purpose, there is a long tradition in using meteorological forecasts of wind speed and direction that are converted into wind power forecasts. Nevertheless, meteorological forecasts are not informative on the intra-hour wind variability and thus cannot be used in the present context focusing on temporal...

  5. Forecasting Downdraft Wind Speeds Associated with Airmass Thunderstorms for Peterson Air Force Base, Colorado, Using the WSR-88D

    National Research Council Canada - National Science Library

    Steen, Travis

    1999-01-01

    .... During the same summer, Air Force Space Command units issued nearly 65% of their weather warnings for convective winds, making the forecasting of convective winds the most frequent challenge to forecasters...

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. An integrated system for wind energy forecast using meteorological models and statistical post-processing

    Science.gov (United States)

    Miranda, P.; Rodrigues, A.; Lopes, J.; Palma, J.; Tome, R.; Sousa, J.; Bessa, R.; Matos, J.

    2009-12-01

    With 3GW of installed wind turbines, corresponding to 23% of the total electric grid, and a 5-year plan that will grow that value above 5GW (near 40% of the grid), Portugal has been a recent success case for renewable energy development. Clearly such large share of wind energy in the national electric system implies a strong requirement for accurate wind forecasts, that can be used to forecast this highly variable energy source and allow for timely decision making in the energy markets, namely for on and off switching of alternative conventional sources. In the past 3 years, a system for 72h energy forecast in mainland Portugal was setup, using 6km resolution meteorological forecasts, forced by global GFS forecasts by NCEP. In the development phase, different boundary conditions (from NCEP and ECMWF) were tested, as well as different limited area models (namely MM5, Aladin, MesoNH and WRF) at resolutions from 12 to 2km, which were evaluated by comparison with wind observations at heights relevant for wind turbines (up to 80m) in different locations and for different synoptic conditions. The developed system, which works with a real time connection with wind farms, also includes a post-processing code that merges recent wind observations with the meteorological forecast, and converts the forecasted wind fields into forecasted energy, by incorporating empirical transfer functions of the wind farm. Wind conditions in Portugal are highly influenced by topography, as most wind farms are located in complex terrain, often in mountainous terrain, where stratification plays a significant role. Coastal effects are also highly relevant, especially during the Summer, where a strong diurnal cycle of the sea-breeze is superimposed on an equally strong boundary layer development, both with a significant impact on low level winds. These two ingredients tend to complicate wind forecasts, requiring fully developed meteorological models. In general, results from 2 full years of

  11. Improving Wind Ramp Forecasts by the HRRR System via Statistical Postprocessing

    Science.gov (United States)

    Worsnop, Rochelle; Scheuerer, Michael; Hamill, Thomas M.

    2017-04-01

    Wind power forecasting is gaining enormous international significance as more and more countries and regions enact policies to increase the use of renewable energy. Wind ramps pose a particular challenge in decision-making processes in the wind energy industry since a sudden decrease or increase in wind energy production must be balanced by conventional power generators and could be costly for wind farm operators. In this study, we assess the performance of the High-Resolution Rapid Refresh (HRRR) numerical weather prediction model in predicting wind ramps with up to 12 hours of lead time at two wind tower locations in the United States. Novel statistical postprocessing methodology is used to generate scenarios of short-term wind power production; this probabilistic enhancement of the deterministic HRRR forecasts significantly improves the skill in predicting wind ramp events, and could be implemented by wind farm operators to support decision-making.

  12. 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.

  13. 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.

  14. Four wind speed multi-step forecasting models using extreme learning machines and signal decomposing algorithms

    International Nuclear Information System (INIS)

    Liu, Hui; Tian, Hong-qi; Li, Yan-fei

    2015-01-01

    Highlights: • A hybrid architecture is proposed for the wind speed forecasting. • Four algorithms are used for the wind speed multi-scale decomposition. • The extreme learning machines are employed for the wind speed forecasting. • All the proposed hybrid models can generate the accurate results. - Abstract: Realization of accurate wind speed forecasting is important to guarantee the safety of wind power utilization. In this paper, a new hybrid forecasting architecture is proposed to realize the wind speed accurate forecasting. In this architecture, four different hybrid models are presented by combining four signal decomposing algorithms (e.g., Wavelet Decomposition/Wavelet Packet Decomposition/Empirical Mode Decomposition/Fast Ensemble Empirical Mode Decomposition) and Extreme Learning Machines. The originality of the study is to investigate the promoted percentages of the Extreme Learning Machines by those mainstream signal decomposing algorithms in the multiple step wind speed forecasting. The results of two forecasting experiments indicate that: (1) the method of Extreme Learning Machines is suitable for the wind speed forecasting; (2) by utilizing the decomposing algorithms, all the proposed hybrid algorithms have better performance than the single Extreme Learning Machines; (3) in the comparisons of the decomposing algorithms in the proposed hybrid architecture, the Fast Ensemble Empirical Mode Decomposition has the best performance in the three-step forecasting results while the Wavelet Packet Decomposition has the best performance in the one and two step forecasting results. At the same time, the Wavelet Packet Decomposition and the Fast Ensemble Empirical Mode Decomposition are better than the Wavelet Decomposition and the Empirical Mode Decomposition in all the step predictions, respectively; and (4) the proposed algorithms are effective in the wind speed accurate predictions

  15. Using ensemble NWP wind power forecasts to improve national power system management

    Science.gov (United States)

    Cannon, D.; Brayshaw, D.; Methven, J.; Coker, P.; Lenaghan, D.

    2014-12-01

    National power systems are becoming increasingly sensitive to atmospheric variability as generation from wind (and other renewables) increases. As such, the days-ahead predictability of wind power has significant implications for power system management. At this time horizon, power system operators plan transmission line outages for maintenance. In addition, forecast users begin to form backup strategies to account for the uncertainty in wind power predictions. Under-estimating this uncertainty could result in a failure to meet system security standards, or in the worst instance, a shortfall in total electricity supply. On the other hand, overly conservative assumptions about the forecast uncertainty incur costs associated with the unnecessary holding of reserve power. Using the power system of Great Britain (GB) as an example, we construct time series of GB-total wind power output using wind speeds from either reanalyses or global weather forecasts. To validate the accuracy of these data sets, wind power reconstructions using reanalyses and forecast analyses over a recent period are compared to measured GB-total power output. The results are found to be highly correlated on time scales greater than around 6 hours. Results are presented using ensemble wind power forecasts from several national and international forecast centres (obtained through TIGGE). Firstly, the skill with which global ensemble forecasts can represent the uncertainty in the GB-total power output at up to 10 days ahead is quantified. Following this, novel ensemble forecast metrics are developed to improve estimates of forecast uncertainty within the context of power system operations, thus enabling the development of more cost effective strategies. Finally, the predictability of extreme events such as prolonged low wind periods or rapid changes in wind power output are examined in detail. These events, if poorly forecast, induce high stress scenarios that could threaten the security of the power

  16. Comparison of the economic impact of different wind power forecast systems for producers

    Science.gov (United States)

    Alessandrini, S.; Davò, F.; Sperati, S.; Benini, M.; Delle Monache, L.

    2014-05-01

    Deterministic forecasts of wind production for the next 72 h at a single wind farm or at the regional level are among the main end-users requirement. However, for an optimal management of wind power production and distribution it is important to provide, together with a deterministic prediction, a probabilistic one. A deterministic forecast consists of a single value for each time in the future for the variable to be predicted, while probabilistic forecasting informs on probabilities for potential future events. This means providing information about uncertainty (i.e. a forecast of the PDF of power) in addition to the commonly provided single-valued power prediction. A significant probabilistic application is related to the trading of energy in day-ahead electricity markets. It has been shown that, when trading future wind energy production, using probabilistic wind power predictions can lead to higher benefits than those obtained by using deterministic forecasts alone. In fact, by using probabilistic forecasting it is possible to solve economic model equations trying to optimize the revenue for the producer depending, for example, on the specific penalties for forecast errors valid in that market. In this work we have applied a probabilistic wind power forecast systems based on the "analog ensemble" method for bidding wind energy during the day-ahead market in the case of a wind farm located in Italy. The actual hourly income for the plant is computed considering the actual selling energy prices and penalties proportional to the unbalancing, defined as the difference between the day-ahead offered energy and the actual production. The economic benefit of using a probabilistic approach for the day-ahead energy bidding are evaluated, resulting in an increase of 23% of the annual income for a wind farm owner in the case of knowing "a priori" the future energy prices. The uncertainty on price forecasting partly reduces the economic benefit gained by using a

  17. 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.

  18. Evaluation of Dynamical Downscaling Resolution Effect on Wind Energy Forecast Value for a Wind Farm in Central Sweden

    DEFF Research Database (Denmark)

    Rosgaard, Martin Haubjerg; Hahmann, Andrea N.; Nielsen, Torben Skov

    2014-01-01

    energy. The present study aims to quantify value added to wind energy forecasts in the 12-48 hour leadtime by downscaling global numerical weather prediction (NWP) data from the National Centers for Environmental Prediction Global Forecast System (GFS) using the limited-area NWP model described...

  19. Research on Short-Term Wind Power Prediction Based on Combined Forecasting Models

    Directory of Open Access Journals (Sweden)

    Zhang Chi

    2016-01-01

    Full Text Available Short-Term wind power forecasting is crucial for power grid since the generated energy of wind farm fluctuates frequently. In this paper, a physical forecasting model based on NWP and a statistical forecasting model with optimized initial value in the method of BP neural network are presented. In order to make full use of the advantages of the models presented and overcome the limitation of the disadvantage, the equal weight model and the minimum variance model are established for wind power prediction. Simulation results show that the combination forecasting model is more precise than single forecasting model and the minimum variance combination model can dynamically adjust weight of each single method, restraining the forecasting error further.

  20. 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...

  1. A new wind speed forecasting strategy based on the chaotic time series modelling technique and the Apriori algorithm

    International Nuclear Information System (INIS)

    Guo, Zhenhai; Chi, Dezhong; Wu, Jie; Zhang, Wenyu

    2014-01-01

    Highlights: • Impact of meteorological factors on wind speed forecasting is taken into account. • Forecasted wind speed results are corrected by the associated rules. • Forecasting accuracy is improved by the new wind speed forecasting strategy. • Robust of the proposed model is validated by data sampled from different sites. - Abstract: Wind energy has been the fastest growing renewable energy resource in recent years. Because of the intermittent nature of wind, wind power is a fluctuating source of electrical energy. Therefore, to minimize the impact of wind power on the electrical grid, accurate and reliable wind power forecasting is mandatory. In this paper, a new wind speed forecasting approach based on based on the chaotic time series modelling technique and the Apriori algorithm has been developed. The new approach consists of four procedures: (I) Clustering by using the k-means clustering approach; (II) Employing the Apriori algorithm to discover the association rules; (III) Forecasting the wind speed according to the chaotic time series forecasting model; and (IV) Correcting the forecasted wind speed data using the associated rules discovered previously. This procedure has been verified by 31-day-ahead daily average wind speed forecasting case studies, which employed the wind speed and other meteorological data collected from four meteorological stations located in the Hexi Corridor area of China. The results of these case studies reveal that the chaotic forecasting model can efficiently improve the accuracy of the wind speed forecasting, and the Apriori algorithm can effectively discover the association rules between the wind speed and other meteorological factors. In addition, the correction results demonstrate that the association rules discovered by the Apriori algorithm have powerful capacities in handling the forecasted wind speed values correction when the forecasted values do not match the classification discovered by the association rules

  2. A hybrid approach for short-term forecasting of wind speed.

    Science.gov (United States)

    Tatinati, Sivanagaraja; Veluvolu, Kalyana C

    2013-01-01

    We propose a hybrid method for forecasting the wind speed. The wind speed data is first decomposed into intrinsic mode functions (IMFs) with empirical mode decomposition. Based on the partial autocorrelation factor of the individual IMFs, adaptive methods are then employed for the prediction of IMFs. Least squares-support vector machines are employed for IMFs with weak correlation factor, and autoregressive model with Kalman filter is employed for IMFs with high correlation factor. Multistep prediction with the proposed hybrid method resulted in improved forecasting. Results with wind speed data show that the proposed method provides better forecasting compared to the existing methods.

  3. 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)

  4. On multivariate imputation and forecasting of decadal wind speed missing data.

    Science.gov (United States)

    Wesonga, Ronald

    2015-01-01

    This paper demonstrates the application of multiple imputations by chained equations and time series forecasting of wind speed data. The study was motivated by the high prevalence of missing wind speed historic data. Findings based on the fully conditional specification under multiple imputations by chained equations, provided reliable wind speed missing data imputations. Further, the forecasting model shows, the smoothing parameter, alpha (0.014) close to zero, confirming that recent past observations are more suitable for use to forecast wind speeds. The maximum decadal wind speed for Entebbe International Airport was estimated to be 17.6 metres per second at a 0.05 level of significance with a bound on the error of estimation of 10.8 metres per second. The large bound on the error of estimations confirms the dynamic tendencies of wind speed at the airport under study.

  5. 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...... are not as complicated as Woolnorth Bluff Point. Large ramps are considered critical events for a wind power forecast for energy trading as well as managing power system security. A methodology is developed to detect large ramp events in the wind farm power data. Forty-one large ramp events are detected over I year...... (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...

  6. Influence of local wind speed and direction on wind power dynamics – Application to offshore very short-term forecasting

    DEFF Research Database (Denmark)

    Gallego, Cristobal; Pinson, Pierre; Madsen, Henrik

    2011-01-01

    on one-step ahead forecasting and a time series resolution of 10 min. It has been found that the local wind direction contributes to model some features of the prevailing winds, such as the impact of the wind direction on the wind variability, whereas the non-linearities related to the power......Wind power time series usually show complex dynamics mainly due to non-linearities related to the wind physics and the power transformation process in wind farms. This article provides an approach to the incorporation of observed local variables (wind speed and direction) to model some...... of these effects by means of statistical models. To this end, a benchmarking between two different families of varyingcoefficient models (regime-switching and conditional parametric models) is carried out. The case of the offshore wind farm of Horns Rev in Denmark has been considered. The analysis is focused...

  7. Forecasting Cool Season Daily Peak Winds at Kennedy Space Center and Cape Canaveral Air Force Station

    Science.gov (United States)

    Barrett, Joe, III; Short, David; Roeder, William

    2008-01-01

    The expected peak wind speed for the day is an important element in the daily 24-Hour and Weekly Planning Forecasts issued by the 45th Weather Squadron (45 WS) for planning operations at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). The morning outlook for peak speeds also begins the warning decision process for gusts ^ 35 kt, ^ 50 kt, and ^ 60 kt from the surface to 300 ft. The 45 WS forecasters have indicated that peak wind speeds are a challenging parameter to forecast during the cool season (October-April). The 45 WS requested that the Applied Meteorology Unit (AMU) develop a tool to help them forecast the speed and timing of the daily peak and average wind, from the surface to 300 ft on KSC/CCAFS during the cool season. The tool must only use data available by 1200 UTC to support the issue time of the Planning Forecasts. Based on observations from the KSC/CCAFS wind tower network, surface observations from the Shuttle Landing Facility (SLF), and CCAFS upper-air soundings from the cool season months of October 2002 to February 2007, the AMU created multiple linear regression equations to predict the timing and speed of the daily peak wind speed, as well as the background average wind speed. Several possible predictors were evaluated, including persistence, the temperature inversion depth, strength, and wind speed at the top of the inversion, wind gust factor (ratio of peak wind speed to average wind speed), synoptic weather pattern, occurrence of precipitation at the SLF, and strongest wind in the lowest 3000 ft, 4000 ft, or 5000 ft. Six synoptic patterns were identified: 1) surface high near or over FL, 2) surface high north or east of FL, 3) surface high south or west of FL, 4) surface front approaching FL, 5) surface front across central FL, and 6) surface front across south FL. The following six predictors were selected: 1) inversion depth, 2) inversion strength, 3) wind gust factor, 4) synoptic weather pattern, 5) occurrence of

  8. Statistical Short-Range Guidance for Peak Wind Speed Forecasts at Edwards Air Force Base, CA

    Science.gov (United States)

    Dreher, Joseph G.; Crawford, Winifred; Lafosse, Richard; Hoeth, Brian; Burns, Kerry

    2009-01-01

    The peak winds near the surface are an important forecast element for space shuttle landings. As defined in the Flight Rules (FR), there are peak wind thresholds that cannot be exceeded in order to ensure the safety of the shuttle during landing operations. The National Weather Service Spaceflight Meteorology Group (SMG) is responsible for weather forecasts for all shuttle landings, and is required to issue surface average and 10-minute peak wind speed forecasts. They indicate peak winds are a challenging parameter to forecast. To alleviate the difficulty in making such wind forecasts, the Applied Meteorology Unit (AMU) developed a PC-based graphical user interface (GUI) for displaying peak wind climatology and probabilities of exceeding peak wind thresholds for the Shuttle Landing Facility (SLF) at Kennedy Space Center (KSC; Lambert 2003). However, the shuttle occasionally may land at Edwards Air Force Base (EAFB) in southern California when weather conditions at KSC in Florida are not acceptable, so SMG forecasters requested a similar tool be developed for EAFB.

  9. 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...

  10. 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)

  11. 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

  12. An Experimental Investigation of FNN Model for Wind Speed Forecasting Using EEMD and CS

    Directory of Open Access Journals (Sweden)

    Jianzhou Wang

    2015-01-01

    Full Text Available With depletion of traditional energy and increasing environmental problems, wind energy, as an alternative renewable energy, has drawn more and more attention internationally. Meanwhile, wind is plentiful, clean, and environmentally friendly; moreover, its speed is a very important piece of information needed in the operations and planning of the wind power system. Therefore, choosing an effective forecasting model with good performance plays a quite significant role in wind power system. A hybrid CS-EEMD-FNN model is firstly proposed in this paper for multistep ahead prediction of wind speed, in which EEMD is employed as a data-cleaning method that aims to remove the high frequency noise embedded in the wind speed series. CS optimization algorithm is used to select the best parameters in the FNN model. In order to evaluate the effectiveness and performance of the proposed hybrid model, three other short-term wind speed forecasting models, namely, FNN model, EEMD-FNN model, and CS-FNN model, are carried out to forecast wind speed using data measured at a typical site in Shandong wind farm, China, over three seasons in 2011. Experimental results demonstrate that the developed hybrid CS-EEMD-FNN model outperforms other models with more accuracy, which is suitable to wind speed forecasting in this area.

  13. Real-Time Forecasting System of Winds, Waves and Surge in Tropical Cyclones

    National Research Council Canada - National Science Library

    Graber, Hans C; Donelan, Mark A; Brown, Michael G; Slinn, Donald N; Hagen, Scott C; Thompson, Donald R; Jensen, Robert E; Black, Peter G; Powell, Mark D; Guiney, John L; Cardone, Vincent J; Cox, Andrew T; Augustus, Ellsworth H; Colonnese, Christopher P

    2003-01-01

    The long-term goal of this partnership is to establish an operational forecasting system of the wind field and resulting waves and surge impacting the coastline during the approach and landfall of tropical cyclones...

  14. Real-Time Forecasting System of Winds, Waves and Surge in Tropical Cyclones

    National Research Council Canada - National Science Library

    Graber, Hans C; Donelan, Mark A; Brown, Michael G; Slinn, Donald N; Hagen, Scott C; Thompson, Donald R; Jensen, Robert E; Black, Peter G; Powell, Mark D; Guiney, John L

    2004-01-01

    The long-term goal of this partnership is to establish an operational forecasting system of the wind field and resulting waves and surge impacting the coastline during the approach and landfall of tropical cyclones...

  15. Real-Time Forecasting System of Winds, Waves and Surge in Tropical Cyclones

    National Research Council Canada - National Science Library

    Graber, Hans C; Donelan, Mark A; Brown, Michael G; Slinn, Donald N; Hagen, Scott C; Thompson, Donald R; Jensen, Robert E; Black, Peter G; Powell, Mark D; Guiney, John L

    2005-01-01

    The long-term goal of this partnership is to establish an operational forecasting system of the wind field and resulting waves and surge impacting the coastline during the approach and landfall of tropical cyclones...

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

    DEFF Research Database (Denmark)

    Tastu, Julija

    a statistical model which would improve the quality of state-of-the-art prediction methods by accounting for the fact that forecasts errors made by such locally-optimized forecasting methods propagate in space and in time under the influence of prevailing weather conditions. Subsequently, the extension from...... 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...... forecasts, then extending to marginal predictive densities and, finally, considering multivariate space-time trajectories. Point predictions is the most classical approach to wind power forecasting, only providing single-valued estimates of the expected future power generation. The objective is to introduce...

  17. A hybrid wavelet transform based short-term wind speed forecasting approach.

    Science.gov (United States)

    Wang, Jujie

    2014-01-01

    It is important to improve the accuracy of wind speed forecasting for wind parks management and wind power utilization. In this paper, a novel hybrid approach known as WTT-TNN is proposed for wind speed forecasting. In the first step of the approach, a wavelet transform technique (WTT) is used to decompose wind speed into an approximate scale and several detailed scales. In the second step, a two-hidden-layer neural network (TNN) is used to predict both approximated scale and detailed scales, respectively. In order to find the optimal network architecture, the partial autocorrelation function is adopted to determine the number of neurons in the input layer, and an experimental simulation is made to determine the number of neurons within each hidden layer in the modeling process of TNN. Afterwards, the final prediction value can be obtained by the sum of these prediction results. In this study, a WTT is employed to extract these different patterns of the wind speed and make it easier for forecasting. To evaluate the performance of the proposed approach, it is applied to forecast Hexi Corridor of China's wind speed. Simulation results in four different cases show that the proposed method increases wind speed forecasting accuracy.

  18. Accuracy of National Weather Service wind-direction forecasts at Macon and Augusta, Georgia

    Science.gov (United States)

    Leonidas G. Lavdas

    1997-01-01

    National Weather Service wind forecasts and observations over a nine-year period (1985 to 1993) were analyzed to determine the usefulness of these forecasts for forestry smoke management. Data from Macon, GA indicated that forecasts were accurate to within plus or minus 22.5E about 38 percent of the time. When a wider plus or minus 67.5E window was used, accuracy...

  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. 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...

  1. 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

  2. Knowledge Mining Based on Environmental Simulation Applied to Wind Farm Power Forecasting

    Directory of Open Access Journals (Sweden)

    Dongxiao Niu

    2013-01-01

    Full Text Available Considering the inherent variability and uncertainty of wind power generation, in this study, a self-organizing map (SOM combined with rough set theory clustering technique (RST is proposed to extract the relative knowledge and to choose the most similar history situation and efficient data for wind power forecasting with numerical weather prediction (NWP. Through integrating the SOM and RST methods to cluster the historical data into several classes, the approach could find the similar days and excavate the hidden rules. According to the data reprocessing, the selected samples will improve the forecast accuracy echo state network (ESN trained by the class of the forecasting day that is adopted to forecast the wind power output accordingly. The developed methods are applied to a case of power forecasting in a wind farm located in northwest of China with wind power data from April 1, 2008, to May 6, 2009. In order to verify its effectiveness, the performance of the proposed method is compared with the traditional backpropagation neural network (BP. The results demonstrated that knowledge mining led to a promising improvement in the performance for wind farm power forecasting.

  3. Wind-Ramp-Forecast Sensitivity to Closure Parameters in a Boundary-Layer Parametrization Scheme

    Science.gov (United States)

    Jahn, David E.; Takle, Eugene S.; Gallus, William A.

    2017-09-01

    Wind ramps are relatively large changes in wind speed over a period of a few hours and present a challenge for electric utilities to balance power generation and load. Failures of boundary-layer parametrization schemes to represent physical processes limit the ability of numerical models to forecast wind ramps, especially in a stable boundary layer. Herein, the eight "closure parameters" of a widely used boundary-layer parameterization scheme are subject to sensitivity tests for a set of wind-ramp cases. A marked sensitivity of forecast wind speed to closure-parameter values is observed primarily for three parameters that influence in the closure equations the depth of turbulent mixing, dissipation, and the transfer of kinetic energy from the mean to the turbulent flow. Reducing the value of these parameters independently by 25% or by 50% reduces the overall average in forecast wind-speed errors by at least 24% for the first two parameters and increases average forecast error by at least 63% for the third parameter. Doubling any of these three parameters increases average forecast error by at least 67%. Such forecast sensitivity to closure parameter values provides motivation to explore alternative values in the context of a stable boundary layer.

  4. 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

    for the spatio‐temporal dependencies observed in the wind generation field. However, it is intuitively expected that, owing to the inertia of meteorological forecasting systems, a forecast error made at a given point in space and time will be related to forecast errors at other points in space in the following...... 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...... period. The existence of such underlying correlation patterns is demonstrated and analyzed in this paper, considering the case‐study of western Denmark. The effects of prevailing wind speed and direction on autocorrelation and cross‐correlation patterns are thoroughly described. For a flat terrain region...

  5. A Novel Empirical Mode Decomposition With Support Vector Regression for Wind Speed Forecasting.

    Science.gov (United States)

    Ren, Ye; Suganthan, Ponnuthurai Nagaratnam; Srikanth, Narasimalu

    2016-08-01

    Wind energy is a clean and an abundant renewable energy source. Accurate wind speed forecasting is essential for power dispatch planning, unit commitment decision, maintenance scheduling, and regulation. However, wind is intermittent and wind speed is difficult to predict. This brief proposes a novel wind speed forecasting method by integrating empirical mode decomposition (EMD) and support vector regression (SVR) methods. The EMD is used to decompose the wind speed time series into several intrinsic mode functions (IMFs) and a residue. Subsequently, a vector combining one historical data from each IMF and the residue is generated to train the SVR. The proposed EMD-SVR model is evaluated with a wind speed data set. The proposed EMD-SVR model outperforms several recently reported methods with respect to accuracy or computational complexity.

  6. Early warnings of extreme winds using the ECMWF Extreme Forecast Index

    DEFF Research Database (Denmark)

    Petroliagis, Thomas I.; Pinson, Pierre

    2014-01-01

    regimes. Overall, it becomes clear that the first indications of an extreme wind event might come from the ECMWF deterministic and/or probabilistic components capturing very intense weather systems (possible windstorms) in the medium term. For early warnings, all available EPS Extreme Forecast Index (EFI......The European FP7 SafeWind Project aims at developing research towards a European vision of wind power forecasting, which requires advanced meteorological support concerning extreme wind events. This study is focused mainly on early warnings of extreme winds in the early medium-range. Three synoptic...... stations (airports) of North Germany (Bremen, Hamburg and Hannover) were considered for the construction of time series of daily maximum wind speeds. All daily wind extremes were found to be linked to very intense surface cyclonic circulation systems being advected mainly by southwest and northwest flow...

  7. 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

  8. Statistical Post-Processing of Wind Speed Forecasts to Estimate Relative Economic Value

    Science.gov (United States)

    Courtney, Jennifer; Lynch, Peter; Sweeney, Conor

    2013-04-01

    The objective of this research is to get the best possible wind speed forecasts for the wind energy industry by using an optimal combination of well-established forecasting and post-processing methods. We start with the ECMWF 51 member ensemble prediction system (EPS) which is underdispersive and hence uncalibrated. We aim to produce wind speed forecasts that are more accurate and calibrated than the EPS. The 51 members of the EPS are clustered to 8 weighted representative members (RMs), chosen to minimize the within-cluster spread, while maximizing the inter-cluster spread. The forecasts are then downscaled using two limited area models, WRF and COSMO, at two resolutions, 14km and 3km. This process creates four distinguishable ensembles which are used as input to statistical post-processes requiring multi-model forecasts. Two such processes are presented here. The first, Bayesian Model Averaging, has been proven to provide more calibrated and accurate wind speed forecasts than the ECMWF EPS using this multi-model input data. The second, heteroscedastic censored regression is indicating positive results also. We compare the two post-processing methods, applied to a year of hindcast wind speed data around Ireland, using an array of deterministic and probabilistic verification techniques, such as MAE, CRPS, probability transform integrals and verification rank histograms, to show which method provides the most accurate and calibrated forecasts. However, the value of a forecast to an end-user cannot be fully quantified by just the accuracy and calibration measurements mentioned, as the relationship between skill and value is complex. Capturing the full potential of the forecast benefits also requires detailed knowledge of the end-users' weather sensitive decision-making processes and most importantly the economic impact it will have on their income. Finally, we present the continuous relative economic value of both post-processing methods to identify which is more

  9. Fine tuning support vector machines for short-term wind speed forecasting

    International Nuclear Information System (INIS)

    Zhou Junyi; Shi Jing; Li Gong

    2011-01-01

    Research highlights: → A systematic approach to tuning SVM models for wind speed prediction is proposed. → Multiple kernel functions and a wide range of tuning parameters are evaluated, and optimal parameters for each kernel function are obtained. → It is found that the forecasting performance of SVM is closely related to the dynamic characteristics of wind speed. → Under the optimal combination of parameters, different kernels give comparable forecasting accuracy. -- Abstract: Accurate forecasting of wind speed is critical to the effective harvesting of wind energy and the integration of wind power into the existing electric power grid. Least-squares support vector machines (LS-SVM), a powerful technique that is widely applied in a variety of classification and function estimation problems, carries great potential for the application of short-term wind speed forecasting. In this case, tuning the model parameters for optimal forecasting accuracy is a fundamental issue. This paper, for the first time, presents a systematic study on fine tuning of LS-SVM model parameters for one-step ahead wind speed forecasting. Three SVM kernels, namely linear, Gaussian, and polynomial kernels, are implemented. The SVM parameters considered include the training sample size, SVM order, regularization parameter, and kernel parameters. The results show that (1) the performance of LS-SVM is closely related to the dynamic characteristics of wind speed; (2) all parameters investigated greatly affect the performance of LS-SVM models; (3) under the optimal combination of parameters after fine tuning, the three kernels give comparable forecasting accuracy; (4) the performance of linear kernel is worse than the other two kernels when the training sample size or SVM order is small. In addition, LS-SVMs are compared against the persistence approach, and it is found that they can outperform the persistence model in the majority of cases.

  10. Two-Step Forecast of Geomagnetic Storm Using Coronal Mass Ejection and Solar Wind Condition

    Science.gov (United States)

    Kim, R.-S.; Moon, Y.-J.; Gopalswamy, N.; Park, Y.-D.; Kim, Y.-H.

    2014-01-01

    To forecast geomagnetic storms, we had examined initially observed parameters of coronal mass ejections (CMEs) and introduced an empirical storm forecast model in a previous study. Now we suggest a two-step forecast considering not only CME parameters observed in the solar vicinity but also solar wind conditions near Earth to improve the forecast capability. We consider the empirical solar wind criteria derived in this study (Bz = -5 nT or Ey = 3 mV/m for t = 2 h for moderate storms with minimum Dst less than -50 nT) (i.e. Magnetic Field Magnitude, B (sub z) less than or equal to -5 nanoTeslas or duskward Electrical Field, E (sub y) greater than or equal to 3 millivolts per meter for time greater than or equal to 2 hours for moderate storms with Minimum Disturbance Storm Time, Dst less than -50 nanoTeslas) and a Dst model developed by Temerin and Li (2002, 2006) (TL [i.e. Temerin Li] model). Using 55 CME-Dst pairs during 1997 to 2003, our solar wind criteria produce slightly better forecasts for 31 storm events (90 percent) than the forecasts based on the TL model (87 percent). However, the latter produces better forecasts for 24 nonstorm events (88 percent), while the former correctly forecasts only 71 percent of them. We then performed the two-step forecast. The results are as follows: (i) for 15 events that are incorrectly forecasted using CME parameters, 12 cases (80 percent) can be properly predicted based on solar wind conditions; (ii) if we forecast a storm when both CME and solar wind conditions are satisfied (n, i.e. cap operator - the intersection set that is comprised of all the elements that are common to both), the critical success index becomes higher than that from the forecast using CME parameters alone, however, only 25 storm events (81 percent) are correctly forecasted; and (iii) if we forecast a storm when either set of these conditions is satisfied (?, i.e. cup operator - the union set that is comprised of all the elements of either or both

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

    Science.gov (United States)

    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. PMID:27382627

  13. 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 advantage of being able to produce spatially out-of-sample forecasts. We use a Bayesian hierarchical framework to obtain fast and accurate forecasts of wind power generation not only at wind farms where recent data are available but also at a larger portfolio including wind farms without recent observations...

  14. 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.

  15. Wind power forecasting system EOlienne SPEO : development, preliminary results and integration at Hydro-Quebec

    Energy Technology Data Exchange (ETDEWEB)

    Forcione, A.; Roberge, G. [Hydro-Quebec, Varennes, PQ (Canada). IREQ; Yu, W.; Glazer, A.; Benoit, R.; Plante, A.; Tran, L.D.; Chardon, L. [Environment Canada, Ottawa, ON (Canada)

    2007-07-01

    Wind generation forecasting at Hydro-Quebec was discussed with particular reference to the collaborative efforts between the utility's Research Institute and Environment Canada in developing the Systeme de Prevision EOlienne (SPEO). The European ANEMOS platform was installed at Hydro-Quebec Distribution in 2006. Operational forecasts using the Global Environmental Multi-scale model (GEM) from the Canadian Meteorological Centre served as input for SPEO. This presentation evaluated the performance of the forecasting model, and presented best approaches for long term use and continuous improvement. SPEO was developed to forecast wind and other atmospheric variables, and not generated power. The development of the software began in September 2006 with the development and integration of necessary components, followed by the calibration of the system, 15 months of operational forecasts, experimentation and final analysis in 2008. The GEM-global model provides 10 days and 240 hours of hourly forecasts with 35 km resolution, while the GEM-regional model provides 2 days and 48 hours of hourly forecasts with 15 km resolution. It was shown that the development of a good forecasting system depends entirely on the availability of a maximum number of observation sources, which for SPEO includes 13 Environment Canada stations and wind farm masts. The final value of a wind forecasting system also depends on compatibility with the electric system management tools and processes. Research is ongoing to improve SPEO through validation tools, integration of newly available observations, recalibration and experimentation. Future tasks will be to extend the 48 hour horizon, to optimize the number crunching efficiency and to characterize wind farms more precisely. figs.

  16. Comparative Validation of Realtime Solar Wind Forecasting Using the UCSD Heliospheric Tomography Model

    Science.gov (United States)

    MacNeice, Peter; Taktakishvili, Alexandra; Jackson, Bernard; Clover, John; Bisi, Mario; Odstrcil, Dusan

    2011-01-01

    The University of California, San Diego 3D Heliospheric Tomography Model reconstructs the evolution of heliospheric structures, and can make forecasts of solar wind density and velocity up to 72 hours in the future. The latest model version, installed and running in realtime at the Community Coordinated Modeling Center(CCMC), analyzes scintillations of meter wavelength radio point sources recorded by the Solar-Terrestrial Environment Laboratory(STELab) together with realtime measurements of solar wind speed and density recorded by the Advanced Composition Explorer(ACE) Solar Wind Electron Proton Alpha Monitor(SWEPAM).The solution is reconstructed using tomographic techniques and a simple kinematic wind model. Since installation, the CCMC has been recording the model forecasts and comparing them with ACE measurements, and with forecasts made using other heliospheric models hosted by the CCMC. We report the preliminary results of this validation work and comparison with alternative models.

  17. 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

    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...... 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......, 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....

  18. Wind Energy Forecasting: A Collaboration of the National Center for Atmospheric Research (NCAR) and Xcel Energy

    Energy Technology Data Exchange (ETDEWEB)

    Parks, K.; Wan, Y. H.; Wiener, G.; Liu, Y.

    2011-10-01

    The focus of this report is the wind forecasting system developed during this contract period with results of performance through the end of 2010. The report is intentionally high-level, with technical details disseminated at various conferences and academic papers. At the end of 2010, Xcel Energy managed the output of 3372 megawatts of installed wind energy. The wind plants span three operating companies1, serving customers in eight states2, and three market structures3. The great majority of the wind energy is contracted through power purchase agreements (PPAs). The remainder is utility owned, Qualifying Facilities (QF), distributed resources (i.e., 'behind the meter'), or merchant entities within Xcel Energy's Balancing Authority footprints. Regardless of the contractual or ownership arrangements, the output of the wind energy is balanced by Xcel Energy's generation resources that include fossil, nuclear, and hydro based facilities that are owned or contracted via PPAs. These facilities are committed and dispatched or bid into day-ahead and real-time markets by Xcel Energy's Commercial Operations department. Wind energy complicates the short and long-term planning goals of least-cost, reliable operations. Due to the uncertainty of wind energy production, inherent suboptimal commitment and dispatch associated with imperfect wind forecasts drives up costs. For example, a gas combined cycle unit may be turned on, or committed, in anticipation of low winds. The reality is winds stayed high, forcing this unit and others to run, or be dispatched, to sub-optimal loading positions. In addition, commitment decisions are frequently irreversible due to minimum up and down time constraints. That is, a dispatcher lives with inefficient decisions made in prior periods. In general, uncertainty contributes to conservative operations - committing more units and keeping them on longer than may have been necessary for purposes of maintaining reliability

  19. A new hybrid model optimized by an intelligent optimization algorithm for wind speed forecasting

    International Nuclear Information System (INIS)

    Su, Zhongyue; Wang, Jianzhou; Lu, Haiyan; Zhao, Ge

    2014-01-01

    Highlights: • A new hybrid model is developed for wind speed forecasting. • The model is based on the Kalman filter and the ARIMA. • An intelligent optimization method is employed in the hybrid model. • The new hybrid model has good performance in western China. - Abstract: Forecasting the wind speed is indispensable in wind-related engineering studies and is important in the management of wind farms. As a technique essential for the future of clean energy systems, reducing the forecasting errors related to wind speed has always been an important research subject. In this paper, an optimized hybrid method based on the Autoregressive Integrated Moving Average (ARIMA) and Kalman filter is proposed to forecast the daily mean wind speed in western China. This approach employs Particle Swarm Optimization (PSO) as an intelligent optimization algorithm to optimize the parameters of the ARIMA model, which develops a hybrid model that is best adapted to the data set, increasing the fitting accuracy and avoiding over-fitting. The proposed method is subsequently examined on the wind farms of western China, where the proposed hybrid model is shown to perform effectively and steadily

  20. Seasonal forecast verification of extreme events for the wind energy sector

    Science.gov (United States)

    Lee, Doo Young; González-Reviriego, Nube; Torralba, Veronica; Cortesi, Nicola; Marcos, Raül; Doblas-Reyes, Francisco

    2017-04-01

    Severe and extreme winds and related destructive wind storms are the second highest cause of global natural catastrophe insurance losses after hurricanes. For this reason, a more accurate assessment of the probability of occurrence of these severe wind speed events is necessary to increase the protection and to minimize the risk of unexpected energy network unbalance. In this study, we focus on the evaluation of the ability of the global seasonal climate prediction systems in forecasting extreme wind speeds. The climate forecast systems employed are the ECMWF seasonal forecast system 4 (ECMWF-S4) and Meteo-France's Systems 4 (METFR-S4). We consider extreme events based on the upper (90th percentile) or lower (10th percentile) thresholds of 6-hourly 10m wind speed within a month. Then 3-month averages of those events have been analyzed at 0-4 months lead time for the May and November start dates during the period 1991-2012. We evaluate the performance of the seasonal climate prediction systems when predicting extreme wind speed at different forecast horizons, by means of deterministic and probabilistic skill measures, such as the temporal correlation coefficient (TCC) and the fair ranked probability skill Score (FRPSS). At the seasonal time scale, this investigation is a first step for providing better climate information to characterize the low and high wind speeds in a particular location.

  1. 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.

  2. The economic benefit of short-term forecasting for wind energy in the UK electricity market

    International Nuclear Information System (INIS)

    Barthelmie, R.J.; Murray, F.; Pryor, S.C.

    2008-01-01

    In the UK market, the total price of renewable electricity is made up of the Renewables Obligation Certificate and the price achieved for the electricity. Accurate forecasting improves the price if electricity is traded via the power exchange. In order to understand the size of wind farm for which short-term forecasting becomes economically viable, we develop a model for wind energy. Simulations were carried out for 2003 electricity prices for different forecast accuracies and strategies. The results indicate that it is possible to increase the price obtained by around pound 5/MWh which is about 14% of the electricity price in 2003 and about 6% of the total price. We show that the economic benefit of using short-term forecasting is also dependant on the accuracy and cost of purchasing the forecast. As the amount of wind energy requiring integration into the grid increases, short-term forecasting becomes more important to both wind farm owners and the transmission/distribution operators. (author)

  3. A combined model based on CEEMDAN and modified flower pollination algorithm for wind speed forecasting

    International Nuclear Information System (INIS)

    Zhang, Wenyu; Qu, Zongxi; Zhang, Kequan; Mao, Wenqian; Ma, Yining; Fan, Xu

    2017-01-01

    Highlights: • A CEEMDAN-CLSFPA combined model is proposed for short-term wind speed forecasting. • The CEEMDAN technique is used to decompose the original wind speed series. • A modified optimization algorithm-CLSFPA is proposed to optimize the weights of the combined model. • The no negative constraint theory is applied to the combined model. • Robustness of the proposed model is validated by data sampled from four different wind farms. - Abstract: Wind energy, which is stochastic and intermittent by nature, has a significant influence on power system operation, power grid security and market economics. Precise and reliable wind speed prediction is vital for wind farm planning and operational planning for power grids. To improve wind speed forecasting accuracy, a large number of forecasting approaches have been proposed; however, these models typically do not account for the importance of data preprocessing and are limited by the use of individual models. In this paper, a novel combined model – combining complete ensemble empirical mode decomposition adaptive noise (CEEMDAN), flower pollination algorithm with chaotic local search (CLSFPA), five neural networks and no negative constraint theory (NNCT) – is proposed for short-term wind speed forecasting. First, a recent CEEMDAN is employed to divide the original wind speed data into a finite set of IMF components, and then a combined model, based on NNCT, is proposed for forecasting each decomposition signal. To improve the forecasting capacity of the combined model, a modified flower pollination algorithm (FPA) with chaotic local search (CLS) is proposed and employed to determine the optimal weight coefficients of the combined model, and the final prediction values were obtained by reconstructing the refined series. To evaluate the forecasting ability of the proposed combined model, 15-min wind speed data from four wind farms in the eastern coastal areas of China are used. The experimental results of

  4. 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.

  5. Wind speed forecasting using FEEMD echo state networks with RELM in Hebei, China

    International Nuclear Information System (INIS)

    Sun, Wei; Liu, Mohan

    2016-01-01

    Highlights: • FEEMD–RELM is proposed for wind speed forecasting. • Short-term and mid-term wind speed are forecasted by the proposed model. • PACF is introduced to select the input of RELM. • Three cases in Hebei province are applied in this paper. - Abstract: Reducing the dependence on fossil-fuel-based resources is becoming significant due to the detrimental effects on environment and global energy-dependent. Thus, increased attention has been paid to wind power, a type of clean and renewable energy. However, owing to the stochastic nature of wind speed, it is essential to build a wind speed forecasting model with high-precision for wind power utilization. Therefore, this paper proposes a hybrid model which combines fast ensemble empirical model decomposition (FEEMD) with regularized extreme learning machine (RELM). The original wind speed series are first decomposed into a limited number of intrinsic mode functions (IMFs) and one residual series. Then RELM is built to forecast the sub-series. Partial auto correlation function (PACF) is applied to analyze the intrinsic relationships between the historical speeds so as to select the inputs of RELM. To verify the developed models, short-term wind speed data in July 2010 and monthly data from January 2000 to May 2010 in Hong songwa wind farm, Chengde city are used for model construction and testing. Two additional forecasting cases in Hebei province are also applied to prove the model’s validity. The simulation test results show that the built model is effective, efficient and practicable.

  6. 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.

  7. Nonparametric Stochastic Model for Uncertainty Quantifi cation of Short-term Wind Speed Forecasts

    Science.gov (United States)

    AL-Shehhi, A. M.; Chaouch, M.; Ouarda, T.

    2014-12-01

    Wind energy is increasing in importance as a renewable energy source due to its potential role in reducing carbon emissions. It is a safe, clean, and inexhaustible source of energy. The amount of wind energy generated by wind turbines is closely related to the wind speed. Wind speed forecasting plays a vital role in the wind energy sector in terms of wind turbine optimal operation, wind energy dispatch and scheduling, efficient energy harvesting etc. It is also considered during planning, design, and assessment of any proposed wind project. Therefore, accurate prediction of wind speed carries a particular importance and plays significant roles in the wind industry. Many methods have been proposed in the literature for short-term wind speed forecasting. These methods are usually based on modeling historical fixed time intervals of the wind speed data and using it for future prediction. The methods mainly include statistical models such as ARMA, ARIMA model, physical models for instance numerical weather prediction and artificial Intelligence techniques for example support vector machine and neural networks. In this paper, we are interested in estimating hourly wind speed measures in United Arab Emirates (UAE). More precisely, we predict hourly wind speed using a nonparametric kernel estimation of the regression and volatility functions pertaining to nonlinear autoregressive model with ARCH model, which includes unknown nonlinear regression function and volatility function already discussed in the literature. The unknown nonlinear regression function describe the dependence between the value of the wind speed at time t and its historical data at time t -1, t - 2, … , t - d. This function plays a key role to predict hourly wind speed process. The volatility function, i.e., the conditional variance given the past, measures the risk associated to this prediction. Since the regression and the volatility functions are supposed to be unknown, they are estimated using

  8. 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

  9. 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

  10. 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.

  11. Evaluation of different operational strategies for lithium ion battery systems connected to a wind turbine for primary frequency regulation and wind power forecast accuracy improvement

    DEFF Research Database (Denmark)

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

    2012-01-01

    High penetration levels of variable wind energy sources can cause problems with their grid integration. Energy storage systems connected to wind turbine/wind power plants can improve predictability of the wind power production and provide ancillary services to the grid. This paper investigates...... economics of different operational strategies for Li-ion systems connected to wind turbines for wind power forecast accuracy improvement and primary frequency regulation....

  12. High-quality Wind Power Scenario Forecasts for Decision-making Under Uncertainty in Power Systems

    DEFF Research Database (Denmark)

    Delikaraoglou, Stefanos; Pinson, Pierre

    2014-01-01

    The large scale integration of wind generation in existing power systems requires novel operational strategies and market clearing mechanisms to account for the variable nature of this energy source. An efficient method to cope with this uncertainty is stochastic optimization which however requires......-valued and probabilistic predictions as well as scenarios representing the spatio-temporal dependence structure of forecast errors. The applicability of the proposed framework is demonstrated with a small-scale stochastic unit commitment model....... high-quality forecasts in the form of scenarios. The main goal of this work is to release a public dataset of wind power forecasts to be used as a reference for future research. To that extent, we provide a complete framework to describe wind power uncertainty in terms of single...

  13. Evaluating Atlantic tropical cyclone track error distributions for use in probabilistic forecasts of wind distribution

    OpenAIRE

    Neese, Jay M.

    2010-01-01

    Approved for public release; distribution is unlimited This thesis investigates whether the National Hurricane Center (NHC) operational product for producing probabilistic forecasts of tropical cyclone (TC) wind distributions could be further improved by examining the distributions of track errors it draws upon to calculate probabilities. The track spread/skill relationship for several global ensemble prediction system forecasts is examined as a condition for a description of a full p...

  14. A New Hybrid Forecasting Strategy Applied to Mean Hourly Wind Speed Time Series

    Directory of Open Access Journals (Sweden)

    Stylianos Sp. Pappas

    2014-01-01

    Full Text Available An alternative electric power source, such as wind power, has to be both reliable and autonomous. An accurate wind speed forecasting method plays the key role in achieving the aforementioned properties and also is a valuable tool in overcoming a variety of economic and technical problems connected to wind power production. The method proposed is based on the reformulation of the problem in the standard state space form and on implementing a bank of Kalman filters (KF, each fitting an ARMA model of different order. The proposed method is to be applied to a greenhouse unit which incorporates an automatized use of renewable energy sources including wind speed power.

  15. Turbulence-driven coronal heating and improvements to empirical forecasting of the solar wind

    International Nuclear Information System (INIS)

    Woolsey, Lauren N.; Cranmer, Steven R.

    2014-01-01

    Forecasting models of the solar wind often rely on simple parameterizations of the magnetic field that ignore the effects of the full magnetic field geometry. In this paper, we present the results of two solar wind prediction models that consider the full magnetic field profile and include the effects of Alfvén waves on coronal heating and wind acceleration. The one-dimensional magnetohydrodynamic code ZEPHYR self-consistently finds solar wind solutions without the need for empirical heating functions. Another one-dimensional code, introduced in this paper (The Efficient Modified-Parker-Equation-Solving Tool, TEMPEST), can act as a smaller, stand-alone code for use in forecasting pipelines. TEMPEST is written in Python and will become a publicly available library of functions that is easy to adapt and expand. We discuss important relations between the magnetic field profile and properties of the solar wind that can be used to independently validate prediction models. ZEPHYR provides the foundation and calibration for TEMPEST, and ultimately we will use these models to predict observations and explain space weather created by the bulk solar wind. We are able to reproduce with both models the general anticorrelation seen in comparisons of observed wind speed at 1 AU and the flux tube expansion factor. There is significantly less spread than comparing the results of the two models than between ZEPHYR and a traditional flux tube expansion relation. We suggest that the new code, TEMPEST, will become a valuable tool in the forecasting of space weather.

  16. 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.

  17. Temporal and spatial variability of wind resources in the United States as derived from the Climate Forecast System Reanalysis

    Science.gov (United States)

    Lejiang Yu; Shiyuan Zhong; Xindi Bian; Warren E. Heilman

    2015-01-01

    This study examines the spatial and temporal variability of wind speed at 80m above ground (the average hub height of most modern wind turbines) in the contiguous United States using Climate Forecast System Reanalysis (CFSR) data from 1979 to 2011. The mean 80-m wind exhibits strong seasonality and large spatial variability, with higher (lower) wind speeds in the...

  18. Peak Wind Forecasts for the Launch-Critical Wind Towers on Kennedy Space Center/Cape Canaveral Air Force Station, Phase IV

    Science.gov (United States)

    Crawford, Winifred

    2011-01-01

    This final report describes the development of a peak wind forecast tool to assist forecasters in determining the probability of violating launch commit criteria (LCC) at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). The peak winds arc an important forecast clement for both the Space Shuttle and Expendable Launch Vehicle (ELV) programs. The LCC define specific peak wind thresholds for each launch operation that cannot be exceeded in order to ensure the safety of the vehicle. The 45th Weather Squadron (45 WS) has found that peak winds are a challenging parameter to forecast, particularly in the cool season months of October through April. Based on the importance of forecasting peak winds, the 45 WS tasked the Applied Meteorology Unit (AMU) to update the statistics in the current peak-wind forecast tool to assist in forecasting LCC violations. The tool includes onshore and offshore flow climatologies of the 5-minute mean and peak winds and probability distributions of the peak winds as a function of the 5-minute mean wind speeds.

  19. 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....

  20. Forecasting Wind and Solar Generation: Improving System Operations, Greening the Grid (Spanish Version)

    Energy Technology Data Exchange (ETDEWEB)

    Tian, Tian; Chernyakhovskiy, Ilya; Brancucci Martinez-Anido, Carlo

    2016-04-01

    This document is the Spanish version of 'Greening the Grid- Forecasting Wind and Solar Generation Improving System Operations'. It discusses improving system operations with forecasting with and solar generation. By integrating variable renewable energy (VRE) forecasts into system operations, power system operators can anticipate up- and down-ramps in VRE generation in order to cost-effectively balance load and generation in intra-day and day-ahead scheduling. This leads to reduced fuel costs, improved system reliability, and maximum use of renewable resources.

  1. Cost-Loss Analysis of Ensemble Solar Wind Forecasting: Space Weather Use of Terrestrial Weather Tools

    Science.gov (United States)

    Henley, E. M.; Pope, E. C. D.

    2017-12-01

    This commentary concerns recent work on solar wind forecasting by Owens and Riley (2017). The approach taken makes effective use of tools commonly used in terrestrial weather—notably, via use of a simple model—generation of an "ensemble" forecast, and application of a "cost-loss" analysis to the resulting probabilistic information, to explore the benefit of this forecast to users with different risk appetites. This commentary aims to highlight these useful techniques to the wider space weather audience and to briefly discuss the general context of application of terrestrial weather approaches to space weather.

  2. 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.

  3. Comparison of Statistical Post-Processing Methods for Probabilistic Wind Speed Forecasting

    Science.gov (United States)

    Han, Keunhee; Choi, JunTae; Kim, Chansoo

    2018-02-01

    In this study, the statistical post-processing methods that include bias-corrected and probabilistic forecasts of wind speed measured in PyeongChang, which is scheduled to host the 2018 Winter Olympics, are compared and analyzed to provide more accurate weather information. The six post-processing methods used in this study are as follows: mean bias-corrected forecast, mean and variance bias-corrected forecast, decaying averaging forecast, mean absolute bias-corrected forecast, and the alternative implementations of ensemble model output statistics (EMOS) and Bayesian model averaging (BMA) models, which are EMOS and BMA exchangeable models by assuming exchangeable ensemble members and simplified version of EMOS and BMA models. Observations for wind speed were obtained from the 26 stations in PyeongChang and 51 ensemble member forecasts derived from the European Centre for Medium-Range Weather Forecasts (ECMWF Directorate, 2012) that were obtained between 1 May 2013 and 18 March 2016. Prior to applying the post-processing methods, reliability analysis was conducted by using rank histograms to identify the statistical consistency of ensemble forecast and corresponding observations. Based on the results of our study, we found that the prediction skills of probabilistic forecasts of EMOS and BMA models were superior to the biascorrected forecasts in terms of deterministic prediction, whereas in probabilistic prediction, BMA models showed better prediction skill than EMOS. Even though the simplified version of BMA model exhibited best prediction skill among the mentioned six methods, the results showed that the differences of prediction skills between the versions of EMOS and BMA were negligible.

  4. Comparison of statistical post-processing methods for probabilistic wind speed forecasting

    Science.gov (United States)

    Han, Keunhee; Choi, JunTae; Kim, Chansoo

    2017-12-01

    In this study, the statistical post-processing methods that include bias-corrected and probabilistic forecasts of wind speed measured in PyeongChang, which is scheduled to host the 2018 Winter Olympics, are compared and analyzed to provide more accurate weather information. The six post-processing methods used in this study are as follows: mean bias-corrected forecast, mean and variance bias-corrected forecast, decaying averaging forecast, mean absolute bias-corrected forecast, and the alternative implementations of ensemble model output statistics (EMOS) and Bayesian model averaging (BMA) models, which are EMOS and BMA exchangeable models by assuming exchangeable ensemble members and simplified version of EMOS and BMA models. Observations for wind speed were obtained from the 26 stations in PyeongChang and 51 ensemble member forecasts derived from the European Centre for Medium-Range Weather Forecasts (ECMWF Directorate, 2012) that were obtained between 1 May 2013 and 18 March 2016. Prior to applying the post-processing methods, reliability analysis was conducted by using rank histograms to identify the statistical consistency of ensemble forecast and corresponding observations. Based on the results of our study, we found that the prediction skills of probabilistic forecasts of EMOS and BMA models were superior to the biascorrected forecasts in terms of deterministic prediction, whereas in probabilistic prediction, BMA models showed better prediction skill than EMOS. Even though the simplified version of BMA model exhibited best prediction skill among the mentioned six methods, the results showed that the differences of prediction skills between the versions of EMOS and BMA were negligible.

  5. Challenges, problems and possible solutions in wind generator systems from the aspect of forecast, planning and delivery of wind energy

    International Nuclear Information System (INIS)

    Giovski, Nikola

    2014-01-01

    The fundamental difficulties of integrating wind energy into the power system arise from its large temporal variability and limited predictability. That's why the integration of wind power presents major challenge for today's operating and planning practices of the power system operators. Accurate predictions of the possible wind power output, in time intervals relevant for creating schedules for production and exchange capacity, allows to system operators and dispatching personnel more efficient power system management. Despite the challenges and problems that arise due to integration of wind power into power systems, which need to be solved or reduced, wind power has its advantages that should be utilized. The effective integration of wind power plants into the transmission grid should allow them to represent the backbone of future energy systems. Modern wind generators represent production units that have the ability to participate in the management of energy systems e.g. in the regulation of frequency, voltage and other network operating requirements. This paper provides a brief overview of global experiences with the challenges, problems and possible solutions that appear in wind generator systems from the aspect of forecasting, planning and delivery of wind energy. (author)

  6. Linear and non-linear autoregressive models for short-term wind speed forecasting

    International Nuclear Information System (INIS)

    Lydia, M.; Suresh Kumar, S.; Immanuel Selvakumar, A.; Edwin Prem Kumar, G.

    2016-01-01

    Highlights: • Models for wind speed prediction at 10-min intervals up to 1 h built on time-series wind speed data. • Four different multivariate models for wind speed built based on exogenous variables. • Non-linear models built using three data mining algorithms outperform the linear models. • Autoregressive models based on wind direction perform better than other models. - Abstract: Wind speed forecasting aids in estimating the energy produced from wind farms. The soaring energy demands of the world and minimal availability of conventional energy sources have significantly increased the role of non-conventional sources of energy like solar, wind, etc. Development of models for wind speed forecasting with higher reliability and greater accuracy is the need of the hour. In this paper, models for predicting wind speed at 10-min intervals up to 1 h have been built based on linear and non-linear autoregressive moving average models with and without external variables. The autoregressive moving average models based on wind direction and annual trends have been built using data obtained from Sotavento Galicia Plc. and autoregressive moving average models based on wind direction, wind shear and temperature have been built on data obtained from Centre for Wind Energy Technology, Chennai, India. While the parameters of the linear models are obtained using the Gauss–Newton algorithm, the non-linear autoregressive models are developed using three different data mining algorithms. The accuracy of the models has been measured using three performance metrics namely, the Mean Absolute Error, Root Mean Squared Error and Mean Absolute Percentage Error.

  7. Forecasting tropical cyclone recurvature with upper tropospheric winds

    Science.gov (United States)

    Gentry, R. C.

    1983-01-01

    Data from 17 tropical cyclones during the 1974 through 1979 hurricane seasons are used to investigate whether the high level winds far to the northwest, north and northeast of the hurricane center can be used to predict hurricane track recurvature. When the man 200-mb winds 1500 to 2000 km northwest and north of the storm center equal or exceed 20 m/s, 80 per cent of the storms recurved before traveling as much as 12 degrees of longitude farther west. The high winds were also used to predict change in direction of forward motion during the next 72 hours. The regression equations developed explain up to 41 per cent of the variance in future direction. In addition to the geostrophic winds used, winds were also obtained by tracking clouds with successive satellite imagery. The u-components of the satellite winds are highly correlated with the geostrophic winds at 200-mb and could probably be used instead of them when available. The v-components are less highly correlated.

  8. Developing a Peak Wind Probability Forecast Tool for Kennedy Space Center and Cape Canaveral Air Force Station

    Science.gov (United States)

    Lambert, WInifred; Roeder, William

    2007-01-01

    This conference presentation describes the development of a peak wind forecast tool to assist forecasters in determining the probability of violating launch commit criteria (LCC) at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) in east-central Florida. The peak winds are an important forecast element for both the Space Shuttle and Expendable Launch Vehicle (ELV) programs. The LCC define specific peak wind thresholds for each launch operation that cannot be exceeded in order to ensure the safety of the vehicle. The 45th Weather Squadron (45 WS) has found that peak winds are a challenging parameter to forecast, particularly in the cool season months of October through April. Based on the importance of forecasting peak winds, the 45 WS tasked the Applied Meteorology Unit (AMU) to develop a short-range peak-wind forecast tool to assist in forecasting LCC violations. The tool will include climatologies of the 5-minute mean and peak winds by month, hour, and direction, and probability distributions of the peak winds as a function of the 5-minute mean wind speeds.

  9. Application of numerical weather prediction in wind power forecasting: Assessment of the diurnal cycle

    Directory of Open Access Journals (Sweden)

    Tobias Heppelmann

    2017-06-01

    Full Text Available For a secure integration of weather dependent renewable energies in Germany's mixed power supply, precise forecasts of expected wind power are indispensable. These in turn are heavily dependent on numerical weather prediction (NWP. With this relevant area of application, NWP models need to be evaluated concerning new variables such as wind speed at hub heights of wind power plants. This article presents verification results of the deterministic NWP forecasts of the global ICON model, its ICON-EU nest, the COSMO-EU, and the COSMO-DE as well as of the ensemble prediction system COSMO-DE-EPS of the German National Weather Service (DWD, against wind mast observations. The focus is on the diurnal cycle in the Planetary Boundary Layer as wind power forecasts for Germany exhibit pronounced systematic amplitude and phase errors in the morning and evening hours. NWP forecasts with lead times up to 48 hours are examined. All considered NWP models reveal shortcomings concerning the representation of the diurnal cycle. Especially in summertime at onshore locations, when Low-Level Jets form, nocturnal wind speeds at hub height are underestimated. In the COSMO model, stable conditions are not sufficiently reflected in the first part of the night and the vertical mixing after sunrise establishes too late. The verification results of the COSMO-DE-EPS confirm the deficiencies of the deterministic forecasts. The deficiencies are present in all ensemble members and thus indicate potential for improvement not only in the model physics parameterization but also concerning the physical ensemble perturbations.

  10. Surface drag effects on simulated wind fields in high-resolution atmospheric forecast model

    Energy Technology Data Exchange (ETDEWEB)

    Lim, Kyo Sun; Lim, Jong Myoung; Ji, Young Yong [Environmental Radioactivity Assessment Team,Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of); Shin, Hye Yum [NOAA/Geophysical Fluid Dynamics Laboratory, Princeton (United States); Hong, Jin Kyu [Yonsei University, Seoul (Korea, Republic of)

    2017-04-15

    It has been reported that the Weather Research and Forecasting (WRF) model generally shows a substantial over prediction bias at low to moderate wind speeds and winds are too geostrophic (Cheng and Steenburgh 2005), which limits the application of WRF model in the area that requires the accurate surface wind estimation such as wind-energy application, air-quality studies, and radioactive-pollutants dispersion studies. The surface drag generated by the subgrid-scale orography is represented by introducing a sink term in the momentum equation in their studies. The purpose of our study is to evaluate the simulated meteorological fields in the high-resolution WRF framework, that includes the parameterization of subgrid-scale orography developed by Mass and Ovens (2010), and enhance the forecast skill of low-level wind fields, which plays an important role in transport and dispersion of air pollutants including radioactive pollutants. The positive bias in 10-m wind speed is significantly alleviated by implementing the subgrid-scale orography parameterization, while other meteorological fields including 10-m wind direction are not changed. Increased variance of subgrid- scale orography enhances the sink of momentum and further reduces the bias in 10-m wind speed.

  11. The use of Markov chains in forecasting wind speed: Matlab source code and applied case study

    Directory of Open Access Journals (Sweden)

    Ionuţ Alexandru Petre

    2017-01-01

    Full Text Available The ability to predict the wind speed has an important role for renewable energy industry which relies on wind speed forecasts in order to calculate the power a wind farm can produce in an area. There are several well-known methods to predict wind speed, but in this paper we focus on short-term wind forecasting using Markov chains. Often gaps can be found in the time series of the wind speed measurements and repeating the measurements is usually not a valid option. In this study it is shown that using Markov chains these gaps from the time series can be filled (they can be generated in an efficient way, but only when the missing data is for a short period of time. Also, the developed Matlab programms that are used in the case study, are included in the paper beeing presented and commented by the authors. In the case study data from a wind farm in Italy is used. The available data are as average wind speed at an interval of 10 minutes in the time period 11/23/2005 - 4/27/2006.

  12. Ensemble downscaling in coupled solar wind-magnetosphere modeling for space weather forecasting.

    Science.gov (United States)

    Owens, M J; Horbury, T S; Wicks, R T; McGregor, S L; Savani, N P; Xiong, M

    2014-06-01

    Advanced forecasting of space weather requires simulation of the whole Sun-to-Earth system, which necessitates driving magnetospheric models with the outputs from solar wind models. This presents a fundamental difficulty, as the magnetosphere is sensitive to both large-scale solar wind structures, which can be captured by solar wind models, and small-scale solar wind "noise," which is far below typical solar wind model resolution and results primarily from stochastic processes. Following similar approaches in terrestrial climate modeling, we propose statistical "downscaling" of solar wind model results prior to their use as input to a magnetospheric model. As magnetospheric response can be highly nonlinear, this is preferable to downscaling the results of magnetospheric modeling. To demonstrate the benefit of this approach, we first approximate solar wind model output by smoothing solar wind observations with an 8 h filter, then add small-scale structure back in through the addition of random noise with the observed spectral characteristics. Here we use a very simple parameterization of noise based upon the observed probability distribution functions of solar wind parameters, but more sophisticated methods will be developed in the future. An ensemble of results from the simple downscaling scheme are tested using a model-independent method and shown to add value to the magnetospheric forecast, both improving the best estimate and quantifying the uncertainty. We suggest a number of features desirable in an operational solar wind downscaling scheme. Solar wind models must be downscaled in order to drive magnetospheric models Ensemble downscaling is more effective than deterministic downscaling The magnetosphere responds nonlinearly to small-scale solar wind fluctuations.

  13. Trading wind energy on the basis of probabilistic forecasts both of wind generation and of market quantities

    DEFF Research Database (Denmark)

    Zugno, Marco; Jónsson, Tryggvi; Pinson, Pierre

    2013-01-01

    in liberalized electricity markets and to assess its performance. At first, the so-called optimal quantile strategy is revisited. It is proved that without market power, i.e. under the price-taker assumption, this strategy maximizes expected market revenues. Forecasts of wind power production, of day......-ahead and real-time market prices and of the system imbalance are inputs to this strategy. Subsequently, constraining of the bid that maximizes the expected revenues is proposed as a way to overcome the strategy's disregard of practical limitations and, at the same time, of risk. Two constraining techniques......Wind power is not easily predictable and non-dispatchable. Nevertheless, wind power producers are increasingly urged to participate in electricity market auctions in the same manner as conventional power producers. The aim of this paper is to propose an operational strategy for trading wind energy...

  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. 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

    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......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...

  16. Multi-Stage Optimization-Based Automatic Voltage Control Systems Considering Wind Power Forecasting Errors

    DEFF Research Database (Denmark)

    Qin, Nan; Bak, Claus Leth; Abildgaard, Hans

    2017-01-01

    cost and the generator reactive power output cost. The problem is formulated in a multi-stage optimal reactive power flow (MORPF) framework, solved by the nonlinear programming techniques via a rolling process. The voltage uncertainty caused by wind power forecasting errors is considered in the optimal...

  17. A comparison of regression algorithms for wind speed forecasting at Alexander Bay

    CSIR Research Space (South Africa)

    Botha, Nicolene

    2016-12-01

    Full Text Available to forecast 1 to 24 hours ahead, in hourly intervals. Predictions are performed on a wind speed time series with three machine learning regression algorithms, namely support vector regression, ordinary least squares and Bayesian ridge regression. The resulting...

  18. 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.

  19. Verification of the ECMWF ensemble forecasts of wind speed against analyses and observations

    DEFF Research Database (Denmark)

    Pinson, Pierre; Hagedorn, Renate

    2012-01-01

    A framework for the verification of ensemble forecasts of near-surface wind speed is described. It is based on existing scores and diagnostic tools, though considering observations from synoptic stations as reference instead of the analysis. This approach is motivated by the idea of having a user...

  20. Real Time Wave Forecasting Using Wind Time History and Genetic Programming

    Directory of Open Access Journals (Sweden)

    A.R. Kambekar

    2014-12-01

    Full Text Available The significant wave height and average wave period form an essential input for operational activities in ocean and coastal areas. Such information is important in issuing appropriate warnings to people planning any construction or instillation works in the oceanic environment. Many countries over the world routinely collect wave and wind data through a network of wave rider buoys. The data collecting agencies transmit the resulting information online to their registered users through an internet or a web-based system. Operational wave forecasts in addition to the measured data are also made and supplied online to the users. This paper discusses operational wave forecasting in real time mode at locations where wind rather than wave data are continuously recorded. It is based on the time series modeling and incorporates an artificial intelligence technique of genetic programming. The significant wave height and average wave period values are forecasted over a period of 96 hr in future from the observations of wind speed and directions extending to a similar time scale in the past. Wind measurements made by floating buoys at eight different locations around India over a period varying from 1.5 yr to 9.0 yr were considered. The platform of Matlab and C++ was used to develop a graphical user interface that will extend an internet based user-friendly access of the forecasts to any registered user of the data dissemination authority.

  1. Analysis of Wind Speed Forecasting Error Effects on Automatic Generation Control Performance

    Directory of Open Access Journals (Sweden)

    H. Rajabi Mashhadi

    2014-09-01

    Full Text Available The main goal of this paper is to study statistical indices and evaluate AGC indices in power system which has large penetration of the WTGs. Increasing penetration of wind turbine generations, needs to study more about impacts of it on power system frequency control. Frequency control is changed with unbalancing real-time system generation and load . Also wind turbine generations have more fluctuations and make system more unbalance. Then AGC loop helps to adjust system frequency and the scheduled tie-line powers. The quality of AGC loop is measured by some indices. A good index is a proper measure shows the AGC performance just as the power system operates. One of well-known measures in literature which was introduced by NERC is Control Performance Standards(CPS. Previously it is claimed that a key factor in CPS index is related to standard deviation of generation error, installed power and frequency response. This paper focuses on impact of a several hours-ahead wind speed forecast error on this factor. Furthermore evaluation of conventional control performances in the power systems with large-scale wind turbine penetration is studied. Effects of wind speed standard deviation and also degree of wind farm penetration are analyzed and importance of mentioned factor are criticized. In addition, influence of mean wind speed forecast error on this factor is investigated. The study system is a two area system which there is significant wind farm in one of those. The results show that mean wind speed forecast error has considerable effect on AGC performance while the mentioned key factor is insensitive to this mean error.

  2. 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

  3. Exploiting sparsity of interconnections in spatio-temporal wind speed forecasting using Wavelet Transform

    International Nuclear Information System (INIS)

    Tascikaraoglu, Akin; Sanandaji, Borhan M.; Poolla, Kameshwar; Varaiya, Pravin

    2016-01-01

    Highlights: • We propose a spatio-temporal approach for wind speed forecasting. • The method is based on a combination of Wavelet decomposition and structured-sparse recovery. • Our analyses confirm that low-dimensional structures govern the interactions between stations. • Our method particularly shows improvements for profiles with high ramps. • We examine our approach on real data and illustrate its superiority over a set of benchmark models. - Abstract: Integration of renewable energy resources into the power grid is essential in achieving the envisioned sustainable energy future. Stochasticity and intermittency characteristics of renewable energies, however, present challenges for integrating these resources into the existing grid in a large scale. Reliable renewable energy integration is facilitated by accurate wind forecasts. In this paper, we propose a novel wind speed forecasting method which first utilizes Wavelet Transform (WT) for decomposition of the wind speed data into more stationary components and then uses a spatio-temporal model on each sub-series for incorporating both temporal and spatial information. The proposed spatio-temporal forecasting approach on each sub-series is based on the assumption that there usually exists an intrinsic low-dimensional structure between time series data in a collection of meteorological stations. Our approach is inspired by Compressive Sensing (CS) and structured-sparse recovery algorithms. Based on detailed case studies, we show that the proposed approach based on exploiting the sparsity of correlations between a large set of meteorological stations and decomposing time series for higher-accuracy forecasts considerably improve the short-term forecasts compared to the temporal and spatio-temporal benchmark methods.

  4. Verification of high-speed solar wind stream forecasts using operational solar wind models

    DEFF Research Database (Denmark)

    Reiss, Martin A.; Temmer, Manuela; Veronig, Astrid M.

    2016-01-01

    and the background solar wind conditions. We found that both solar wind models are capable of predicting the large-scale features of the observed solar wind speed (root-mean-square error, RMSE ≈100 km/s) but tend to either overestimate (ESWF) or underestimate (WSA) the number of high-speed solar wind streams (threat...

  5. Ensemble and probabilistic forecasting of (u,v)-wind for the energy application

    DEFF Research Database (Denmark)

    Pinson, Pierre

    2011-01-01

    and probabilistic forecasts are becoming increasingly popular among the actors of the power system and electricity markets. The energy application is particularly interesting since covering a variety of decision-making problems requiring different types of input forecasts. A few of them will be reviewed...... behaviour embedding variability and potentially limited predictability. This forces substantial changes to the energy management and trading activities, which are to increasingly rely on high-quality meteorological forecasts for various lead times ranging from a few minutes to a few months, while evolving...... of the ensembles and the wind stochastic process. The parameters of these models are adaptively and recursively estimated, hence allowing for seasonal variations in the calibration while accommodating changes in the operational setup of the ensemble forecasting system considered. These model parameters are also...

  6. High resolution forecasting for wind energy applications using Bayesian model averaging

    Energy Technology Data Exchange (ETDEWEB)

    Courtney, Jennifer F.; Lynch, Peter; Sweeney, Conor [Meteorology and Climate Centre, UCD, Dublin (Ireland)], e-mail: jennifer.courtney@ucdconnect.ie

    2013-02-15

    Two methods of post-processing the uncalibrated wind speed forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble prediction system (EPS) are presented here. Both methods involve statistically post-processing the EPS or a downscaled version of it with Bayesian model averaging (BMA). The first method applies BMA directly to the EPS data. The second method involves clustering the EPS to eight representative members (RMs) and downscaling the data through two limited area models at two resolutions. Four weighted ensemble mean forecasts are produced and used as input to the BMA method. Both methods are tested against 13 meteorological stations around Ireland with 1 yr of forecast/observation data. Results show calibration and accuracy improvements using both methods, with the best results stemming from Method 2, which has comparatively low mean absolute error and continuous ranked probability scores.

  7. High resolution forecasting for wind energy applications using Bayesian model averaging

    Directory of Open Access Journals (Sweden)

    Jennifer F. Courtney

    2013-02-01

    Full Text Available Two methods of post-processing the uncalibrated wind speed forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF ensemble prediction system (EPS are presented here. Both methods involve statistically post-processing the EPS or a downscaled version of it with Bayesian model averaging (BMA. The first method applies BMA directly to the EPS data. The second method involves clustering the EPS to eight representative members (RMs and downscaling the data through two limited area models at two resolutions. Four weighted ensemble mean forecasts are produced and used as input to the BMA method. Both methods are tested against 13 meteorological stations around Ireland with 1 yr of forecast/observation data. Results show calibration and accuracy improvements using both methods, with the best results stemming from Method 2, which has comparatively low mean absolute error and continuous ranked probability scores.

  8. Analysis and forecasting of wind velocity in chetumal, quintana roo, using the single exponential smoothing method

    Energy Technology Data Exchange (ETDEWEB)

    Cadenas, E. [Facultad de Ingenieria Mecanica, Universidad Michoacana de San Nicolas de Hidalgo, Santiago Tapia No. 403, Centro (Mexico); Jaramillo, O.A.; Rivera, W. [Centro de Ivestigacion en Energia, Universidad Nacional Autonoma de Mexico, Apartado Postal 34, Temixco 62580, Morelos (Mexico)

    2010-05-15

    In this paper the analysis and forecasting of wind velocities in Chetumal, Quintana Roo, Mexico is presented. Measurements were made by the Instituto de Investigaciones Electricas (IIE) during two years, from 2004 to 2005. This location exemplifies the wind energy generation potential in the Caribbean coast of Mexico that could be employed in the hotel industry in the next decade. The wind speed and wind direction were measured at 10 m above ground level. Sensors with high accuracy and a low starting threshold were used. The wind velocity was recorded using a data acquisition system supplied by a 10 W photovoltaic panel. The wind speed values were measured with a frequency of 1 Hz and the average wind speed was recorded considering regular intervals of 10 min. First a statistical analysis of the time series was made in the first part of the paper through conventional and robust measures. Also the forecasting of the last day of measurements was made utilizing the single exponential smoothing method (SES). The results showed a very good accuracy of the data with this technique for an {alpha} value of 0.9. Finally the SES method was compared with the artificial neural network (ANN) method showing the former better results. (author)

  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. 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...... 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....... 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...

  11. 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

    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...... 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...

  12. 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....

  13. 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....

  14. Wind Speed Forecasting Based on FEEMD and LSSVM Optimized by the Bat Algorithm

    Directory of Open Access Journals (Sweden)

    Wei Sun

    2015-06-01

    Full Text Available Affected by various environmental factors, wind speed presents high fluctuation, nonlinear and non-stationary characteristics. To evaluate wind energy properly and efficiently, this paper proposes a modified fast ensemble empirical model decomposition (FEEMD-bat algorithm (BA-least support vector machines (LSSVM (FEEMD-BA-LSSVM model combined with input selected by deep quantitative analysis. The original wind speed series are first decomposed into a limited number of intrinsic mode functions (IMFs with one residual series. Then a LSSVM is built to forecast these sub-series. In order to select input from environment variables, Cointegration and Granger causality tests are proposed to check the influence of temperature with different leading lengths. Partial correlation is applied to analyze the inner relationships between the historical speeds thus to select the LSSVM input. The parameters in LSSVM are fine-tuned by BA to ensure the generalization of LSSVM. The forecasting results suggest the hybrid approach outperforms the compared models.

  15. Forecasting and decision-making in electricity markets with focus on wind energy

    DEFF Research Database (Denmark)

    Jónsson, Tryggvi

    by these different characteristics. The thesis presents analyses of how this impact is realised in markets significantly penetrated by wind power. Due to its representation by forecasts in the supply curve, such predictions are used to describe their non-linear influence on the market prices. Methods adequately...... accounting for this effect in models for day-ahead forecasting of the prices are also presented in the thesis. Prompted by the volatile behaviour of electricity markets, considerable focus has been on time-varying and robust parameter estimates. The models derived are all based on well know methods from...... the statistical literature. The stochastic production of wind turbines prompts the need for alternative methods for optimally bidding wind power to day-ahead markets. Such bidding strategies are formulated in this thesis, which utilise the information provided by the market models. Bids that maximise expected...

  16. 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

  17. Wind Power Forecasting Based on Echo State Networks and Long Short-Term Memory

    DEFF Research Database (Denmark)

    López, Erick; Allende, Héctor; Gil, Esteban

    2018-01-01

    Wind power generation has presented an important development around the world. However, its integration into electrical systems presents numerous challenges due to the variable nature of the wind. Therefore, to maintain an economical and reliable electricity supply, it is necessary to accurately...... predict wind generation. The Wind Power Prediction Tool (WPPT) has been proposed to solve this task using the power curve associated with a wind farm. Recurrent Neural Networks (RNNs) model complex non-linear relationships without requiring explicit mathematical expressions that relate the variables...... involved. In particular, two types of RNN, Long Short-Term Memory (LSTM) and Echo State Network (ESN), have shown good results in time series forecasting. In this work, we present an LSTM+ESN architecture that combines the characteristics of both networks. An architecture similar to an ESN is proposed...

  18. Statistical learning for wind power: A modeling and stability study towards forecasting

    Science.gov (United States)

    Fischer, Aurélie; Montuelle, Lucie; Mougeot, Mathilde; Picard, Dominique

    2017-12-01

    We focus on wind power modeling using machine learning techniques. We show on real data provided by the wind energy company Ma{\\"i}a Eolis, that parametric models, even following closely the physical equation relating wind production to wind speed are outperformed by intelligent learning algorithms. In particular, the CART-Bagging algorithm gives very stable and promising results. Besides, as a step towards forecast, we quantify the impact of using deteriorated wind measures on the performances. We show also on this application that the default methodology to select a subset of predictors provided in the standard random forest package can be refined, especially when there exists among the predictors one variable which has a major impact.

  19. 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.)

  20. 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 Vector Autoregressive (SC-VAR) model is introduced to obtain sparse model structures in a spatio-temporal wind power forecasting framework by reformulating the original VAR model into a constrained Mixed Integer Non-Linear Programming (MINLP) problem. It allows controlling the sparsity of the coefficient...... and forecasting, the original SC-VAR is modified and a Correlation-Constrained SC-VAR (CCSC-VAR) is proposed based on spatial correlation information about wind farms. Our approach is evaluated based on a case study of very-short-term forecasting for 25 wind farms in Denmark. Comparison is performed with a set...

  1. 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....... In this study, a MOS based on multiple linear regression is proposed: the model screens the most relevant NWP forecast variables and selects the best predictors to fit a regression equation that minimizes the forecast errors, utilizing wind farm power output measurements as input. The performance of the method...... is evaluated in two wind farms, located in different topographical areas and with different NWP grid spacing. Because of the high seasonal variability of NWP forecasts, it was considered appropriate to implement monthly stratified MOS. In both wind farms, the first predictors were always wind speeds (at...

  2. Enhanced Short-Term Wind Power Forecasting and Value to Grid Operations: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Orwig, K.; Clark, C.; Cline, J.; Benjamin, S.; Wilczak, J.; Marquis, M.; Finley, C.; Stern, A.; Freedman, J.

    2012-09-01

    The current state of the art of wind power forecasting in the 0- to 6-hour time frame has levels of uncertainty that are adding increased costs and risk on 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 1-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 provides an overview of the regions selected, instrumentation deployed, data quality and control, assimilation of data into HRRR, and preliminary results of HRRR performance analysis.

  3. A Hybrid Model Based on Ensemble Empirical Mode Decomposition and Fruit Fly Optimization Algorithm for Wind Speed Forecasting

    Directory of Open Access Journals (Sweden)

    Zongxi Qu

    2016-01-01

    Full Text Available As a type of clean and renewable energy, the superiority of wind power has increasingly captured the world’s attention. Reliable and precise wind speed prediction is vital for wind power generation systems. Thus, a more effective and precise prediction model is essentially needed in the field of wind speed forecasting. Most previous forecasting models could adapt to various wind speed series data; however, these models ignored the importance of the data preprocessing and model parameter optimization. In view of its importance, a novel hybrid ensemble learning paradigm is proposed. In this model, the original wind speed data is firstly divided into a finite set of signal components by ensemble empirical mode decomposition, and then each signal is predicted by several artificial intelligence models with optimized parameters by using the fruit fly optimization algorithm and the final prediction values were obtained by reconstructing the refined series. To estimate the forecasting ability of the proposed model, 15 min wind speed data for wind farms in the coastal areas of China was performed to forecast as a case study. The empirical results show that the proposed hybrid model is superior to some existing traditional forecasting models regarding forecast performance.

  4. Probabilistic modelling for forecasting the wind energy resource at the seasonal horizon

    Science.gov (United States)

    Alonzo, Bastien; Drobinski, Philippe; Plougonven, Riwal; Tankov, Peter

    2017-04-01

    We build and evaluate a probabilistic model designed for forecasting the distribution of the daily mean wind speed at the seasonal timescale. On such long-term timescales, numerical weather prediction models can bring valuable information on the large-scale circulation of the atmosphere which strongly influences surface wind speed. As an example, variations in the position of the storm track over the Atlantic directly impact surface winds in the North of France in autumn and winter. The model aims at predicting the daily mean wind speed distribution knowing the large scale situation of the atmosphere which is summarized by an index derived from the multi-polynomial regression between the 10 first Principal Components of the 500hPa geopotential height and the daily mean wind speed. The conditionnal probability density function of the wind speed knowing the index is estimated by a gaussian kernel density estimation over 20 years of daily reanalysis data. Evaluating the probabilistic model on a validation period of 15 years, we show that it is at least as well calibrated as the seasonal climatology which can be taken as a first guess prediction at such long-term horizon. We also show that the model is 20% sharper than the climatology in average, due to a less pronounced seasonal variability of the confidence interval width. We use the ECMWF seasonal forecast ensemble in order to predict the daily mean wind speed distribution at the seasonal timescale. The ensemble forecast, from which the index is derived, displays a growing uncertainty with time leading to an increase of the confidence interval width predicted by the probabilistic model. We show that the model remains sharper than the climatology at the monthly horizon, but tends to the climatological interval width after 30 days.

  5. Time Series Analysis and Forecasting for Wind Speeds Using Support Vector Regression Coupled with Artificial Intelligent Algorithms

    Directory of Open Access Journals (Sweden)

    Ping Jiang

    2015-01-01

    Full Text Available Wind speed/power has received increasing attention around the earth due to its renewable nature as well as environmental friendliness. With the global installed wind power capacity rapidly increasing, wind industry is growing into a large-scale business. Reliable short-term wind speed forecasts play a practical and crucial role in wind energy conversion systems, such as the dynamic control of wind turbines and power system scheduling. In this paper, an intelligent hybrid model for short-term wind speed prediction is examined; the model is based on cross correlation (CC analysis and a support vector regression (SVR model that is coupled with brainstorm optimization (BSO and cuckoo search (CS algorithms, which are successfully utilized for parameter determination. The proposed hybrid models were used to forecast short-term wind speeds collected from four wind turbines located on a wind farm in China. The forecasting results demonstrate that the intelligent hybrid models outperform single models for short-term wind speed forecasting, which mainly results from the superiority of BSO and CS for parameter optimization.

  6. A data-driven multi-model methodology with deep feature selection for short-term wind forecasting

    International Nuclear Information System (INIS)

    Feng, Cong; Cui, Mingjian; Hodge, Bri-Mathias; Zhang, Jie

    2017-01-01

    Highlights: • An ensemble model is developed to produce both deterministic and probabilistic wind forecasts. • A deep feature selection framework is developed to optimally determine the inputs to the forecasting methodology. • The developed ensemble methodology has improved the forecasting accuracy by up to 30%. - Abstract: With the growing wind penetration into the power system worldwide, improving wind power forecasting accuracy is becoming increasingly important to ensure continued economic and reliable power system operations. In this paper, a data-driven multi-model wind forecasting methodology is developed with a two-layer ensemble machine learning technique. The first layer is composed of multiple machine learning models that generate individual forecasts. A deep feature selection framework is developed to determine the most suitable inputs to the first layer machine learning models. Then, a blending algorithm is applied in the second layer to create an ensemble of the forecasts produced by first layer models and generate both deterministic and probabilistic forecasts. This two-layer model seeks to utilize the statistically different characteristics of each machine learning algorithm. A number of machine learning algorithms are selected and compared in both layers. This developed multi-model wind forecasting methodology is compared to several benchmarks. The effectiveness of the proposed methodology is evaluated to provide 1-hour-ahead wind speed forecasting at seven locations of the Surface Radiation network. Numerical results show that comparing to the single-algorithm models, the developed multi-model framework with deep feature selection procedure has improved the forecasting accuracy by up to 30%.

  7. 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.

  8. NWP Forecast Errors of Boundary Layer Flow in Complex Terrain Observed During the Second Wind Forecast Improvement Project (WFIP2) Field Campaign.

    Science.gov (United States)

    Wilczak, James M.

    2017-04-01

    The Second Wind Forecast Improvement Project (WFIP2) is a U.S. Department of Energy and NOAA-led program whose goal is to improve the accuracy of NWP forecasts of wind speed in complex terrain for wind energy applications. WFIP2 includes a field campaign held in the vicinity of the Columbia River Basin in the Pacific Northwest of the U.S., which began in October 2015, and will continue through March, 2017. As part of WFIP2 a large suite of in-situ and remote sensing instrumentation has been deployed, including a network of three 449 MHz radar wind profilers (RWP's) with RASS, eight 915 MHz RWP's with RASS, 18 sodars, 4 profiling microwave radiometers, 5 scanning lidars, 5 profiling lidars, a network of 10 microbarographs, and many surface meteorological stations. Key NWP forecast models utilized for WFIP2 are the 13 km resolution Rapid Refresh (RAP), 3km High Resolution Rapid Refresh (HRRR), 0.75km HRRR-Nest, and the 12 km North American Mesoscale (NAM) forecast system. Preliminary results from WFIP2 will be presented, including seasonal variations of model forecast errors of wind speed, direction, temperature and humidity profiles and boundary layer depths; meteorological phenomena producing large forecast errors; and the relative skill of the various NWP forecasting systems. Diurnal time height cross-sections of the model's mean bias and RMSE are evaluated for each of the models, providing a holistic view of model accuracy at simulating boundary layer structure. Model errors are analyzed as a function of season (3 month averages) and location, and show the impact of increasing model resolution on forecast skill. Seasonal averages of model biases and RMSE provide more robust results than do shorter case study episodes, and can be used to verify that model errors found in shorter case study episodes are in fact representative. The results are used to identify specific model weaknesses and the corresponding parameterization schemes that are in greatest need of

  9. Tool for Forecasting Cool-Season Peak Winds Across Kennedy Space Center and Cape Canaveral Air Force Station (CCAFS)

    Science.gov (United States)

    Barrett, Joe H., III; Roeder, William P.

    2010-01-01

    Peak wind speed is important element in 24-Hour and Weekly Planning Forecasts issued by 45th Weather Squadron (45 WS). Forecasts issued for planning operations at KSC/CCAFS. 45 WS wind advisories issued for wind gusts greater than or equal to 25 kt. 35 kt and 50 kt from surface to 300 ft. AMU developed cool-season (Oct - Apr) tool to help 45 WS forecast: daily peak wind speed, 5-minute average speed at time of peak wind, and probability peak speed greater than or equal to 25 kt, 35 kt, 50 kt. AMU tool also forecasts daily average wind speed from 30 ft to 60 ft. Phase I and II tools delivered as a Microsoft Excel graphical user interface (GUI). Phase II tool also delivered as Meteorological Interactive Data Display System (MIDDS) GUI. Phase I and II forecast methods were compared to climatology, 45 WS wind advisories and North American Mesoscale model (MesoNAM) forecasts in a verification data set.

  10. International wind energy development. World market update 2012. Forecast 2013-2017

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2013-03-15

    The BTM wind report, World Market Update 2012, published by Navigant Research, is the eighteenth edition of this annual wind energy market report. The report includes more than 80 tables, charts and graphs illustrating global wind market development, as well as a wind market forecast for 2013?2017 and highlighted trends for the wind market through 2022. The report delivers several views on the fast?growing wind market, including: 1) More than 285 GW of wind power now installed globally; 2) 45GW of new capacity added in 2012, including 1.1 GW from offshore wind; 3) The United States surpassed China as the largest market in terms of new installations in 2012; 4) Europe lost its position as the largest world region in terms of new installations; 5) Wind installations in the Americas grew by 12.3 percent compared with 2011; 6) Big shake?up in the top ten wind turbine supplier ranking; 7) Strong Chinese presence among top 15 wind owner?operators; 8) Wind market structures continue to evolve; 9) The penetration of wind power in the world's electricity supply has reached 2.62 percent; 10) Offshore wind more than doubled the capacity added in 2011, with more than 4 GW currently under construction. With the addition of 44,951 MW in new installations in 2012, world wind power capacity grew to around 285,700 MW, an increase in the total wind power installation base of 18.6 percent. Market growth year-over-year in 2012, though a modest 7.8 percent, was still higher than in 2011. Average annual growth for the past five years has been 17.8 percent, achieved during the aftermath of the 2008 financial crisis, with traditionally large markets for wind power in economic recession in America and Europe. The wind power industry continues to demonstrate its ability to rapidly evolve to meet new demands in markets that face a variety of challenges. The focus on product diversification grows with wind turbine vendors designing machines for maximum energy production in low wind speed

  11. Recursive wind speed forecasting based on Hammerstein Auto-Regressive model

    International Nuclear Information System (INIS)

    Ait Maatallah, Othman; Achuthan, Ajit; Janoyan, Kerop; Marzocca, Pier

    2015-01-01

    Highlights: • Developed a new recursive WSF model for 1–24 h horizon based on Hammerstein model. • Nonlinear HAR model successfully captured chaotic dynamics of wind speed time series. • Recursive WSF intrinsic error accumulation corrected by applying rotation. • Model verified for real wind speed data from two sites with different characteristics. • HAR model outperformed both ARIMA and ANN models in terms of accuracy of prediction. - Abstract: A new Wind Speed Forecasting (WSF) model, suitable for a short term 1–24 h forecast horizon, is developed by adapting Hammerstein model to an Autoregressive approach. The model is applied to real data collected for a period of three years (2004–2006) from two different sites. The performance of HAR model is evaluated by comparing its prediction with the classical Autoregressive Integrated Moving Average (ARIMA) model and a multi-layer perceptron Artificial Neural Network (ANN). Results show that the HAR model outperforms both the ARIMA model and ANN model in terms of root mean square error (RMSE), mean absolute error (MAE), and Mean Absolute Percentage Error (MAPE). When compared to the conventional models, the new HAR model can better capture various wind speed characteristics, including asymmetric (non-gaussian) wind speed distribution, non-stationary time series profile, and the chaotic dynamics. The new model is beneficial for various applications in the renewable energy area, particularly for power scheduling

  12. Short-term forecasting of thunderstorms at Kennedy Space Center, based on the surface wind field

    Science.gov (United States)

    Watson, Andrew I.; Lopez, Raul E.; Holle, Ronald L.; Daugherty, John R.; Ortiz, Robert

    1989-01-01

    Techniques incorporating wind convergence that can be used for the short-term prediction of thunderstorm development are described. With these techniques, the convergence signal is sensed by the wind network array 15 to 90 min before actual storm development. Particular attention is given to the convergence cell technique (which has been applied at the Kennedy Space Center) where each convective region is analyzed independently. It is noted that, while the monitoring of areal and cellular convergence can be used to help locate the seeds of developing thunderstorms and pinpoint the lightning threat areas, this forecasting aid cannot be used in isolation.

  13. Wind laws for shockless initialization. [numerical forecasting model

    Science.gov (United States)

    Ghil, M.; Shkoller, B.

    1976-01-01

    A system of diagnostic equations for the velocity field, or wind laws, was derived for each of a number of models of large-scale atmospheric flow. The derivation in each case is mathematically exact and does not involve any physical assumptions not already present in the prognostic equations, such as nondivergence or vanishing of derivatives of the divergence. Therefore, initial states computed by solving these diagnostic equations should be compatible with the type of motion described by the prognostic equations of the model and should not generate initialization shocks when inserted into the model. Numerical solutions of the diagnostic system corresponding to a barotropic model are exhibited. Some problems concerning the possibility of implementing such a system in operational numerical weather prediction are discussed.

  14. Operational Utilization of High Resolution Ocean Surface Wind Vectors (25km or better) in the Marine Forecasting Environment

    National Research Council Canada - National Science Library

    Chang, Paul S; Sienkiewicz, Joseph; Knabb, Richard; Gaiser, Peter W; Long, David G; Freeberg, Mark

    2005-01-01

    .... This work will build upon an ongoing effort to quantify the impacts of QuikSCAT ocean vector wind data in the operational short-term warnings and forecasts issued by the NWS Ocean Prediction Center (OPC...

  15. Operational Utilization of High Resolution Ocean Surface Wind Vectors (25km or Better) in the Marine Forecasting Environment

    National Research Council Canada - National Science Library

    Chang, Paul S; Sienkiewicz, Joseph; Knabb, Richard; Gaiser, Peter W; Long, David G; Freeberg, Mark

    2004-01-01

    .... This work will build upon an ongoing effort to quantify the impacts of QuikSCAT ocean vector wind data in the operational short-term warnings and forecasts issued by the NWS Ocean Prediction Center (OPC...

  16. Impact of scatterometer wind (ASCAT-A/B) data assimilation on semi real-time forecast system at KIAPS

    Science.gov (United States)

    Han, H. J.; Kang, J. H.

    2016-12-01

    Since Jul. 2015, KIAPS (Korea Institute of Atmospheric Prediction Systems) has been performing the semi real-time forecast system to assess the performance of their forecast system as a NWP model. KPOP (KIAPS Protocol for Observation Processing) is a part of KIAPS data assimilation system and has been performing well in KIAPS semi real-time forecast system. In this study, due to the fact that KPOP would be able to treat the scatterometer wind data, we analyze the effect of scatterometer wind (ASCAT-A/B) on KIAPS semi real-time forecast system. O-B global distribution and statistics of scatterometer wind give use two information which are the difference between background field and observation is not too large and KPOP processed the scatterometer wind data well. The changes of analysis increment because of O-B global distribution appear remarkably at the bottom of atmospheric field. It also shows that scatterometer wind data cover wide ocean where data would be able to short. Performance of scatterometer wind data can be checked through the vertical error reduction against IFS between background and analysis field and vertical statistics of O-A. By these analysis result, we can notice that scatterometer wind data will influence the positive effect on lower level performance of semi real-time forecast system at KIAPS. After, long-term result based on effect of scatterometer wind data will be analyzed.

  17. Can Weather Radars Help Monitoring and Forecasting Wind Power Fluctuations at Large Offshore Wind Farms?

    DEFF Research Database (Denmark)

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

    2011-01-01

    The substantial impact of wind power fluctuations at large offshore wind farms calls for the development of dedicated monitoring and prediction approaches. Based on recent findings, a Local Area Weather Radar (LAWR) was installed at Horns Rev with the aim of improving predictability, controlability...... and potentially maintenance planning. Additional images are available from a Doppler radar covering the same area. The parallel analysis of rain events detection and of regime sequences in wind (and power) fluctuations demonstrates the interest of employing weather radars for a better operation and management...... of offshore wind farms....

  18. Weather Research and Forecasting Model Wind Sensitivity Study at Edwards Air Force Base, CA

    Science.gov (United States)

    Watson, Leela R.; Bauman, William H., III; Hoeth, Brian

    2009-01-01

    This abstract describes work that will be done by the Applied Meteorology Unit (AMU) in assessing the success of different model configurations in predicting "wind cycling" cases at Edwards Air Force Base, CA (EAFB), in which the wind speeds and directions oscillate among towers near the EAFB runway. The Weather Research and Forecasting (WRF) model allows users to choose among two dynamical cores - the Advanced Research WRF (ARW) and the Non-hydrostatic Mesoscale Model (NMM). There are also data assimilation analysis packages available for the initialization of the WRF model - the Local Analysis and Prediction System (LAPS) and the Advanced Regional Prediction System (ARPS) Data Analysis System (ADAS). Having a series of initialization options and WRF cores, as well as many options within each core, creates challenges for local forecasters, such as determining which configuration options are best to address specific forecast concerns. The goal of this project is to assess the different configurations available and determine which configuration will best predict surface wind speed and direction at EAFB.

  19. International wind energy development. World market update 1998. Forecast 1999-2003

    International Nuclear Information System (INIS)

    1999-03-01

    This is the fourth issue of the annual World Market Update from BTM Consult ApS, covering the year 1998. All figures in the status part refer to end of the year 1998, the past 3 years development is also assessed and the forecast looks 5 years ahead. The most significant figures and trends in 1998 were: The marketplace - The annual installation of new wind power capacity increased by 55% resulting in a cumulative installation by the end of 1998 of 10.153 MW. 1.766 MW was installed in Europe and the region is still the leading market regarding utilization of wind energy. The US market took a rapid pace and installed 577 MW during the year. The large Enron Wind Corp has taken the larger part of this market. On the supply side Danish NEG Micon A/S has consolidated the position as being the supplier of the most MW wind capacity in the world and the company has a world market share of 23,5 per cent. The company acquired the Danish Wind World af 1997 A/S which was among the larger companies in 1997. Also the Dutch manufacturer NedWind B.V. was acquired by NEG Micon A/S curing 1998. The group of 'other' manufactureres represents a minor percentage of deliveries than earlier and concentration in the industry seems to continue. The liberalized Energy Market and how to position the industry in this different economic environment will be a challenge for the wind industry way into the next century. In Europe, the European Commission's draft Directive with proposal for an outline of common rules for support of among other renewables wind energy has been set on another route which seems to delay the paper. In the US there are still hopes for a new period with PTC (Production Tax Credit). There are in some States hopes among the wind energy people that the 'Green Market Programs' will play a more dominant role in the future. In Asia the crises seems to halt the wind power development. Forecast and Technical trends - Based on the positive trends in the markets for wind power

  20. Tool for Forecasting Cool-Season Peak Winds Across Kennedy Space Center and Cape Canaveral Air Force Station

    Science.gov (United States)

    Barrett, Joe H., III; Roeder, William P.

    2010-01-01

    The expected peak wind speed for the day is an important element in the daily morning forecast for ground and space launch operations at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). The 45th Weather Squadron (45 WS) must issue forecast advisories for KSC/CCAFS when they expect peak gusts for >= 25, >= 35, and >= 50 kt thresholds at any level from the surface to 300 ft. In Phase I of this task, the 45 WS tasked the Applied Meteorology Unit (AMU) to develop a cool-season (October - April) tool to help forecast the non-convective peak wind from the surface to 300 ft at KSC/CCAFS. During the warm season, these wind speeds are rarely exceeded except during convective winds or under the influence of tropical cyclones, for which other techniques are already in use. The tool used single and multiple linear regression equations to predict the peak wind from the morning sounding. The forecaster manually entered several observed sounding parameters into a Microsoft Excel graphical user interface (GUI), and then the tool displayed the forecast peak wind speed, average wind speed at the time of the peak wind, the timing of the peak wind and the probability the peak wind will meet or exceed 35, 50 and 60 kt. The 45 WS customers later dropped the requirement for >= 60 kt wind warnings. During Phase II of this task, the AMU expanded the period of record (POR) by six years to increase the number of observations used to create the forecast equations. A large number of possible predictors were evaluated from archived soundings, including inversion depth and strength, low-level wind shear, mixing height, temperature lapse rate and winds from the surface to 3000 ft. Each day in the POR was stratified in a number of ways, such as by low-level wind direction, synoptic weather pattern, precipitation and Bulk Richardson number. The most accurate Phase II equations were then selected for an independent verification. The Phase I and II forecast methods were

  1. 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.

  2. Research and Application Based on Adaptive Boosting Strategy and Modified CGFPA Algorithm: A Case Study for Wind Speed Forecasting

    Directory of Open Access Journals (Sweden)

    Jiani Heng

    2016-03-01

    Full Text Available Wind energy is increasingly considered one of the most promising sustainable energy sources for its characteristics of cleanliness without any pollution. Wind speed forecasting is a vital problem in wind power industry. However, individual forecasting models ignore the significance of data preprocessing and model parameter optimization, which may lead to poor forecasting performance. In this paper, a novel hybrid [k, Bt] -ABBP (back propagation based on adaptive strategy with parameters k and Bt model was developed based on an adaptive boosting (AB strategy that integrates several BP (back propagation neural networks for wind speed forecasting. The fast ensemble empirical mode decomposition technique is initially conducted in the preprocessing stage to reconstruct data, while a novel modified FPA (flower pollination algorithm incorporating a conjugate gradient (CG is proposed for searching for the optimal parameters of the [k, Bt] -ABBP mode. The case studies of five wind power stations in Penglai, China are used as illustrative examples for evaluating the effectiveness and efficiency of the developed hybrid forecast strategy. Numerical results show that the developed hybrid model is simple and can satisfactorily approximate the actual wind speed series. Therefore, the developed hybrid model can be an effective tool in mining and analysis for wind power plants.

  3. Cepheid radii and effective temperatures

    International Nuclear Information System (INIS)

    Fernley, J.A.; Skillen, I.; Jameson, R.F.

    1989-01-01

    New infrared photometry for the Cepheid variables T Vul, δ Cephei and XCyg is presented. Combining this with published infrared photometry of T Vul, ηAql, S Sge and XCyg and published optical photometry we use the infrared flux method to determine effective temperatures and angular radii at all phases of the pulsation cycle. These angular radii combined with published radial velocity curves then give the radii of the stars. Knowing the radii and effective temperatures we obtain the absolute magnitudes. (author)

  4. International wind energy development. World market update 2002. Forecast 2003-2007

    International Nuclear Information System (INIS)

    2003-03-01

    This report highlights the development of the international wind power market during 2002 and the new forecast up to 2007. The data presented includes both supply side and demand side information. With 7,227 MW of new installations the total installed capacity of wind power grew to over 32,000 MW. This is the highest figure ever in a single year. The growth rate of 6% over 2001, however, was the lowest since 1996. In spite of this modest figure, the average growth rate over the past five years (from 1997) has been much higher, at 35.7%, and last year's record growth (2001 over 2000) was 52%. The key features of development during 2002 were: 7,227 MW new installed wind power; cumulative installed capacity by the end of 2002 had reached 32,037 MW, consisting of around 61,500 wind turbines dispersed over more than 40 countries; A major share of new installations took place in Europe, with 85.4% of the total. Germany accounted for 53% of the European total; America fell back form its peak level of 1,745 MW in 2001 to a modest 494 MW in 2002, with the majority installed in the USA; Development in Asia was lower than in 2001; Of the emerging markets in the Far East/Pacific, China and Australia were the only two markets to show growth over 2001; The Top Ten markets in the world are headed by Germany, Spain, Denmark and the USA. Newcomers to the Top Ten markets ranking were Australia and the Netherlands; In terms of cumulative installation, the German market passed the 10,000 MW milestone and is by far the largest market in the world. There were 12,000 MW installed in Germany by end of 2002. Spain became No. 2 with 5,042 MW; Penetration of wind power in the world's electricity supply had reached 0.4% by end of 2002. Ten of the world's roughly 25 suppliers of wind turbines are responsible for more than 90% of total supply in the global market. This trend is continuing, with the Top Ten manufacturers in 2002 delivering 95% of the total record installation. Vestas Wind

  5. 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...

  6. Options to Improve the Quality of Wind Generation Output Forecasting with the Use of Available Information as Explanatory Variables

    Directory of Open Access Journals (Sweden)

    Rafał Magulski

    2015-06-01

    Full Text Available Development of wind generation, besides its positive aspects related to the use of renewable energy, is a challenge from the point of view of power systems’ operational security and economy. The uncertain and variable nature of wind generation sources entails the need for the for the TSO to provide adequate reserves of power, necessary to maintain the grid’s stable operation, and the actors involved in the trading of energy from these sources incur additional of balancing unplanned output deviations. The paper presents the results of analyses concerning the options to forecast a selected wind farm’s output exercised by means of different methods of prediction, using a different range of measurement and forecasting data available on the farm and its surroundings. The analyses focused on the evaluation of forecast errors, and selection of input data for forecasting models and assessment of their impact on prediction quality improvement.

  7. Large-scale analysis and forecast experiments with wind data from the Seasat A scatterometer

    Science.gov (United States)

    Baker, W. E.; Atlas, R.; Kalnay, E.; Halem, M.; Woiceshyn, P. M.; Peteherych, S.; Edelmann, D.

    1984-01-01

    A series of data assimilation experiments is performed to assess the impact of Seasat A satellite scatterometer (SASS) wind data on Goddard Laboratory for Atmospheric Sciences (GLAS) model forecasts. The SASS data are dealiased as part of an objective analysis system utilizing a three-pass procedure. The impact of the SASS data is evaluated with and without temperature soundings from the NOAA 4 Vertical Temperature Profile Radiometer (VTPR) instrument in order to study possible redundancy between surface wind data and upper air temperature data. In the northern hemisphere the SASS data are generally found to have a negligible effect on the forecasts. In the southern hemisphere the forecast impact from SASS data is somewhat larger and primarily beneficial in the absence of VTPR data. However, the inclusion of VTPR data effectively eliminates the positive impact over Australia and South America. This indicates that SASS data can be beneficial for numerical weather prediction in regions with large data gaps, but in the presence of satellite soundings the usefulness of SASS data is significantly reduced.

  8. Methodology and forecast products for the optimal offering of ancillary services from wind in a market environment

    DEFF Research Database (Denmark)

    Bacher, Peder; Madsen, Henrik; Pinson, Pierre

    of wind power for example in the planning of ancillary power services, where the level of available wind power with a high degree of certainty is important to know. The presented extreme value models are applied to negative forecast residuals from state-of-the-art wind power forecast software...... of extreme weather induced phenomena, for example extreme water levels in a river, wind levels or at sea for design of dykes (de Haan and de Ronde, 1998). In insurance and finance the extreme value modelling is widespread (Embrechts et al., 1997). Extreme value statistics for energy and power applications...... is also widely used, for example for planning in wind power operation (Horvat et al., 2013) and peak wind prediction (Cook, 1982) and (Friederichs and Thorarinsdottir, 2012). Several books provide comprehensive introductions to extreme value theory, for example Coles (2001) and Beirlant et al. (2006...

  9. 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.

  10. 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.

  11. Statistical Short-Range Guidance for Peak Wind Speed Forecasts on Kennedy Space Center/Cape Canaveral Air Force Station: Phase I Results

    Science.gov (United States)

    Lambert, Winifred C.; Merceret, Francis J. (Technical Monitor)

    2002-01-01

    This report describes the results of the ANU's (Applied Meteorology Unit) Short-Range Statistical Forecasting task for peak winds. The peak wind speeds are an important forecast element for the Space Shuttle and Expendable Launch Vehicle programs. The Keith Weather Squadron and the Spaceflight Meteorology Group indicate that peak winds are challenging to forecast. The Applied Meteorology Unit was tasked to develop tools that aid in short-range forecasts of peak winds at tower sites of operational interest. A 7 year record of wind tower data was used in the analysis. Hourly and directional climatologies by tower and month were developed to determine the seasonal behavior of the average and peak winds. In all climatologies, the average and peak wind speeds were highly variable in time. This indicated that the development of a peak wind forecasting tool would be difficult. Probability density functions (PDF) of peak wind speed were calculated to determine the distribution of peak speed with average speed. These provide forecasters with a means of determining the probability of meeting or exceeding a certain peak wind given an observed or forecast average speed. The climatologies and PDFs provide tools with which to make peak wind forecasts that are critical to safe operations.

  12. International wind energy development. World marked update 1999. Forecast 2000-2004

    International Nuclear Information System (INIS)

    2000-03-01

    This is the fifth issue of the annual World Market Update by BTM Consult ApS, covering the year 1999. All figures in the status refer to the end of year 1999. It is the last update from the 20th century, in which wind energy developed during the last two decades to become a very serious part of the world electricity supply. As in previous reports, the past 3 years' development in the wind energy sector is assessed, and the forecast looks 5 years ahead. Wind power is the world's fastest growing energy source, with an average annual growth rate of 40 % over the last five years. Wind energy is a clean and abundant energy source, and it is becomming a preferred source of energy not only due to the environmental benefits, but also because it has become increasingly cost competitive in the world energy markets. One of the most significant figures and trends from this fast growing market during 1999 was that the annual installation of new wind power capacity increased by 51 %, resulting in a cumulative installation by the end of 1999 of 13,932 MW. The growth rates in the wind industry can easily be compared to the growth rates in the IT sector, although the growth differ much from country to country. The high growth rates are still very much influenced by political and economical issues, but the continuously improved technology and thus also the redused cost of energy becomes more and more significant, and there are hardly any arguments left why wind energy should not play a very significant role in the electricity supply. Approximately 81 % of the new capacity of 3,922 were installed in Europe, emphasizing that this region is still the major market place. The US market picked up close to the PTC expiry date (Production Tax Credit) on June 30, 1999. In terms of single markets it was, however, the German market which once again took the lead with installed capacity of 1,568 MW. Germany thereby consolidated the position as the leading wind energy country in the world. Spain

  13. An advanced strategy for wind speed forecasting using expert 2-D FIR filters

    Directory of Open Access Journals (Sweden)

    MOGHADDAM, A. A.

    2010-11-01

    Full Text Available Renewable energies such as wind and solar have become the most attractive means of electricity generation nowadays. Social and environmental benefits as well as economical issues result in further utilization of such these energy resources. In this regard, wind energy plays an important roll in operation of small-scale power systems like Micro Grid. On the other hand, wind stochastic nature in different time and place horizons, makes accurate forecasting of its behavior an inevitable task for market planners and energy management systems. In this paper an advanced strategy for wind speed estimation has been purposed and its superior performance is compared to that of conventional methods. The model is based on linear predictive filtering and image processing principles using 2-D FIR filters. To show the efficiency of purposed predictive model different FIR filters are designed and tested through similar data. Wind speed data have been collected during the period January 1, 2009 to December 31, 2009 from Casella automatic weather station at Plymouth. It is observed that 2-D FIR filters act more accurately in comparison with 1-D conventional representations; however, their prediction ability varies considerably through different filter sizing.

  14. 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.

  15. DEWEPS - Development and Evaluation of new Wind forecasting tools with an Ensemble Prediction System

    Energy Technology Data Exchange (ETDEWEB)

    Moehrlen, C.; Joergensen, Jess

    2012-02-15

    There is an ongoing trend of increased privatization in the handling of renewable energy. This trend is required to ensure an efficient energy system, where improvements that make economic sense are prioritised. The reason why centralized forecasting can be a challenge in that matter is that the TSOs tend to optimize on physical error rather than cost. Consequently, the market is likely to speculate against the TSO, which in turn increases the cost of balancing. A privatized pool of wind and/or solar power is more difficult to speculate against, because the optimization criteria is unpredictable due to subjective risk considerations that may be taken into account at any time. Although there is and additional level of costs for the trading of the private volume, it can be argued that competition will accelerate efficiency from an economic perspective. The amount of power put into the market will become less predictable, when the wind power spot market bid takes place on the basis of a risk consideration in addition to the forecast information itself. The scope of this project is to contribute to more efficient wind power integration targeted both to centralised and decentralised cost efficient IT solutions, which will complement each other in market based energy systems. The DEWEPS project resulted in an extension of the number of Ensemble forecasts, an incremental trade strategy for balancing unpredictable power production, and an IT platform for efficient handling of power generation units. Together, these three elements contribute to less need for reserves, more capacity in the market, and thus more competition. (LN)

  16. 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.

  17. 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.

  18. Dynamic sizing of energy storage for hedging wind power forecast uncertainty

    DEFF Research Database (Denmark)

    Pinson, Pierre; Papefthymiou, George; Klöckl, Bernd

    2009-01-01

    . This approach leads to a dynamic assessment of the energy storage capacity for different delivery periods. In such a context, energy storage is used as a means of risk hedging against penalties from the regulation market. The application of the algorithm on real data (both measurements and forecasts......In market conditions where program responsible parties are penalized for deviations from proposed bids, energy storage can be used for compensating the energy imbalances induced by limited predictability of wind power. The energy storage capacity necessary for performing this task will differ......) of the yearly output of a wind farm shows that the application of a dynamic daily sizing of the necessary storage leads to a significant reduction of the storage capacity used, without affecting the producer's profit significantly. The method proposed here may provide the basis for the introduction of storage...

  19. Probabilistic Solar Wind Forecasting Using Large Ensembles of Near-Sun Conditions With a Simple One-Dimensional "Upwind" Scheme.

    Science.gov (United States)

    Owens, Mathew J; Riley, Pete

    2017-11-01

    Long lead-time space-weather forecasting requires accurate prediction of the near-Earth solar wind. The current state of the art uses a coronal model to extrapolate the observed photospheric magnetic field to the upper corona, where it is related to solar wind speed through empirical relations. These near-Sun solar wind and magnetic field conditions provide the inner boundary condition to three-dimensional numerical magnetohydrodynamic (MHD) models of the heliosphere out to 1 AU. This physics-based approach can capture dynamic processes within the solar wind, which affect the resulting conditions in near-Earth space. However, this deterministic approach lacks a quantification of forecast uncertainty. Here we describe a complementary method to exploit the near-Sun solar wind information produced by coronal models and provide a quantitative estimate of forecast uncertainty. By sampling the near-Sun solar wind speed at a range of latitudes about the sub-Earth point, we produce a large ensemble (N = 576) of time series at the base of the Sun-Earth line. Propagating these conditions to Earth by a three-dimensional MHD model would be computationally prohibitive; thus, a computationally efficient one-dimensional "upwind" scheme is used. The variance in the resulting near-Earth solar wind speed ensemble is shown to provide an accurate measure of the forecast uncertainty. Applying this technique over 1996-2016, the upwind ensemble is found to provide a more "actionable" forecast than a single deterministic forecast; potential economic value is increased for all operational scenarios, but particularly when false alarms are important (i.e., where the cost of taking mitigating action is relatively large).

  20. Probabilistic Solar Wind Forecasting Using Large Ensembles of Near-Sun Conditions With a Simple One-Dimensional "Upwind" Scheme

    Science.gov (United States)

    Owens, Mathew J.; Riley, Pete

    2017-11-01

    Long lead-time space-weather forecasting requires accurate prediction of the near-Earth solar wind. The current state of the art uses a coronal model to extrapolate the observed photospheric magnetic field to the upper corona, where it is related to solar wind speed through empirical relations. These near-Sun solar wind and magnetic field conditions provide the inner boundary condition to three-dimensional numerical magnetohydrodynamic (MHD) models of the heliosphere out to 1 AU. This physics-based approach can capture dynamic processes within the solar wind, which affect the resulting conditions in near-Earth space. However, this deterministic approach lacks a quantification of forecast uncertainty. Here we describe a complementary method to exploit the near-Sun solar wind information produced by coronal models and provide a quantitative estimate of forecast uncertainty. By sampling the near-Sun solar wind speed at a range of latitudes about the sub-Earth point, we produce a large ensemble (N = 576) of time series at the base of the Sun-Earth line. Propagating these conditions to Earth by a three-dimensional MHD model would be computationally prohibitive; thus, a computationally efficient one-dimensional "upwind" scheme is used. The variance in the resulting near-Earth solar wind speed ensemble is shown to provide an accurate measure of the forecast uncertainty. Applying this technique over 1996-2016, the upwind ensemble is found to provide a more "actionable" forecast than a single deterministic forecast; potential economic value is increased for all operational scenarios, but particularly when false alarms are important (i.e., where the cost of taking mitigating action is relatively large).

  1. International wind energy development. World market update 2009. Forecast 2010-2014

    Energy Technology Data Exchange (ETDEWEB)

    2010-03-15

    This is the fifteenth edition of the annual World Market Update produced by BTM Consult ApS, and covers developments in the wind energy sector during 2009. As in previous editions, the report also assesses important changes over the last three years and forecasts progress for five years ahead. The special topic in this year's WMU is an evaluation of the aftermath of the COP-15 climate change negotiations in relation to future wind power development. The global market for wind power not only produced a record for new installations in 2009 of 38 GW installed capacity, it also created a new order in the balance of international wind power. The rapid increase in the rate of installations in both Asia and the US was already clear in 2008; that trend has continued at a faster pace in 2009. By far the largest number of new wind projects were seen in the US and China. Another new reality is that most of the world's manufacturing of wind turbines now takes place in China. As a result three Chinese companies are among the world's top ten turbine manufacturers. At the same time a rapid expansion of manufacturing capacity by European turbine makers has taken place in the US. Europe contributed 28.2% of the newly added capacity - 10,738 MW - taking the continent's total wind power generation capacity to 76,553 MW. The growth in Asia's markets has once again been staggering. With 14,991 MW of new installations, South and East Asia accounted for 39.4% of the global total in 2009. China was the major contributor, with 13,750 MW of new capacity, more than double that installed in 2008. In terms of cumulative installed wind power, the US is still the world leader, with 35,159 MW. China overtook Germany with a margin of less than 50 MW. China now has a total of 25,853 MW, followed by Germany's 25,813 MW. A new world order in wind power has become a reality. The forecast released in this WMU shows an average growth rate of 13.5% for the period 2010

  2. 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.

  3. International wind energy development. World market update 2010. Forecast 2011-2015

    Energy Technology Data Exchange (ETDEWEB)

    2011-03-15

    This is the sixteenth edition of the annual World Market Update produced by BTM Consult ApS - a part of Navigant Consulting, and covers developments in the wind energy sector during 2010. As in previous editions, the report also assesses important changes over the last three years and forecasts progress for five years ahead. The special topic in this year's WMU is a review of Direct-Drive concept versus traditional Drive Train with gearbox. The global market for wind power produced a record for new installations in 2010 of 39.4 GW installed capacity, however, with a much lower growth rate than in the period 2005 to 2009. The rapid increase in the rate of installations in both Asia and the US was already clear in 2008-09. That trend has continued in China but the US experienced a significant slow-down in 2010. Europe stayed relatively stable - old markets stagnated but new emerging markets grew. Another new reality is that most of the world's manufacturing of wind turbines now takes place in China. Companies producing wind turbines there have experienced an explosive rate of growth. As a result four Chinese companies are among the world's Top Ten turbine manufacturers. An inevitable impact of this shift is that the market shares of the traditional industry leaders from the US and Europe have decreased significantly with Vestas and Siemens as exception in 2010. At the same time a rapid expansion of manufacturing capacity by European turbine makers has taken place in the US. Europe contributed 29.9% of the newly added capacity - 10,920 MW - taking the continent's total wind power generation capacity to 87,565 MW. The growth in Asia's markets has once again been staggering. With 21,130 MW of new installations, South and East Asia accounted for 53.6% of the global total in 2010.China was the major contributor, with 18,928 MW of new capacity, 37% over that of 2009. In terms of cumulative installed wind power, China surpassed the US in 2010, with

  4. 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

    and forecasting, the original SC-VAR is modified and a Correlation-Constrained SC-VAR (CCSC-VAR) is proposed based on spatial correlation information about wind farms. Our approach is evaluated based on a case study of very-short-term forecasting for 25 wind farms in Denmark. Comparison is performed with a set...... of traditional local methods and spatio-temporal methods. The results obtained show the proposed CCSC-VAR has better overall performance than both the original SC-VAR and other benchmark methods, taking into account all evaluation indicators, including sparsitycontrol ability, sparsity, accuracy and efficiency...

  5. 915-MHz Wind Profiler for Cloud Forecasting at Brookhaven National Laboratory

    Energy Technology Data Exchange (ETDEWEB)

    Jensen, M. [Brookhaven National Lab. (BNL), Upton, NY (United States); Bartholomew, M. J. [Brookhaven National Lab. (BNL), Upton, NY (United States); Giangrande, S. [Brookhaven National Lab. (BNL), Upton, NY (United States)

    2016-03-01

    When considering the amount of shortwave radiation incident on a photovoltaic solar array and, therefore, the amount and stability of the energy output from the system, clouds represent the greatest source of short-term (i.e., scale of minutes to hours) variability through scattering and reflection of incoming solar radiation. Providing estimates of this short-term variability is important for determining and regulating the output from large solar arrays as they connect with the larger power infrastructure. In support of the installation of a 37-MW solar array on the grounds of Brookhaven National Laboratory (BNL), a study of the impacts of clouds on the output of the solar array has been undertaken. The study emphasis is on predicting the change in surface solar radiation resulting from the observed/forecast cloud field on a 5-minute time scale. At these time scales, advection of cloud elements over the solar array is of particular importance. As part of the BNL Aerosol Life Cycle Intensive Operational Period (IOP), a 915-MHz Radar Wind Profiler (RWP) was deployed to determine the profile of low-level horizontal winds and the depth of the planetary boundary layer. The initial deployment mission of the 915-MHz RWP for cloud forecasting has been expanded the deployment to provide horizontal wind measurements for estimating and constraining cloud advection speeds. A secondary focus is on the observation of dynamics and microphysics of precipitation during cold season/winter storms on Long Island. In total, the profiler was deployed at BNL for 1 year from May 2011 through May 2012.

  6. 915-Mhz Wind Profiler for Cloud Forecasting at Brookhaven National Laboratory

    Energy Technology Data Exchange (ETDEWEB)

    Jensen, M. [Brookhaven National Laboratory (BNL), Upton, NY (United States); Bartholomew, M. J. [Brookhaven National Laboratory (BNL), Upton, NY (United States); Giangrande, S. [Brookhaven National Laboratory (BNL), Upton, NY (United States)

    2016-03-01

    When considering the amount of shortwave radiation incident on a photovoltaic solar array and, therefore, the amount and stability of the energy output from the system, clouds represent the greatest source of short-term (i.e., scale of minutes to hours) variability through scattering and reflection of incoming solar radiation. Providing estimates of this short-term variability is important for determining and regulating the output from large solar arrays as they connect with the larger power infrastructure. In support of the installation of a 37-MW solar array on the grounds of Brookhaven National Laboratory (BNL), a study of the impacts of clouds on the output of the solar array has been undertaken. The study emphasis is on predicting the change in surface solar radiation resulting from the observed/forecast cloud field on a 5-minute time scale. At these time scales, advection of cloud elements over the solar array is of particular importance. As part of the BNL Aerosol Life Cycle Intensive Operational Period (IOP), a 915-MHz Radar Wind Profiler (RWP) was deployed to determine the profile of low-level horizontal winds and the depth of the planetary boundary layer. The initial deployment mission of the 915-MHz RWP for cloud forecasting has been expanded the deployment to provide horizontal wind measurements for estimating and constraining cloud advection speeds. A secondary focus is on the observation of dynamics and microphysics of precipitation during cold season/winter storms on Long Island. In total, the profiler was deployed at BNL for 1 year from May 2011 through May 2012.

  7. Application of a regional hurricane wind risk forecasting model for wood-frame houses.

    Science.gov (United States)

    Jain, Vineet Kumar; Davidson, Rachel Ann

    2007-02-01

    Hurricane wind risk in a region changes over time due to changes in the number, type, locations, vulnerability, and value of buildings. A model was developed to quantitatively estimate changes over time in hurricane wind risk to wood-frame houses (defined in terms of potential for direct economic loss), and to estimate how different factors, such as building code changes and population growth, contribute to that change. The model, which is implemented in a simulation, produces a probability distribution of direct economic losses for each census tract in the study region at each time step in the specified time horizon. By changing parameter values and rerunning the analysis, the effects of different changes in the built environment on the hurricane risk trends can be estimated and the relative effectiveness of hypothetical mitigation strategies can be evaluated. Using a case study application for wood-frame houses in selected counties in North Carolina from 2000 to 2020, this article demonstrates how the hurricane wind risk forecasting model can be used: (1) to provide insight into the dynamics of regional hurricane wind risk-the total change in risk over time and the relative contribution of different factors to that change, and (2) to support mitigation planning. Insights from the case study include, for example, that the many factors contributing to hurricane wind risk for wood-frame houses interact in a way that is difficult to predict a priori, and that in the case study, the reduction in hurricane losses due to vulnerability changes (e.g., building code changes) is approximately equal to the increase in losses due to building inventory growth. The potential for the model to support risk communication is also discussed.

  8. Short-Term Forecasting of Inertial Response from a Wind Power Plant: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Muljadi, Eduard; Gevorgian, Vahan; Hoke, Andy

    2016-09-01

    The total inertia stored in all rotating masses (synchronous generators, induction motors, etc.) connected to a power system grid is an essential force that keeps the system stable after disturbances. Power systems have been experiencing reduced inertia during the past few decades [1]. This trend will continue as the level of renewable generation (e.g., wind and solar) increases. Wind power plants (WPPs) and other renewable power plants with power electronic interfaces are capable of delivering frequency response (both droop and/or inertial response) by a control action; thus, the reduction in available online inertia can be compensated by designing the plant control to include frequency response. The source of energy to be delivered as inertial response is determined by the type of generation (wind, photovoltaic, concentrating solar power, etc.) and the control strategy chosen. The importance of providing ancillary services to ensure frequency control within a power system is evidenced from many recent publications with different perspectives (manufacturer, system operator, regulator, etc.) [2]-[6]. This paper is intended to provide operators with a method for the real-time assessment of the available inertia of a WPP. This is critical to managing power system stability and the reserve margin. In many states, modern WPPs are required to provide ancillary services (e.g., frequency regulation via governor response and inertial response) to the grid. This paper describes the method of estimating the available inertia and the profile of the forecasted response from a WPP.

  9. Testing the data assimilation technique for short-term wind forecast in the PBL: a case study

    Science.gov (United States)

    Avolio, E.; Federico, S.; Sempreviva, A. M.; Calidonna, C. R.; Courtney, M.

    2012-04-01

    In this contribution we show the results of using a data assimilation technique to improve the short-term wind forecast at a site in northern Europe. The assimilation technique is a simple four-dimensional nudging and, for this purpose, we set-up a version of the Regional Atmospheric Modelling System. The nudging technique consists of adding an extra-tendency term, to the prognostic equations of the zonal and meridional wind components, which forces the variable toward the observations. dφm- (φobs -φm-) dt = τ f(r) (1) where φmis model variable (zonal or meridional wind component), φobs is the observation, τ is relaxation time scale (900 s), f(r) is a Gaussian function f(r) = e0-(r/r)Λ2 , and r0=50 km. The method was applied in Denmark where suitable observations were available at the Danish National Test Station for Large Wind Turbines, located at Høvsøre (Western Jutland, Denmark), and refer to the measurements of vertical wind profiles; the instrument is the WINDCUBE Doppler LIDAR. Data were available every 10 minutes at the following levels: 40 m, 60 m, 80 m, 100 m, 116 m, 130 m, 160 m, 200 m, 250 m and 300 m. The data represent the average of the measurement for the previous 10 minutes. Only data available at the 00 minutes of each hour were considered in this study. The RAMS model is set-up with four nested grids. The fourth grid has 1 km horizontal resolution and is centred over the site. Model levels do not coincide with the measurement levels, and, to assimilate and to verify the forecast, the observations were linearly interpolated to the model levels. The physical configuration of the model is the one adopted for operational forecast over the Calabria Region in South Italy. In order to show the potential impact of the nudging technique, we run the model in two different configurations: (a) a simple forecast and (b) an analysis-forecast run. The runs duration is twenty-four hours for both configurations. For each configuration, simulations were

  10. 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.

  11. Statistical Short-Range Guidance for Peak Wind Forecasts on Kennedy Space Center/Cape Canaveral Air Force Station, Phase III

    Science.gov (United States)

    Crawford, Winifred

    2010-01-01

    This final report describes the development of a peak wind forecast tool to assist forecasters in determining the probability of violating launch commit criteria (LCC) at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). The peak winds are an important forecast element for both the Space Shuttle and Expendable Launch Vehicle (ELV) programs. The LCC define specific peak wind thresholds for each launch operation that cannot be exceeded in order to ensure the safety of the vehicle. The 45th Weather Squadron (45 WS) has found that peak winds are a challenging parameter to forecast, particularly in the cool season months of October through April. Based on the importance of forecasting peak winds, the 45 WS tasked the Applied Meteorology Unit (AMU) to develop a short-range peak-wind forecast tool to assist in forecasting LCC violations.The tool includes climatologies of the 5-minute mean and peak winds by month, hour, and direction, and probability distributions of the peak winds as a function of the 5-minute mean wind speeds.

  12. The Structure of Vertical Wind Shear in Tropical Cyclone Environments: Implications for Forecasting and Predictability

    Science.gov (United States)

    Finocchio, Peter M.

    The vertical wind shear measured between 200 and 850 hPa is commonly used to diagnose environmental interactions with a tropical cyclone (TC) and to forecast the storm's intensity and structural evolution. More often than not, stronger vertical shear within this deep layer prohibits the intensification of TCs and leads to predictable asymmetries in precipitation. But such bulk measures of vertical wind shear can occasionally mislead the forecaster. In the first part of this dissertation, we use a series of idealized numerical simulations to examine how a TC responds to changing the structure of unidirectional vertical wind shear while fixing the 200-850-hPa shear magnitude. These simulations demonstrate a significant intensity response, in which shear concentrated in shallow layers of the lower troposphere prevents vortex intensification. We attribute the arrested development of TCs in lower-level shear to the intrusion of mid-level environmental air over the surface vortex early in the simulations. Convection developing on the downshear side of the storm interacts with the intruding air so as to enhance the downward flux of low-entropy air into the boundary layer. We also construct a two-dimensional intensity response surface from a set of simulations that sparsely sample the joint shear height-depth parameter space. This surface reveals regions of the two-parameter space for which TC intensity is particularly sensitive. We interpret these parameter ranges as those which lead to reduced intensity predictability. Despite the robust response to changing the shape of a sheared wind profile in idealized simulations, we do not encounter such sensitivity within a large set of reanalyzed TCs in the Northern Hemisphere. Instead, there is remarkable consistency in the structure of reanalyzed wind profiles around TCs. This is evident in the distributions of two new parameters describing the height and depth of vertical wind shear, which highlight a clear preference for

  13. Application of Ensemble Sensitivity Analysis to Observation Targeting for Short-term Wind Speed Forecasting in the Washington-Oregon Region

    Energy Technology Data Exchange (ETDEWEB)

    Zack, John [AWS Truewind, LLC, Albany, NY (United States); Natenberg, Eddie [AWS Truewind, LLC, Albany, NY (United States); Young, Steve [AWS Truewind, LLC, Albany, NY (United States); Knowe, Glenn Van [AWS Truewind, LLC, Albany, NY (United States); Waight, Ken [AWS Truewind, LLC, Albany, NY (United States); Manobianco, John [AWS Truewind, LLC, Albany, NY (United States); Kamath, Chandrika [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

    2010-10-01

    To economically and reliably balance electrical load and generation, electrical grid operators, also called Balancing Authorities (BA), need highly accurate electrical power generation forecasts in time frames ranging from a few minutes to six hours ahead. As wind power generation increases, there is a requirement to improve the accuracy of 0- to 6-hour ahead wind power forecasts. Forecasts covering this short look-ahead period have depended heavily on short-term trends obtained from the actual power production and meteorological data of a wind generation facility. Additional data are often available from Numerical Weather Prediction (NWP) models and sometimes from off-site meteorological towers near wind generation facilities.

  14. A Novel Combined Model Based on an Artificial Intelligence Algorithm—A Case Study on Wind Speed Forecasting in Penglai, China

    Directory of Open Access Journals (Sweden)

    Feiyu Zhang

    2016-06-01

    Full Text Available Wind speed forecasting plays a key role in wind-related engineering studies and is important in the management of wind farms. Current forecasting models based on different optimization algorithms can be adapted to various wind speed time series data. However, these methodologies cannot aggregate different hybrid forecasting methods and take advantage of the component models. To avoid these limitations, we propose a novel combined forecasting model called SSA-PSO-DWCM, i.e., particle swarm optimization (PSO determined weight coefficients model. This model consisted of three main steps: one is the decomposition of the original wind speed signals to discard the noise, the second is the parameter optimization of the forecasting method, and the last is the combination of different models in a nonlinear way. The proposed combined model is examined by forecasting the wind speed (10-min intervals of wind turbine 5 located in the Penglai region of China. The simulations reveal that the proposed combined model demonstrates a more reliable forecast than the component forecasting engines and the traditional combined method, which is based on a linear method.

  15. Wind Power Forecasting Using Multi-Objective Evolutionary Algorithms for Wavelet Neural Network-Optimized Prediction Intervals

    Directory of Open Access Journals (Sweden)

    Yanxia Shen

    2018-01-01

    Full Text Available The intermittency of renewable energy will increase the uncertainty of the power system, so it is necessary to predict the short-term wind power, after which the electrical power system can operate reliably and safely. Unlike the traditional point forecasting, the purpose of this study is to quantify the potential uncertainties of wind power and to construct prediction intervals (PIs and prediction models using wavelet neural network (WNN. Lower upper bound estimation (LUBE of the PIs is achieved by minimizing a multi-objective function covering both interval width and coverage probabilities. Considering the influence of the points out of the PIs to shorten the width of PIs without compromising coverage probability, a new, improved, multi-objective artificial bee colony (MOABC algorithm combining multi-objective evolutionary knowledge, called EKMOABC, is proposed for the optimization of the forecasting model. In this paper, some comparative simulations are carried out and the results show that the proposed model and algorithm can achieve higher quality PIs for wind power forecasting. Taking into account the intermittency of renewable energy, such a type of wind power forecast can actually provide a more reliable reference for dispatching of the power system.

  16. The use of different ensemble forecasting systems for wind power prediction on a real case in the South of Italy

    DEFF Research Database (Denmark)

    Alessandrini, Stefano; Sperati, Simone; Pinson, Pierre

    Short-term forecasting applied to wind energy is becoming increasingly important due to the constant growth of this renewable source, whose uncertainty requires a constant effort to meet the needs of the national electrical systems and their operators. Regarding to this, the probabilistic approac...

  17. Application of the nonlinear time series prediction method of genetic algorithm for forecasting surface wind of point station in the South China Sea with scatterometer observations

    International Nuclear Information System (INIS)

    Zhong Jian; Dong Gang; Sun Yimei; Zhang Zhaoyang; Wu Yuqin

    2016-01-01

    The present work reports the development of nonlinear time series prediction method of genetic algorithm (GA) with singular spectrum analysis (SSA) for forecasting the surface wind of a point station in the South China Sea (SCS) with scatterometer observations. Before the nonlinear technique GA is used for forecasting the time series of surface wind, the SSA is applied to reduce the noise. The surface wind speed and surface wind components from scatterometer observations at three locations in the SCS have been used to develop and test the technique. The predictions have been compared with persistence forecasts in terms of root mean square error. The predicted surface wind with GA and SSA made up to four days (longer for some point station) in advance have been found to be significantly superior to those made by persistence model. This method can serve as a cost-effective alternate prediction technique for forecasting surface wind of a point station in the SCS basin. (paper)

  18. Implementation and validation of a coastal forecasting system for wind waves in the Mediterranean Sea

    Directory of Open Access Journals (Sweden)

    R. Inghilesi

    2012-02-01

    Full Text Available A coastal forecasting system was implemented to provide wind wave forecasts over the whole Mediterranean Sea area, and with the added capability to focus on selected coastal areas. The goal of the system was to achieve a representation of the small-scale coastal processes influencing the propagation of waves towards the coasts. The system was based on a chain of nested wave models and adopted the WAve Model (WAM to analyse the large-scale, deep-sea propagation of waves; and the Simulating WAves Nearshore (SWAN to simulate waves in key coastal areas. Regional intermediate-scale WAM grids were introduced to bridge the gap between the large-scale and each coastal area. Even applying two consecutive nestings (Mediterranean grid → regional grid → coastal grid, a very high resolution was still required for the large scale WAM implementation in order to get a final resolution of about 400 m on the shores. In this study three regional areas in the Tyrrhenian Sea were selected, with a single coastal area embedded in each of them. The number of regional and coastal grids in the system could easily be modified without significantly affecting the efficiency of the system. The coastal system was tested in three Italian coastal regions in order to optimize the numerical parameters and to check the results in orographically complex zones for which wave records were available. Fifteen storm events in the period 2004–2009 were considered.

  19. International wind energy development. World market update 2006. Forecast 2007-2011

    International Nuclear Information System (INIS)

    2007-03-01

    The report covers development in the international wind power market during 2006 and the new Forecast until 2011. Furthermore a long term Prediction is made up to 2016. With 15,016 MW of new installations, the total installed capacity of wind power grew to around 74,300 MW. This was an increase in cumulative installation of 25%. Looking at the annual installation of 15,016 MW there was an increase of 30%. This is on top of a 2005 growth of 42%. The key figures for development during 2006 were: a) 15,016 MW of newly installed wind power capacity. b)Cumulative installed capacity by the end of 2006 reached 74,306 MW, consisting of around 10,600 wind turbines dispersed in 36 countries. c) Europe maintained its role as the largest wind power continent. 51% of all new installation in 2006 took place in Europe. d) The Americas had a record year thanks to the development in the US, where 2,454 MW of new capacity was added. The reason is the PTC (Production Tax Credit) in the US market in force again and will be so until end of 2008. The Americas accounted for 23.4% of the world's installation in 2006. e) Asia showed significant growth. Including OECD Pacific, Asia doubled its installation, from 7,890 MW in 2005 to 11.601 MW by the end of 2006. India was by far the leading country, with 1,840 MW of new capacity in 2006. China also showed strong progress, with almost 1,334 MW of new installation. The region as a whole accounted for 24.7% of the year's world wide total. f) Among the Top Ten markets USA maintained its position as largest market in 2006. Germany, the world's largest market for a decade, increased its installation from 2005 to installing 2.233 MW, after three year on decline. It is, however, enough to maintain their position as no. 2 market in the world. France and Portugal showed remarkable growth. Spain is still No.2 market in Europe, with 1,587 MW of new installation. g) Penetration of wind power in the world's electricity supply reached 0.82% by the end of

  20. 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...... Prediction System (EPS) can be used as indicator of a three-hourly, three days ahead, wind power forecast’s accuracy. In particular it has been noticed that to extract usable information from data the Ensemble members needed to be statistically calibrated, since the rank histograms for the three-day period...... that a reliable estimation of their uncertainty could be a useful information too. In fact the prediction accuracy is unfortunately not constant and can depend on the location of a particular wind farm, on the forecast time and on the atmospheric situation. Previous studies indicated that the ECMWF Ensemble...

  1. Electrical Energy Forecasting and Optimal Allocation of ESS in a Hybrid Wind-Diesel Power System

    Directory of Open Access Journals (Sweden)

    Hai Lan

    2017-02-01

    Full Text Available Due to the increasingly serious energy crisis and environmental pollution problem, traditional fossil energy is gradually being replaced by renewable energy in recent years. However, the introduction of renewable energy into power systems will lead to large voltage fluctuations and high capital costs. To solve these problems, an energy storage system (ESS is employed into a power system to reduce total costs and greenhouse gas emissions. Hence, this paper proposes a two-stage method based on a back-propagation neural network (BPNN and hybrid multi-objective particle swarm optimization (HMOPSO to determine the optimal placements and sizes of ESSs in a transmission system. Owing to the uncertainties of renewable energy, a BPNN is utilized to forecast the outputs of the wind power and load demand based on historic data in the city of Madison, USA. Furthermore, power-voltage (P-V sensitivity analysis is conducted in this paper to improve the converge speed of the proposed algorithm, and continuous wind distribution is discretized by a three-point estimation method. The Institute of Electrical and Electronic Engineers (IEEE 30-bus system is adopted to perform case studies. The simulation results of each case clearly demonstrate the necessity for optimal storage allocation and the efficiency of the proposed method.

  2. Wind Turbine Waste Heat Recovery—A Short-Term Heat Loss Forecasting Approach

    Directory of Open Access Journals (Sweden)

    George Xydis

    2015-07-01

    Full Text Available The transition from the era of massive renewable energy deployment to the era of cheaper energy needed has made scientists and developers more careful with respect to energy planning compared with a few years ago. The focus is—and will be—placed on retrofitting and on extracting the maximum amount of locally generated energy. The question is not only how much energy can be generated, but also what kind of energy and how it can be utilized efficiently. The waste heat coming from wind farms (WFs when in operation—which until now was wasted—was thoroughly studied. A short-term forecasting methodology that can provide the operator with a better view of the expected heat losses is presented. The majority of mechanical (due to friction and electro-thermal (i.e., generator losses takes place at the nacelle while a smaller part of this thermal source is located near the foundation of the wind turbine (WT where the power electronics and the transformers are usually located. That thermal load can be easily collected via a working fluid and then be transported to the nearest local community or nearby agricultural or small scale industrial units using the necessary piping.

  3. The measurement of winds over the ocean from Skylab with application to measuring and forecasting typhoons and hurricanes

    Science.gov (United States)

    Cardone, V. J.; Pierson, W. J.

    1975-01-01

    On Skylab, a combination microwave radar-radiometer (S193) made measurements in a tropical hurricane (AVA), a tropical storm, and various extratropical wind systems. The winds at each cell scanned by the instrument were determined by objective numerical analysis techniques. The measured radar backscatter is compared to the analyzed winds and shown to provide an accurate method for measuring winds from space. An operational version of the instrument on an orbiting satellite will be able to provide the kind of measurements in tropical cyclones available today only by expensive and dangerous aircraft reconnaissance. Additionally, the specifications of the wind field in the tropical boundary layer should contribute to improved accuracy of tropical cyclone forecasts made with numerical weather predictions models currently being applied to the tropical atmosphere.

  4. Hour-ahead wind power and speed forecasting using simultaneous perturbation stochastic approximation (SPSA) algorithm and neural network with fuzzy inputs

    International Nuclear Information System (INIS)

    Hong, Ying-Yi; Chang, Huei-Lin; Chiu, Ching-Sheng

    2010-01-01

    Wind energy is currently one of the types of renewable energy with a large generation capacity. However, since the operation of wind power generation is challenging due to its intermittent characteristics, forecasting wind power generation efficiently is essential for economic operation. This paper proposes a new method of wind power and speed forecasting using a multi-layer feed-forward neural network (MFNN) to develop forecasting in time-scales that can vary from a few minutes to an hour. Inputs for the MFNN are modeled by fuzzy numbers because the measurement facilities provide maximum, average and minimum values. Then simultaneous perturbation stochastic approximation (SPSA) algorithm is employed to train the MFNN. Real wind power generation and wind speed data measured at a wind farm are used for simulation. Comparative studies between the proposed method and traditional methods are shown.

  5. Climatology and trend of wind power resources in China and its surrounding regions: a revisit using Climate Forecast System Reanalysis data

    Science.gov (United States)

    Lejiang Yu; Shiyuan Zhong; Xindi Bian; Warren E. Heilman

    2015-01-01

    The mean climatology, seasonal and interannual variability and trend of wind speeds at the hub height (80 m) of modern wind turbines over China and its surrounding regions are revisited using 33-year (1979–2011) wind data from the Climate Forecast System Reanalysis (CFSR) that has many improvements including higher spatial resolution over previous global reanalysis...

  6. ANEMOS: Development of a next generation wind power forecasting system for the large-scale integration of onshore and offshore wind farms.

    Science.gov (United States)

    Kariniotakis, G.; Anemos Team

    2003-04-01

    Objectives: Accurate forecasting of the wind energy production up to two days ahead is recognized as a major contribution for reliable large-scale wind power integration. Especially, in a liberalized electricity market, prediction tools enhance the position of wind energy compared to other forms of dispatchable generation. ANEMOS, is a new 3.5 years R&D project supported by the European Commission, that resembles research organizations and end-users with an important experience on the domain. The project aims to develop advanced forecasting models that will substantially outperform current methods. Emphasis is given to situations like complex terrain, extreme weather conditions, as well as to offshore prediction for which no specific tools currently exist. The prediction models will be implemented in a software platform and installed for online operation at onshore and offshore wind farms by the end-users participating in the project. Approach: The paper presents the methodology of the project. Initially, the prediction requirements are identified according to the profiles of the end-users. The project develops prediction models based on both a physical and an alternative statistical approach. Research on physical models gives emphasis to techniques for use in complex terrain and the development of prediction tools based on CFD techniques, advanced model output statistics or high-resolution meteorological information. Statistical models (i.e. based on artificial intelligence) are developed for downscaling, power curve representation, upscaling for prediction at regional or national level, etc. A benchmarking process is set-up to evaluate the performance of the developed models and to compare them with existing ones using a number of case studies. The synergy between statistical and physical approaches is examined to identify promising areas for further improvement of forecasting accuracy. Appropriate physical and statistical prediction models are also developed for

  7. Weather Research and Forecasting model simulation of an onshore wind farm: assessment against LiDAR and SCADA data

    Science.gov (United States)

    Santoni, Christian; Garcia-Cartagena, Edgardo J.; Zhan, Lu; Iungo, Giacomo Valerio; Leonardi, Stefano

    2017-11-01

    The integration of wind farm parameterizations into numerical weather prediction models is essential to study power production under realistic conditions. Nevertheless, recent models are unable to capture turbine wake interactions and, consequently, the mean kinetic energy entrainment, which are essential for the development of power optimization models. To address the study of wind turbine wake interaction, one-way nested mesoscale to large-eddy simulation (LES) were performed using the Weather Research and Forecasting model (WRF). The simulation contains five nested domains modeling the mesoscale wind on the entire North Texas Panhandle region to the microscale wind fluctuations and turbine wakes of a wind farm located at Panhandle, Texas. The wind speed, direction and boundary layer profile obtained from WRF were compared against measurements obtained with a sonic anemometer and light detection and ranging system located within the wind farm. Additionally, the power production were assessed against measurements obtained from the supervisory control and data acquisition system located in each turbine. Furthermore, to incorporate the turbines into very coarse LES, a modification to the implementation of the wind farm parameterization by Fitch et al. (2012) is proposed. This work was supported by the NSF, Grants No. 1243482 (WINDINSPIRE) and IIP 1362033 (WindSTAR), and TACC.

  8. 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

    within COnsortium for Small-scale MOdelling) applied for power forecasts on a real case in Southern Italy is presented. The approach is based on retrieving meteorological ensemble variables (i.e. wind speed, wind direction), using them to create a power Probability Density Function (PDF) for each 0-72. h......Wind power forecasting (WPF) represents a crucial tool to reduce problems of grid integration and to facilitate energy trading. By now it is advantageous to associate a deterministic forecast with a probabilistic one, in order to give to the end-users information about prediction uncertainty...

  9. 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.

  10. Verification and implementation of microburst day potential index (MDPI) and wind INDEX (WINDEX) forecasting tools at Cape Canaveral Air Station

    Science.gov (United States)

    Wheeler, Mark

    1996-01-01

    This report details the research, development, utility, verification and transition on wet microburst forecasting and detection the Applied Meteorology Unit (AMU) did in support of ground and launch operations at Kennedy Space Center (KSC) and Cape Canaveral Air Station (CCAS). The unforecasted wind event on 16 August 1994 of 33.5 ms-1 (65 knots) at the Shuttle Landing Facility raised the issue of wet microburst detection and forecasting. The AMU researched and analyzed the downburst wind event and determined it was a wet microburst event. A program was developed for operational use on the Meteorological Interactive Data Display System (MIDDS) weather system to analyze, compute and display Theta(epsilon) profiles, the microburst day potential index (MDPI), and wind index (WINDEX) maximum wind gust value. Key microburst nowcasting signatures using the WSR-88D data were highlighted. Verification of the data sets indicated that the MDPI has good potential in alerting the duty forecaster to the potential of wet microburst and the WINDEX values computed from the hourly surface data do have potential in showing a trend for the maximum gust potential. WINDEX should help in filling in the temporal hole between the MDPI on the last Cape Canaveral rawinsonde and the nowcasting radar data tools.

  11. International wind energy development. World market update 2000. Forecast 2001-2005

    International Nuclear Information System (INIS)

    2001-03-01

    In the year 2000, the wind power development took another major step forward. Installed capacity set a new record with the additon of 4,495 MW of new generating capacity, this is 574 MW more than the record set in 1999. Growth in new capacity declined from 51% in 1999 to 15% in 2000. Nevertheless, the trend of wind energy being preferred over other technologies for new generating capacity is continuing. Europe remains the major market for wind power. Of the new capacity added in 2000, 86% was installed in Europe. Germany again took the lead as the single most active market with the installation of 1,665 MW in new capacity, 100 MW more than in the previous year. In doing so, Germany extended its reign as the world's leading developer of wind energy. Spain also took a major step towards meeting its renewable energy goals with the installation of some 1,024 MW of new wind generating capacity. Denmark replaced the United States as the world's third largest market. The Danes installed a new record of 603 MW. This was due to a last-minute rush of contracts signed at the end of 1999 in order to utilize attractive payment rates for new installations, which expired at the end of the year. The turbines were then installed in 2000. Market leaders among wind turbine manufacturers changed from previous years. Vestas Wind Systems A/S is now the world's largest manufacturer of wind turbines, followed by Gamesa S.A. of Spain (Vestas owns 40% of the shares in the Spanish company). Enercon GmbH has become the third largest manufacturer of wind turbines. It is interesting to note that Enercon GmbH made steady progress in sales to several emerging markets throughout the world. New on the Top-10 list is an Indian manufacturer. Suzlon Energy Ltd. installed 103 MW of capacity and thereby becomes the number 10 in the list of the worlds largest manufacturer. The cumulative installed capacity of 18,449 MW at the end of 2000 will supply approximately 37 TWh (37 billion kWh) per year. The

  12. The use of different ensemble forecasting systems for wind power prediction on a real case in the South of Italy

    DEFF Research Database (Denmark)

    Alessandrini, Stefano; Sperati, Simone; Pinson, Pierre

    2012-01-01

    Short-term forecasting applied to wind energy is becoming increasingly important due to the constant growth of this renewable source, whose uncertainty requires a constant effort to meet the needs of the national electrical systems and their operators. Regarding to this, the probabilistic approach...... the data to wind energy: the spread calculated on wind power can then be used as an accuracy predictor due to its level of correlation with the deterministic WPF error. In this presentation we investigate the performances for both wind power and accuracy prediction of the new EPS used at the ECMWF, whose...... horizontal resolution was increased on January 2010 from 60 km to 32 km, on a complex terrain area already used in previous studies and located in Southern Italy. The work consists in the use of the ECMWF deterministic model in a WPF approach followed by a recursive feed-forward Neural Networks (NN...

  13. Modeling the wind-fields of accidental releases with an operational regional forecast model

    International Nuclear Information System (INIS)

    Albritton, J.R.; Lee, R.L.; Sugiyama, G.

    1995-01-01

    The Atmospheric Release Advisory Capability (ARAC) is an operational emergency preparedness and response organization supported primarily by the Departments of Energy and Defense. ARAC can provide real-time assessments of atmospheric releases of radioactive materials at any location in the world. ARAC uses robust three-dimensional atmospheric transport and dispersion models, extensive geophysical and dose-factor databases, meteorological data-acquisition systems, and an experienced staff. Although it was originally conceived and developed as an emergency response and assessment service for nuclear accidents, the ARAC system has been adapted to also simulate non-radiological hazardous releases. For example, in 1991 ARAC responded to three major events: the oil fires in Kuwait, the eruption of Mt. Pinatubo in the Philippines, and the herbicide spill into the upper Sacramento River in California. ARAC's operational simulation system, includes two three-dimensional finite-difference models: a diagnostic wind-field scheme, and a Lagrangian particle-in-cell transport and dispersion scheme. The meteorological component of ARAC's real-time response system employs models using real-time data from all available stations near the accident site to generate a wind-field for input to the transport and dispersion model. Here we report on simulation studies of past and potential release sites to show that even in the absence of local meteorological observational data, readily available gridded analysis and forecast data and a prognostic model, the Navy Operational Regional Atmospheric Prediction System, applied at an appropriate grid resolution can successfully simulate complex local flows

  14. 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)

  15. Mechanistic Drifting Forecast Model for A Small Semi-Submersible Drifter Under Tide-Wind-Wave Conditions

    Science.gov (United States)

    Zhang, Wei-Na; Huang, Hui-ming; Wang, Yi-gang; Chen, Da-ke; Zhang, lin

    2018-03-01

    Understanding the drifting motion of a small semi-submersible drifter is of vital importance regarding monitoring surface currents and the floating pollutants in coastal regions. This work addresses this issue by establishing a mechanistic drifting forecast model based on kinetic analysis. Taking tide-wind-wave into consideration, the forecast model is validated against in situ drifting experiment in the Radial Sand Ridges. Model results show good performance with respect to the measured drifting features, characterized by migrating back and forth twice a day with daily downwind displacements. Trajectory models are used to evaluate the influence of the individual hydrodynamic forcing. The tidal current is the fundamental dynamic condition in the Radial Sand Ridges and has the greatest impact on the drifting distance. However, it loses its leading position in the field of the daily displacement of the used drifter. The simulations reveal that different hydrodynamic forces dominate the daily displacement of the used drifter at different wind scales. The wave-induced mass transport has the greatest influence on the daily displacement at Beaufort wind scale 5-6; while wind drag contributes mostly at wind scale 2-4.

  16. Simulation of the Impact of New Aircraft- and Satellite-Based Ocean Surface Wind Measurements on H*Wind Analyses and Numerical Forecasts

    Science.gov (United States)

    Miller, Timothy; Atlas, Robert; Black, Peter; Chen, Shuyi; Hood, Robbie; Johnson, James; Jones, Linwood; Ruf, Chris; Uhlhorn, Eric; Krishnamurti, T. N.; hide

    2009-01-01

    The Hurricane Imaging Radiometer (HIRAD) is a new airborne microwave remote sensor for hurricane observations that is currently under development by NASA Marshall Space Flight Center, NOAA Hurricane Research Division, the University of Central Florida and the University of Michigan. HIRAD is being designed to enhance the realtime airborne ocean surface winds observation capabilities of NOAA and USAF Weather Squadron hurricane hunter aircraft using the operational airborne Stepped Frequency Microwave Radiometer (SFMR). Unlike SFMR, which measures wind speed and rain rate along the ground track directly beneath the aircraft, HIRAD will provide images of the surface wind and rain field over a wide swath ( 3 x the aircraft altitude). The present paper describes a set of Observing System Simulation Experiments (OSSEs) in which measurements from the new instrument as well as those from existing instruments (air, surface, and space-based) are simulated from the output of a detailed numerical model, and those results are used to construct H*Wind analyses. The H*Wind analysis, a product of the Hurricane Research Division of NOAA s Atlantic Oceanographic and Meteorological Laboratory, brings together wind measurements from a variety of observation platforms into an objective analysis of the distribution of wind speeds in a tropical cyclone. This product is designed to improve understanding of the extent and strength of the wind field, and to improve the assessment of hurricane intensity. See http://www.aoml.noaa.gov/hrd/data_sub/wind.html. Evaluations will be presented on the impact of the HIRAD instrument on H*Wind analyses, both in terms of adding it to the full suite of current measurements, as well as using it to replace instrument(s) that may not be functioning at the future time the HIRAD instrument is implemented. Also shown will be preliminary results of numerical weather prediction OSSEs in which the impact of the addition of HIRAD observations to the initial state

  17. 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

    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...

  18. 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.

  19. Enhanced Forecasting Approach for Electricity Market Prices and Wind Power Data Series in the Short-Term

    Directory of Open Access Journals (Sweden)

    Gerardo J. Osório

    2016-08-01

    Full Text Available The uncertainty and variability in electricity market price (EMP signals and players’ behavior, as well as in renewable power generation, especially wind power, pose considerable challenges. Hence, enhancement of forecasting approaches is required for all electricity market players to deal with the non-stationary and stochastic nature of such time series, making it possible to accurately support their decisions in a competitive environment with lower forecasting error and with an acceptable computational time. As previously published methodologies have shown, hybrid approaches are good candidates to overcome most of the previous concerns about time-series forecasting. In this sense, this paper proposes an enhanced hybrid approach composed of an innovative combination of wavelet transform (WT, differential evolutionary particle swarm optimization (DEEPSO, and an adaptive neuro-fuzzy inference system (ANFIS to forecast EMP signals in different electricity markets and wind power in Portugal, in the short-term, considering only historical data. Test results are provided by comparing with other reported studies, demonstrating the proficiency of the proposed hybrid approach in a real environment.

  20. The use of real-time off-site observations as a methodology for increasing forecast skill in prediction of large wind power ramps one or more hours ahead of their impact on a wind plant.

    Energy Technology Data Exchange (ETDEWEB)

    Martin Wilde, Principal Investigator

    2012-12-31

    ABSTRACT Application of Real-Time Offsite Measurements in Improved Short-Term Wind Ramp Prediction Skill Improved forecasting performance immediately preceding wind ramp events is of preeminent concern to most wind energy companies, system operators, and balancing authorities. The value of near real-time hub height-level wind data and more general meteorological measurements to short-term wind power forecasting is well understood. For some sites, access to onsite measured wind data - even historical - can reduce forecast error in the short-range to medium-range horizons by as much as 50%. Unfortunately, valuable free-stream wind measurements at tall tower are not typically available at most wind plants, thereby forcing wind forecasters to rely upon wind measurements below hub height and/or turbine nacelle anemometry. Free-stream measurements can be appropriately scaled to hub-height levels, using existing empirically-derived relationships that account for surface roughness and turbulence. But there is large uncertainty in these relationships for a given time of day and state of the boundary layer. Alternatively, forecasts can rely entirely on turbine anemometry measurements, though such measurements are themselves subject to wake effects that are not stationary. The void in free-stream hub-height level measurements of wind can be filled by remote sensing (e.g., sodar, lidar, and radar). However, the expense of such equipment may not be sustainable. There is a growing market for traditional anemometry on tall tower networks, maintained by third parties to the forecasting process (i.e., independent of forecasters and the forecast users). This study examines the value of offsite tall-tower data from the WINDataNOW Technology network for short-horizon wind power predictions at a wind farm in northern Montana. The presentation shall describe successful physical and statistical techniques for its application and the practicality of its application in an operational

  1. Estimating the impact of wind generation and wind forecast errors on energy prices and costs in Ireland

    OpenAIRE

    Swinand, Gregory P; O'Mahoney, Amy

    2014-01-01

    This paper studies the impact of wind generation on system costs and prices in Ireland. The need to mitigate climate change, achieve renewables energy targets, and use renewable sources of energy means that many countries are considering greater levels of wind generation in their power generation mix. The overall impact of wind generation on system costs and performance has only been studied recently, and often with limited actual data from power systems with increased wind penetration. The p...

  2. Increase of the Integration Degree of Wind Power Plants into the Energy System Using Wind Forecasting and Power Consumption Predictor Models by Transmission System Operator

    Directory of Open Access Journals (Sweden)

    Manusov V.Z.

    2017-12-01

    Full Text Available Wind power plants’ (WPPs high penetration into the power system leads to various inconveniences in the work of system operators. This fact is associated with the unpredictable nature of wind speed and generated power, respectively. Due to these factors, such source of electricity must be connected to the power system to avoid detrimental effects on the stability and quality of electricity. The power generated by the WPPs is not regulated by the system operator. Accurate forecasting of wind speed and power, as well as power load can solve this problem, thereby making a significant contribution to improving the power supply systems reliability. The article presents a mathematical model for the wind speed prediction, which is based on autoregression and fuzzy logic derivation of Takagi-Sugeno. The new model of wavelet transform has been developed, which makes it possible to include unnecessary noise from the model, as well as to reveal the cycling of the processes and their trend. It has been proved, that the proposed combination of methods can be used simultaneously to predict the power consumption and the wind power plant potential power at any time interval, depending on the planning horizon. The proposed models support a new scientific concept for the predictive control system of wind power stations and increase their degree integration into the electric power system.

  3. A new method for wind speed forecasting based on copula theory.

    Science.gov (United States)

    Wang, Yuankun; Ma, Huiqun; Wang, Dong; Wang, Guizuo; Wu, Jichun; Bian, Jinyu; Liu, Jiufu

    2018-01-01

    How to determine representative wind speed is crucial in wind resource assessment. Accurate wind resource assessments are important to wind farms development. Linear regressions are usually used to obtain the representative wind speed. However, terrain flexibility of wind farm and long distance between wind speed sites often lead to low correlation. In this study, copula method is used to determine the representative year's wind speed in wind farm by interpreting the interaction of the local wind farm and the meteorological station. The result shows that the method proposed here can not only determine the relationship between the local anemometric tower and nearby meteorological station through Kendall's tau, but also determine the joint distribution without assuming the variables to be independent. Moreover, the representative wind data can be obtained by the conditional distribution much more reasonably. We hope this study could provide scientific reference for accurate wind resource assessments. Copyright © 2017 Elsevier Inc. All rights reserved.

  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...

  5. 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.

  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. Using meteorological forecasts in on-line predictions of wind power

    DEFF Research Database (Denmark)

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

    1999-01-01

    This report describes a model investigation into wind power prediction model as well as a tool for predicting the power production from wind turbines in an area - the Wind Power Prediction Tool (WPPT). The predictions are based on on-line measurements of power production for a selected set...

  8. On forecasting ionospheric total electron content responses to high-speed solar wind streams

    Directory of Open Access Journals (Sweden)

    Meng Xing

    2016-01-01

    Full Text Available Conditions in the ionosphere have become increasingly important to forecast, since more and more spaceborne and ground-based technological systems rely on ionospheric weather. Here we explore the feasibility of ionospheric forecasts with the current generation of physics-based models. In particular, we focus on total electron content (TEC predictions using the Global Ionosphere-Thermosphere Model (GITM. Simulations are configured in a forecast mode and performed for four typical high-speed-stream events during 2007–2012. The simulated TECs are quantified through a metric, which divides the globe into a number of local regions and robustly differentiates between quiet and disturbed periods. Proposed forecast products are hourly global maps color-coded by the TEC disturbance level of each local region. To assess the forecasts, we compare the simulated TEC disturbances with global TEC maps derived from Global Positioning System (GPS satellite observations. The forecast performance is found to be merely acceptable, with a large number of regions where the observed variations are not captured by the simulations. Examples of model-data agreements and disagreements are investigated in detail, aiming to understand the model behavior and improve future forecasts. For one event, we identify two adjacent regions with similar TEC observations but significant differences in how local chemistry versus plasma transport contribute to electron density changes in the simulation. Suggestions for further analysis are described.

  9. Noise model based ν-support vector regression with its application to short-term wind speed forecasting.

    Science.gov (United States)

    Hu, Qinghua; Zhang, Shiguang; Xie, Zongxia; Mi, Jusheng; Wan, Jie

    2014-09-01

    Support vector regression (SVR) techniques are aimed at discovering a linear or nonlinear structure hidden in sample data. Most existing regression techniques take the assumption that the error distribution is Gaussian. However, it was observed that the noise in some real-world applications, such as wind power forecasting and direction of the arrival estimation problem, does not satisfy Gaussian distribution, but a beta distribution, Laplacian distribution, or other models. In these cases the current regression techniques are not optimal. According to the Bayesian approach, we derive a general loss function and develop a technique of the uniform model of ν-support vector regression for the general noise model (N-SVR). The Augmented Lagrange Multiplier method is introduced to solve N-SVR. Numerical experiments on artificial data sets, UCI data and short-term wind speed prediction are conducted. The results show the effectiveness of the proposed technique. Copyright © 2014 Elsevier Ltd. All rights reserved.

  10. Probabilistic Solar Wind Forecasting Using Large Ensembles of Near‐Sun Conditions With a Simple One‐Dimensional “Upwind” Scheme

    Science.gov (United States)

    Riley, Pete

    2017-01-01

    Abstract Long lead‐time space‐weather forecasting requires accurate prediction of the near‐Earth solar wind. The current state of the art uses a coronal model to extrapolate the observed photospheric magnetic field to the upper corona, where it is related to solar wind speed through empirical relations. These near‐Sun solar wind and magnetic field conditions provide the inner boundary condition to three‐dimensional numerical magnetohydrodynamic (MHD) models of the heliosphere out to 1 AU. This physics‐based approach can capture dynamic processes within the solar wind, which affect the resulting conditions in near‐Earth space. However, this deterministic approach lacks a quantification of forecast uncertainty. Here we describe a complementary method to exploit the near‐Sun solar wind information produced by coronal models and provide a quantitative estimate of forecast uncertainty. By sampling the near‐Sun solar wind speed at a range of latitudes about the sub‐Earth point, we produce a large ensemble (N = 576) of time series at the base of the Sun‐Earth line. Propagating these conditions to Earth by a three‐dimensional MHD model would be computationally prohibitive; thus, a computationally efficient one‐dimensional “upwind” scheme is used. The variance in the resulting near‐Earth solar wind speed ensemble is shown to provide an accurate measure of the forecast uncertainty. Applying this technique over 1996–2016, the upwind ensemble is found to provide a more “actionable” forecast than a single deterministic forecast; potential economic value is increased for all operational scenarios, but particularly when false alarms are important (i.e., where the cost of taking mitigating action is relatively large). PMID:29398982

  11. A compound structure of ELM based on feature selection and parameter optimization using hybrid backtracking search algorithm for wind speed forecasting

    International Nuclear Information System (INIS)

    Zhang, Chu; Zhou, Jianzhong; Li, Chaoshun; Fu, Wenlong; Peng, Tian

    2017-01-01

    Highlights: • A novel hybrid approach is proposed for wind speed forecasting. • The variational mode decomposition (VMD) is optimized to decompose the original wind speed series. • The input matrix and parameters of ELM are optimized simultaneously by using a hybrid BSA. • Results show that OVMD-HBSA-ELM achieves better performance in terms of prediction accuracy. - Abstract: Reliable wind speed forecasting is essential for wind power integration in wind power generation system. The purpose of paper is to develop a novel hybrid model for short-term wind speed forecasting and demonstrates its efficiency. In the proposed model, a compound structure of extreme learning machine (ELM) based on feature selection and parameter optimization using hybrid backtracking search algorithm (HBSA) is employed as the predictor. The real-valued BSA (RBSA) is exploited to search for the optimal combination of weights and bias of ELM while the binary-valued BSA (BBSA) is exploited as a feature selection method applying on the candidate inputs predefined by partial autocorrelation function (PACF) values to reconstruct the input-matrix. Due to the volatility and randomness of wind speed signal, an optimized variational mode decomposition (OVMD) is employed to eliminate the redundant noises. The parameters of the proposed OVMD are determined according to the center frequencies of the decomposed modes and the residual evaluation index (REI). The wind speed signal is decomposed into a few modes via OVMD. The aggregation of the forecasting results of these modes constructs the final forecasting result of the proposed model. The proposed hybrid model has been applied on the mean half-hour wind speed observation data from two wind farms in Inner Mongolia, China and 10-min wind speed data from the Sotavento Galicia wind farm are studied as an additional case. Parallel experiments have been designed to compare with the proposed model. Results obtained from this study indicate that the

  12. Wind Farm Power Forecasting for Less Than an Hour Using Multi Dimensional Models

    DEFF Research Database (Denmark)

    Knudsen, Torben; Bak, Thomas; Jensen, Tom Nørgaard

    2018-01-01

    The paper focus on prediction of wind farm power for horizons of 0-10 minutes and not more than one hour using statistical methods. These short term predictions are relevant for both transmission system operators, wind farm operators and traders. Previous research indicates that for short time...... horizons the persistence method performs as well as more complex methods. However, these results are based on accumulated power for an entire wind farm. The contribution in this paper is to develop multi-dimensional linear methods based on measurements of power or wind speed from individual wind turbine...... in a wind farm. These multi-dimensional methods are compared with the persistence method using real 1 minute average data from the Sheringham Shoal wind farm with 88 turbines. The results show that the use of measurements from individual turbines reduce the prediction errors 5-10% and also improves...

  13. Validation and development of existing and new RAOB-based warm-season convective wind forecasting tools for Cape Canaveral Air Force Station and Kennedy Space Center

    Science.gov (United States)

    McCue, Mitchell Hollis

    Using a 15-year (1995 to 2009) climatology of 1500 UTC warm-season (May through September) rawinsonde observation (RAOB) data from the Cape Canaveral Air Force Station (CCAFS) Skid Strip (KXMR) and 5 minute wind data from 36 wind towers on CCAFS and Kennedy Space Center (KSC), several convective wind forecasting techniques currently employed by the 45th Weather Squadron (45 WS) were evaluated. Present forecasting methods under evaluation include examining the vertical equivalent potential temperature (theta e) profile, vertical profiles of wind spend and direction, and several wet downburst forecasting indices. Although previous research found that currently used wet downburst forecasting methods showed little promise for forecasting convective winds, it was carried out with a very small sample, limiting the reliability of the results. Evaluation versus a larger 15-year dataset was performed to truly assess the forecasting utility of these methods in the central Florida warm-season convective environment. In addition, several new predictive analytic based forecast methods for predicting the occurrence of warm-season convection and its associated wind gusts were developed and validated. This research was performed in order to help the 45 WS better forecast not only which days are more likely to produce convective wind gusts, but also to better predict which days are more likely to yield warning criteria wind events of 35 knots or greater, should convection be forecasted. Convective wind forecasting is a very challenging problem that requires new statistically based modeling techniques since conventional meteorologically based methods do not perform well. New predictive analytic based forecasting methods were constructed using R statistical software and incorporate several techniques including multiple linear regression, logistic regression, multinomial logistic regression, classification and regression trees (CART), and ensemble CART using bootstrapping. All of

  14. Real-Time Forecasting System of Winds, Waves and Surge in Tropical Cyclones

    National Research Council Canada - National Science Library

    Graber, Hans C; Donelan, Mark A; Brown, Michael G; Slinn, Donald N; Hagen, Scott C; Thompson, Donald R; Jensen, Robert E; Black, Peter G; Powell, Mark D; Guiney, John L

    2004-01-01

    .... The results of this forecasting system would provide real-time information to the National Hurricane Center during the tropical cyclone season in the Atlantic for establishing improved advisories...

  15. Real-Time Forecasting System of Winds, Waves and Surge in Tropical Cyclones

    National Research Council Canada - National Science Library

    Graber, Hans C; Donelan, Mark A; Brown, Michael G; Slinn, Donald N; Hagen, Scott C; Thompson, Donald R; Jensen, Robert E; Black, Peter G; Powell, Mark D; Guiney, John L; Cardone, Vincent J; Cox, Andrew T; Augustus, Ellsworth H; Colonnese, Christopher P

    2003-01-01

    .... The results of this forecasting system would provide real-time information to the National Hurricane Center during the tropical cyclone season in the Atlantic for establishing improved advisories...

  16. Real-Time Forecasting System of Winds, Waves and Surge in Tropical Cyclones

    National Research Council Canada - National Science Library

    Graber, Hans C; Donelan, Mark A; Brown, Michael G; Slinn, Donald N; Hagen, Scott C; Thompson, Donald R; Jensen, Robert E; Black, Peter G; Powell, Mark D; Guiney, John L; Cardone, Vincent J; Cox, Andrew T

    2006-01-01

    ... of tropical cyclones The results of this forecasting system would provide real-time information to the National Hurricane Center during the tropical cyclone season in the Atlantic for establishing improved...

  17. Real-Time Forecasting System of Winds, Waves and Surge in Tropical Cyclones

    National Research Council Canada - National Science Library

    Graber, Hans C; Donelan, Mark A; Brown, Michael G; Slinn, Donald N; Hagen, Scott C; Thompson, Donald R; Jensen, Robert E; Black, Peter G; Powell, Mark D; Guiney, John L

    2005-01-01

    .... The results of this forecasting system would provide real-time information to the National Hurricane Center during the tropical cyclone season in the Atlantic for establishing improved advisories...

  18. Forecasting Wind and Solar Generation: Improving System Operations, Greening the Grid

    Energy Technology Data Exchange (ETDEWEB)

    Tian; Tian; Chernyakhovskiy, Ilya

    2016-01-01

    This document discusses improving system operations with forecasting and solar generation. By integrating variable renewable energy (VRE) forecasts into system operations, power system operators can anticipate up- and down-ramps in VRE generation in order to cost-effectively balance load and generation in intra-day and day-ahead scheduling. This leads to reduced fuel costs, improved system reliability, and maximum use of renewable resources.

  19. Implementation of a Generalized Actuator Line Model for Wind Turbine Parameterization in the Weather Research and Forecasting Model

    Energy Technology Data Exchange (ETDEWEB)

    Lundquist, Julie [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Marjanovic, Nikola [University of California, Berkeley; Lawrence Livermore National Laboratory; Mirocha, Jeffrey D. [Lawrence Livermore National Laboratory; Kosovic, Branko [University Corporation for Atmospheric Research; Chow, Fotini Katopodes [University of California, Berkeley

    2017-12-22

    A generalized actuator line (GAL) wind turbine parameterization is implemented within the Weather Research and Forecasting model to enable high-fidelity large-eddy simulations of wind turbine interactions with boundary layer flows under realistic atmospheric forcing conditions. Numerical simulations using the GAL parameterization are evaluated against both an already implemented generalized actuator disk (GAD) wind turbine parameterization and two field campaigns that measured the inflow and near-wake regions of a single turbine. The representation of wake wind speed, variance, and vorticity distributions is examined by comparing fine-resolution GAL and GAD simulations and GAD simulations at both fine and coarse-resolutions. The higher-resolution simulations show slightly larger and more persistent velocity deficits in the wake and substantially increased variance and vorticity when compared to the coarse-resolution GAD. The GAL generates distinct tip and root vortices that maintain coherence as helical tubes for approximately one rotor diameter downstream. Coarse-resolution simulations using the GAD produce similar aggregated wake characteristics to both fine-scale GAD and GAL simulations at a fraction of the computational cost. The GAL parameterization provides the capability to resolve near wake physics, including vorticity shedding and wake expansion.

  20. Analysis of Hurricane Irene’s Wind Field Using the Advanced Research Weather Research and Forecast (WRF-ARW Model

    Directory of Open Access Journals (Sweden)

    Alfred M. Klausmann

    2014-01-01

    Full Text Available Hurricane Irene caused widespread and significant impacts along the U.S. east coast during 27–29 August 2011. During this period, the storm moved across eastern North Carolina and then tracked northward crossing into Long Island and western New England. Impacts included severe flooding from the mid-Atlantic states into eastern New York and western New England, widespread wind damage and power outages across a large portion of southern and central New England, and a major storm surge along portions of the Long Island coast. The objective of this study was to conduct retrospective simulations using the Advanced Research Weather Research and Forecast (WRF-ARW model in an effort to reconstruct the storm’s surface wind field during the period of 27–29 August 2011. The goal was to evaluate how to use the WRF modeling system as a tool for reconstructing the surface wind field from historical storm events to support storm surge studies. The results suggest that, with even modest data assimilation applied to these simulations, the model was able to resolve the detailed structure of the storm, the storm track, and the spatial surface wind field pattern very well. The WRF model shows real potential for being used as a tool to analyze historical storm events to support storm surge studies.

  1. Simulation of the Impact of New Aircraft-and Satellite-based Ocean Surface Wind Measurements on Wind Analyses and Numerical Forecasts

    Science.gov (United States)

    Miller, TImothy; Atlas, Robert; Black, Peter; Chen, Shuyi; Jones, Linwood; Ruf, Chris; Uhlhorn, Eric; Gamache, John; Amarin, Ruba; El-Nimri, Salem; hide

    2010-01-01

    The Hurricane Imaging Radiometer (HIRAD) is a new airborne microwave remote sensor for hurricane observations that is currently under development by NASA Marshall Space Flight Center, NOAA Hurricane Research Division, the University of Central Florida and the University of Michigan. HIRAD is being designed to enhance the realtime airborne ocean surface winds observation capabilities of NOAA and USAF Weather Squadron hurricane hunter aircraft currently using the operational airborne Stepped Frequency Microwave Radiometer (SFMR). Unlike SFMR, which measures wind speed and rain rate along the ground track directly beneath the aircraft, HIRAD will provide images of the surface wind and rain field over a wide swath (approx. 3 x the aircraft altitude). The present paper describes a set of Observing System Simulation Experiments (OSSEs) in which measurements from the new instrument as well as those from existing instruments (air, surface, and space-based) are simulated from the output of a detailed numerical model, and those results are used to construct H*Wind analyses, a product of the Hurricane Research Division of NOAA s Atlantic Oceanographic and Meteorological Laboratory. Evaluations will be presented on the impact of the HIRAD instrument on H*Wind analyses, both in terms of adding it to the full suite of current measurements, as well as using it to replace instrument(s) that may not be functioning at the future time the HIRAD instrument is implemented. Also shown will be preliminary results of numerical weather prediction OSSEs in which the impact of the addition of HIRAD observations to the initial state on numerical forecasts of the hurricane intensity and structure is assessed.

  2. The Impacts of Satellite Remotely Sensed Winds and Total Precipitable Vapour in WRF Tropical Cyclone Track Forecasts

    Directory of Open Access Journals (Sweden)

    Diandong Ren

    2016-01-01

    Full Text Available This study assesses the impact assimilating the scatterometer near-surface wind observations and total precipitable water from the SSMI, into WRF on genesis and track forecasting of four tropical cyclones (TCs. These TCs are selected to be representative of different intensity categories and basins. Impact is via a series of data denial experiments that systematically exclude the remote sensed information. Compared with the control case, in which only the final analysis atmospheric variables are used to initialize and provide the lateral boundary conditions, the data assimilation runs performed consistently better, but with very different skill levels for the different TCs. Eliassen-Palm flux analyses are employed. It is confirmed that if a polar orbital satellite footprint passes over the TC’s critical genesis region, the forecast will profit most from assimilating the remotely sensed information. If the critical genesis region lies within an interorbital gap then, regardless of how strong the TC later becomes (e.g., Katrina 2005, the improvement from assimilating near-surface winds and total precipitable water in the model prediction is severely limited. This underpins the need for a synergy of data from different scatterometers/radiometers. Other approaches are suggested to improve the accuracy in the prediction of TC genesis and tracks.

  3. Short-Term Wind Speed Forecasting Study and Its Application Using a Hybrid Model Optimized by Cuckoo Search

    Directory of Open Access Journals (Sweden)

    Xuejun Chen

    2015-01-01

    Full Text Available The support vector regression (SVR and neural network (NN are both new tools from the artificial intelligence field, which have been successfully exploited to solve various problems especially for time series forecasting. However, traditional SVR and NN cannot accurately describe intricate time series with the characteristics of high volatility, nonstationarity, and nonlinearity, such as wind speed and electricity price time series. This study proposes an ensemble approach on the basis of 5-3 Hanning filter (5-3H and wavelet denoising (WD techniques, in conjunction with artificial intelligence optimization based SVR and NN model. So as to confirm the validity of the proposed model, two applicative case studies are conducted in terms of wind speed series from Gansu Province in China and electricity price from New South Wales in Australia. The computational results reveal that cuckoo search (CS outperforms both PSO and GA with respect to convergence and global searching capacity, and the proposed CS-based hybrid model is effective and feasible in generating more reliable and skillful forecasts.

  4. A preliminary evaluation of short-term thunderstorm forecasting using surface winds at Kennedy Space Center

    Science.gov (United States)

    Watson, Andrew I.; Holle, Ronald L.; Lopez, Raul E.; Nicholson, James R.

    1990-01-01

    In 1987 NASA expanded its surface wind network onto the mainland west of Kennedy Space Center, increasing the network area from nearly 800 sq km to over 1600 sq km. Here, the results of this expansion are reported using three years of wind and lightning information collected during June, July, August, and September of 1987, 1988, and 1989. The divergence-lightning relationships and the importance of wind direction are addressed, and the verification is summarized.

  5. Recent developments and assessment of a three-dimensional PBL parameterization for improved wind forecasting over complex terrain

    Science.gov (United States)

    Kosovic, B.; Jimenez, P. A.; Haupt, S. E.; Martilli, A.; Olson, J.; Bao, J. W.

    2017-12-01

    At present, the planetary boundary layer (PBL) parameterizations available in most numerical weather prediction (NWP) models are one-dimensional. One-dimensional parameterizations are based on the assumption of horizontal homogeneity. This homogeneity assumption is appropriate for grid cell sizes greater than 10 km. However, for mesoscale simulations of flows in complex terrain with grid cell sizes below 1 km, the assumption of horizontal homogeneity is violated. Applying a one-dimensional PBL parameterization to high-resolution mesoscale simulations in complex terrain could result in significant error. For high-resolution mesoscale simulations of flows in complex terrain, we have therefore developed and implemented a three-dimensional (3D) PBL parameterization in the Weather Research and Forecasting (WRF) model. The implementation of the 3D PBL scheme is based on the developments outlined by Mellor and Yamada (1974, 1982). Our implementation in the Weather Research and Forecasting (WRF) model uses a pure algebraic model (level 2) to diagnose the turbulent fluxes. To evaluate the performance of the 3D PBL model, we use observations from the Wind Forecast Improvement Project 2 (WFIP2). The WFIP2 field study took place in the Columbia River Gorge area from 2015-2017. We focus on selected cases when physical phenomena of significance for wind energy applications such as mountain waves, topographic wakes, and gap flows were observed. Our assessment of the 3D PBL parameterization also considers a large-eddy simulation (LES). We carried out a nested LES with grid cell sizes of 30 m and 10 m covering a large fraction of the WFIP2 study area. Both LES domains were discretized using 6000 x 3000 x 200 grid cells in zonal, meridional, and vertical direction, respectively. The LES results are used to assess the relative magnitude of horizontal gradients of turbulent stresses and fluxes in comparison to vertical gradients. The presentation will highlight the advantages of the 3

  6. An Innovative Hybrid Model Based on Data Pre-Processing and Modified Optimization Algorithm and Its Application in Wind Speed Forecasting

    Directory of Open Access Journals (Sweden)

    Ping Jiang

    2017-07-01

    Full Text Available Wind speed forecasting has an unsuperseded function in the high-efficiency operation of wind farms, and is significant in wind-related engineering studies. Back-propagation (BP algorithms have been comprehensively employed to forecast time series that are nonlinear, irregular, and unstable. However, the single model usually overlooks the importance of data pre-processing and parameter optimization of the model, which results in weak forecasting performance. In this paper, a more precise and robust model that combines data pre-processing, BP neural network, and a modified artificial intelligence optimization algorithm was proposed, which succeeded in avoiding the limitations of the individual algorithm. The novel model not only improves the forecasting accuracy but also retains the advantages of the firefly algorithm (FA and overcomes the disadvantage of the FA while optimizing in the later stage. To verify the forecasting performance of the presented hybrid model, 10-min wind speed data from Penglai city, Shandong province, China, were analyzed in this study. The simulations revealed that the proposed hybrid model significantly outperforms other single metaheuristics.

  7. Evaluation of the wind farm parameterization in the Weather Research and Forecasting model (version 3.8.1) with meteorological and turbine power data

    Science.gov (United States)

    Lee, Joseph C. Y.; Lundquist, Julie K.

    2017-11-01

    Forecasts of wind-power production are necessary to facilitate the integration of wind energy into power grids, and these forecasts should incorporate the impact of wind-turbine wakes. This paper focuses on a case study of four diurnal cycles with significant power production, and assesses the skill of the wind farm parameterization (WFP) distributed with the Weather Research and Forecasting (WRF) model version 3.8.1, as well as its sensitivity to model configuration. After validating the simulated ambient flow with observations, we quantify the value of the WFP as it accounts for wake impacts on power production of downwind turbines. We also illustrate with statistical significance that a vertical grid with approximately 12 m vertical resolution is necessary for reproducing the observed power production. Further, the WFP overestimates wake effects and hence underestimates downwind power production during high wind speed, highly stable, and low turbulence conditions. We also find the WFP performance is independent of the number of wind turbines per model grid cell and the upwind-downwind position of turbines. Rather, the ability of the WFP to predict power production is most dependent on the skill of the WRF model in simulating the ambient wind speed.

  8. Evaluation of the wind farm parameterization in the Weather Research and Forecasting model (version 3.8.1 with meteorological and turbine power data

    Directory of Open Access Journals (Sweden)

    J. C. Y. Lee

    2017-11-01

    Full Text Available Forecasts of wind-power production are necessary to facilitate the integration of wind energy into power grids, and these forecasts should incorporate the impact of wind-turbine wakes. This paper focuses on a case study of four diurnal cycles with significant power production, and assesses the skill of the wind farm parameterization (WFP distributed with the Weather Research and Forecasting (WRF model version 3.8.1, as well as its sensitivity to model configuration. After validating the simulated ambient flow with observations, we quantify the value of the WFP as it accounts for wake impacts on power production of downwind turbines. We also illustrate with statistical significance that a vertical grid with approximately 12 m vertical resolution is necessary for reproducing the observed power production. Further, the WFP overestimates wake effects and hence underestimates downwind power production during high wind speed, highly stable, and low turbulence conditions. We also find the WFP performance is independent of the number of wind turbines per model grid cell and the upwind–downwind position of turbines. Rather, the ability of the WFP to predict power production is most dependent on the skill of the WRF model in simulating the ambient wind speed.

  9. Forecast skill of synoptic conditions associated with Santa Ana winds in Southern California

    Science.gov (United States)

    Charles Jones; Francis Fujioka; Leila M.V. Carvalho

    2010-01-01

    Santa Ana winds (SAW) are synoptically driven mesoscale winds observed in Southern California usually during late fall and winter. Because of the complex topography of the region, SAW episodes can sometimes be extremely intense and pose significant environmental hazards, especially during wildfire incidents. A simple set of criteria was used to identify synoptic-scale...

  10. 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...

  11. Eco-forecasting see different for or against the big wind power sites development

    International Nuclear Information System (INIS)

    Wallut, J.M.; Bal, J.L.

    2002-01-01

    The wind energy is the most economical and technological developed industry of the renewable energy, but it is not perhaps the best way to fight with the greenhouse gases. The pro and the con of the wind power are discussed in this paper. (A.L.B.)

  12. Forecasting and simulating wind speed in Corsica by using an autoregressive model

    International Nuclear Information System (INIS)

    Poggi, P.; Muselli, M.; Notton, G.; Cristofari, C.; Louche, A.

    2003-01-01

    Alternative approaches for generating wind speed time series are discussed. The method utilized involves the use of an autoregressive process model. The model has been applied to three Mediterranean sites in Corsica and has been used to generate 3-hourly synthetic time series for these considered sites. The synthetic time series have been examined to determine their ability to preserve the statistical properties of the Corsican wind speed time series. In this context, using the main statistical characteristics of the wind speed (mean, variance, probability distribution, autocorrelation function), the data simulated are compared to experimental ones in order to check whether the wind speed behavior was correctly reproduced over the studied periods. The purpose is to create a data generator in order to construct a reference year for wind systems simulation in Corsica

  13. Climatology of the Iberia coastal low-level wind jet: weather research forecasting model high-resolution results

    Directory of Open Access Journals (Sweden)

    Pedro M. M. Soares

    2013-01-01

    Full Text Available Coastal low-level jets (CLLJ are a low-tropospheric wind feature driven by the pressure gradient produced by a sharp contrast between high temperatures over land and lower temperatures over the sea. This contrast between the cold ocean and the warm land in the summer is intensified by the impact of the coastal parallel winds on the ocean generating upwelling currents, sharpening the temperature gradient close to the coast and giving rise to strong baroclinic structures at the coast. During summertime, the Iberian Peninsula is often under the effect of the Azores High and of a thermal low pressure system inland, leading to a seasonal wind, in the west coast, called the Nortada (northerly wind. This study presents a regional climatology of the CLLJ off the west coast of the Iberian Peninsula, based on a 9 km resolution downscaling dataset, produced using the Weather Research and Forecasting (WRF mesoscale model, forced by 19 years of ERA-Interim reanalysis (1989–2007. The simulation results show that the jet hourly frequency of occurrence in the summer is above 30% and decreases to about 10% during spring and autumn. The monthly frequencies of occurrence can reach higher values, around 40% in summer months, and reveal large inter-annual variability in all three seasons. In the summer, at a daily base, the CLLJ is present in almost 70% of the days. The CLLJ wind direction is mostly from north-northeasterly and occurs more persistently in three areas where the interaction of the jet flow with local capes and headlands is more pronounced. The coastal jets in this area occur at heights between 300 and 400 m, and its speed has a mean around 15 m/s, reaching maximum speeds of 25 m/s.

  14. Probabilistic forecasts of wind power generation accounting for geographically dispersed information

    DEFF Research Database (Denmark)

    Tastu, Julija; Pinson, Pierre; Trombe, Pierre-Julien

    2014-01-01

    be optimized by accounting for spatio-temporal effects that are so far merely considered. The way these effects may be included in relevant models is described for the case of both parametric and nonparametric approaches to generating probabilistic forecasts. The resulting predictions are evaluated on the real...... of the first order moments of predictive densities. The best performing approach, based on adaptive quantile regression, using spatially corrected point forecasts as input, consistently outperforms the state-of-theartbenchmark based on local information only, by 1.5%-4.6%, depending upon the lead time....

  15. A robust combination approach for short-term wind speed forecasting and analysis – Combination of the ARIMA (Autoregressive Integrated Moving Average), ELM (Extreme Learning Machine), SVM (Support Vector Machine) and LSSVM (Least Square SVM) forecasts using a GPR (Gaussian Process Regression) model

    International Nuclear Information System (INIS)

    Wang, Jianzhou; Hu, Jianming

    2015-01-01

    With the increasing importance of wind power as a component of power systems, the problems induced by the stochastic and intermittent nature of wind speed have compelled system operators and researchers to search for more reliable techniques to forecast wind speed. This paper proposes a combination model for probabilistic short-term wind speed forecasting. In this proposed hybrid approach, EWT (Empirical Wavelet Transform) is employed to extract meaningful information from a wind speed series by designing an appropriate wavelet filter bank. The GPR (Gaussian Process Regression) model is utilized to combine independent forecasts generated by various forecasting engines (ARIMA (Autoregressive Integrated Moving Average), ELM (Extreme Learning Machine), SVM (Support Vector Machine) and LSSVM (Least Square SVM)) in a nonlinear way rather than the commonly used linear way. The proposed approach provides more probabilistic information for wind speed predictions besides improving the forecasting accuracy for single-value predictions. The effectiveness of the proposed approach is demonstrated with wind speed data from two wind farms in China. The results indicate that the individual forecasting engines do not consistently forecast short-term wind speed for the two sites, and the proposed combination method can generate a more reliable and accurate forecast. - Highlights: • The proposed approach can make probabilistic modeling for wind speed series. • The proposed approach adapts to the time-varying characteristic of the wind speed. • The hybrid approach can extract the meaningful components from the wind speed series. • The proposed method can generate adaptive, reliable and more accurate forecasting results. • The proposed model combines four independent forecasting engines in a nonlinear way.

  16. A Time-Varying Potential-Based Demand Response Method for Mitigating the Impacts of Wind Power Forecasting Errors

    Directory of Open Access Journals (Sweden)

    Jia Ning

    2017-11-01

    Full Text Available The uncertainty of wind power results in wind power forecasting errors (WPFE which lead to difficulties in formulating dispatching strategies to maintain the power balance. Demand response (DR is a promising tool to balance power by alleviating the impact of WPFE. This paper offers a control method of combining DR and automatic generation control (AGC units to smooth the system’s imbalance, considering the real-time DR potential (DRP and security constraints. A schematic diagram is proposed from the perspective of a dispatching center that manages smart appliances including air conditioner (AC, water heater (WH, electric vehicle (EV loads, and AGC units to maximize the wind accommodation. The presented model schedules the AC, WH, and EV loads without compromising the consumers’ comfort preferences. Meanwhile, the ramp constraint of generators and power flow transmission constraint are considered to guarantee the safety and stability of the power system. To demonstrate the performance of the proposed approach, simulations are performed in an IEEE 24-node system. The results indicate that considerable benefits can be realized by coordinating the DR and AGC units to mitigate the WPFE impacts.

  17. Short-Term Wind Speed Forecasting Using the Data Processing Approach and the Support Vector Machine Model Optimized by the Improved Cuckoo Search Parameter Estimation Algorithm

    Directory of Open Access Journals (Sweden)

    Chen Wang

    2016-01-01

    Full Text Available Power systems could be at risk when the power-grid collapse accident occurs. As a clean and renewable resource, wind energy plays an increasingly vital role in reducing air pollution and wind power generation becomes an important way to produce electrical power. Therefore, accurate wind power and wind speed forecasting are in need. In this research, a novel short-term wind speed forecasting portfolio has been proposed using the following three procedures: (I data preprocessing: apart from the regular normalization preprocessing, the data are preprocessed through empirical model decomposition (EMD, which reduces the effect of noise on the wind speed data; (II artificially intelligent parameter optimization introduction: the unknown parameters in the support vector machine (SVM model are optimized by the cuckoo search (CS algorithm; (III parameter optimization approach modification: an improved parameter optimization approach, called the SDCS model, based on the CS algorithm and the steepest descent (SD method is proposed. The comparison results show that the simple and effective portfolio EMD-SDCS-SVM produces promising predictions and has better performance than the individual forecasting components, with very small root mean squared errors and mean absolute percentage errors.

  18. MPC for Wind Power Gradients - Utilizing Forecasts, Rotor Inertia, and Central Energy Storage

    DEFF Research Database (Denmark)

    Hovgaard, Tobias Gybel; Larsen, Lars F. S.; Jørgensen, John Bagterp

    2013-01-01

    decentralized energy storage in the turbines’ inertia combined with a central storage unit or deferrable consumers can be utilized to achieve this goal at a minimum cost. We propose a variation on model predictive control to incorporate predictions of wind speed. Due to the aerodynamics of the turbines...... the model contains nonconvex terms. To handle this nonconvexity, we propose a sequential convex optimization method, which typically converges in fewer than 10 iterations. We demonstrate our method in simulations with various wind scenarios and prices for energy storage. These simulations show substantial......We consider the control of a wind power plant, possibly consisting of many individual wind turbines. The goal is to maximize the energy delivered to the power grid under very strict grid requirements to power quality. We define an extremely low power output gradient and demonstrate how...

  19. International wind energy development. World market update 1996. Forecast 1997-2001

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1997-03-01

    It is intended topublish and update this report `International Wind Power Development - Market Update 1996` every year in March. The latest issue was published in December of 1995 by BTM Consult ApS. Market data for demand side does normally, where it has been possible, only include installed wind turbines which has been connected to the grid during 1996. Supply side data is based on collected information (reference lists) from the 12-15 largest wind turbine manufacturers in the world. Due to these circumstances there are some conflicts between demand side and supply side data. Another important factor is that all wind turbine manufacturers do not use the same methodology when they are making their reference lists. Some manufacturers include only installed and operating wind turbines, while others include all units shipped from the factory. This is another conflict that might cause some differences in the data. Lately another new issue has been added to the list of uncertain factors, which is: is a Nordex Balcke-Duerr wind turbine Danish or German? And is a Gamesa Eolica wind turbine Danish or Spanish? etc. To minimize annual variations, this report includes the last 3 years data for the largest markets and wind turbine manufacturers. The prognosis includes data for the next 5 years until the year 2001, and an estimate has also been included for development until the year 2005. This version of the report can be found on internet web-pages: http://home4.inet.tele.dk/btmcwind/index.html. (EG)

  20. International wind energy development. World market update 1996. Forecast 1997-2001

    International Nuclear Information System (INIS)

    1997-03-01

    It is intended topublish and update this report 'International Wind Power Development - Market Update 1996' every year in March. The latest issue was published in December of 1995 by BTM Consult ApS. Market data for demand side does normally, where it has been possible, only include installed wind turbines which has been connected to the grid during 1996. Supply side data is based on collected information (reference lists) from the 12-15 largest wind turbine manufacturers in the world. Due to these circumstances there are some conflicts between demand side and supply side data. Another important factor is that all wind turbine manufacturers do not use the same methodology when they are making their reference lists. Some manufacturers include only installed and operating wind turbines, while others include all units shipped from the factory. This is another conflict that might cause some differences in the data. Lately another new issue has been added to the list of uncertain factors, which is: is a Nordex Balcke-Duerr wind turbine Danish or German? And is a Gamesa Eolica wind turbine Danish or Spanish? etc. To minimize annual variations, this report includes the last 3 years data for the largest markets and wind turbine manufacturers. The prognosis includes data for the next 5 years until the year 2001, and an estimate has also been included for development until the year 2005. This version of the report can be found on internet web-pages: http://home4.inet.tele.dk/btmcwind/index.html. (EG)

  1. Development and verification of a new wind speed forecasting system using an ensemble Kalman filter data assimilation technique in a fully coupled hydrologic and atmospheric model

    Science.gov (United States)

    Williams, John L.; Maxwell, Reed M.; Monache, Luca Delle

    2013-12-01

    Wind power is rapidly gaining prominence as a major source of renewable energy. Harnessing this promising energy source is challenging because of the chaotic nature of wind and its inherently intermittent nature. Accurate forecasting tools are critical to support the integration of wind energy into power grids and to maximize its impact on renewable energy portfolios. We have adapted the Data Assimilation Research Testbed (DART), a community software facility which includes the ensemble Kalman filter (EnKF) algorithm, to expand our capability to use observational data to improve forecasts produced with a fully coupled hydrologic and atmospheric modeling system, the ParFlow (PF) hydrologic model and the Weather Research and Forecasting (WRF) mesoscale atmospheric model, coupled via mass and energy fluxes across the land surface, and resulting in the PF.WRF model. Numerous studies have shown that soil moisture distribution and land surface vegetative processes profoundly influence atmospheric boundary layer development and weather processes on local and regional scales. We have used the PF.WRF model to explore the connections between the land surface and the atmosphere in terms of land surface energy flux partitioning and coupled variable fields including hydraulic conductivity, soil moisture, and wind speed and demonstrated that reductions in uncertainty in these coupled fields realized through assimilation of soil moisture observations propagate through the hydrologic and atmospheric system. The sensitivities found in this study will enable further studies to optimize observation strategies to maximize the utility of the PF.WRF-DART forecasting system.

  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. Battlescale Forecast Model Sensitivity Study

    National Research Council Canada - National Science Library

    Sauter, Barbara

    2003-01-01

    .... Changes to the surface observations used in the Battlescale Forecast Model initialization led to no significant changes in the resulting forecast values of temperature, relative humidity, wind speed, or wind direction...

  4. Real time wave forecasting using wind time history and numerical model

    Science.gov (United States)

    Jain, Pooja; Deo, M. C.; Latha, G.; Rajendran, V.

    Operational activities in the ocean like planning for structural repairs or fishing expeditions require real time prediction of waves over typical time duration of say a few hours. Such predictions can be made by using a numerical model or a time series model employing continuously recorded waves. This paper presents another option to do so and it is based on a different time series approach in which the input is in the form of preceding wind speed and wind direction observations. This would be useful for those stations where the costly wave buoys are not deployed and instead only meteorological buoys measuring wind are moored. The technique employs alternative artificial intelligence approaches of an artificial neural network (ANN), genetic programming (GP) and model tree (MT) to carry out the time series modeling of wind to obtain waves. Wind observations at four offshore sites along the east coast of India were used. For calibration purpose the wave data was generated using a numerical model. The predicted waves obtained using the proposed time series models when compared with the numerically generated waves showed good resemblance in terms of the selected error criteria. Large differences across the chosen techniques of ANN, GP, MT were not noticed. Wave hindcasting at the same time step and the predictions over shorter lead times were better than the predictions over longer lead times. The proposed method is a cost effective and convenient option when a site-specific information is desired.

  5. A Hybrid Multi-Step Rolling Forecasting Model Based on SSA and Simulated Annealing—Adaptive Particle Swarm Optimization for Wind Speed

    Directory of Open Access Journals (Sweden)

    Pei Du

    2016-08-01

    Full Text Available With the limitations of conventional energy becoming increasing distinct, wind energy is emerging as a promising renewable energy source that plays a critical role in the modern electric and economic fields. However, how to select optimization algorithms to forecast wind speed series and improve prediction performance is still a highly challenging problem. Traditional single algorithms are widely utilized to select and optimize parameters of neural network algorithms, but these algorithms usually ignore the significance of parameter optimization, precise searching, and the application of accurate data, which results in poor forecasting performance. With the aim of overcoming the weaknesses of individual algorithms, a novel hybrid algorithm was created, which can not only easily obtain the real and effective wind speed series by using singular spectrum analysis, but also possesses stronger adaptive search and optimization capabilities than the other algorithms: it is faster, has fewer parameters, and is less expensive. For the purpose of estimating the forecasting ability of the proposed combined model, 10-min wind speed series from three wind farms in Shandong Province, eastern China, are employed as a case study. The experimental results were considerably more accurately predicted by the presented algorithm than the comparison algorithms.

  6. International wind energy development. World market update 2001. Forecast 2002-2006

    International Nuclear Information System (INIS)

    2002-03-01

    In the year 2001, the wind power development took another major step forward. Installed capacity set a new record with the addition of 6,824 MW of new generating capacity. This is 2,329 MW more than the record set in year 2000. Growth in new capacity is up from 15% in year 2000 to 52% in 2001. This confirmed that the trend of wind energy being the preferred technology over other technologies for new generating capacity is continuing. Europe is the leading region for wind power. Of the new capacity added in 2001, 4,527 MW was installed in Europe. Germany once more shows this country's potential as the single most active market with the installation of 2,627 MW in new capacity, nearly 1,000 MW more than in the previous year. Germany consolidated its position as the world's leading developer of wind energy. The market in the US is once again the second largest wind energy market ahead of Spain. The expiration of the PTC at the end of the year 2001 gave some rush in the installation at the end of the year. The Danish market is on a deroute and lost pace completely in a changed political climate which means that the development of windpower is slowing down. Denmark has a very high penetration of windpower. On the supplier side Vestas Wind Systems A/S maintained its position as being the world's largest manufacturer of wind turbines. Vestas Wind Systems is now followed by Enercon GmbH. Without counting the US market figures Vestas Wind Systems A/S and Enercon has become the very closed in terms of sold MW. In the 3rd place is the Danish company NEG Micon A/S. Newcomers in the Top Ten list are Mitsubishi (JP) and REpower (GE). The most significant technological trend in the market is the continuing upscaling of machines. From year 2001 the average size of WTGs is 915 kW (in 2000: 800kW). In the very near future there will be erected wind turbines mainly dedicated for the upcoming offshore market of 4.5-5.0 MW. On Offshore there is only 10 MW installed during the year 2001

  7. Sensitivity of Turbine-Height Wind Speeds to Parameters in Planetary Boundary-Layer and Surface-Layer Schemes in the Weather Research and Forecasting Model

    Energy Technology Data Exchange (ETDEWEB)

    Yang, Ben; Qian, Yun; Berg, Larry K.; Ma, Po-Lun; Wharton, Sonia; Bulaevskaya, Vera; Yan, Huiping; Hou, Zhangshuan; Shaw, William J.

    2016-07-21

    We evaluate the sensitivity of simulated turbine-height winds to 26 parameters applied in a planetary boundary layer (PBL) scheme and a surface layer scheme of the Weather Research and Forecasting (WRF) model over an area of complex terrain during the Columbia Basin Wind Energy Study. An efficient sampling algorithm and a generalized linear model are used to explore the multiple-dimensional parameter space and quantify the parametric sensitivity of modeled turbine-height winds. The results indicate that most of the variability in the ensemble simulations is contributed by parameters related to the dissipation of the turbulence kinetic energy (TKE), Prandtl number, turbulence length scales, surface roughness, and the von Kármán constant. The relative contributions of individual parameters are found to be dependent on both the terrain slope and atmospheric stability. The parameter associated with the TKE dissipation rate is found to be the most important one, and a larger dissipation rate can produce larger hub-height winds. A larger Prandtl number results in weaker nighttime winds. Increasing surface roughness reduces the frequencies of both extremely weak and strong winds, implying a reduction in the variability of the wind speed. All of the above parameters can significantly affect the vertical profiles of wind speed, the altitude of the low-level jet and the magnitude of the wind shear strength. The wind direction is found to be modulated by the same subset of influential parameters. Remainder of abstract is in attachment.

  8. Rejoinder on: Space-time wind speed forecasting for improved power system dispatch

    KAUST Repository

    Zhu, Xinxin

    2014-02-27

    We are thankful to the four reviewers for providing very valuable and insightful comments. We have divided our rejoinder into two main parts: (1) the rotating RSTD model; and (2) the integration of wind power into a power system. In each part, we present our views on the various comments of the discussants and provide further discussion. © 2014 Sociedad de Estadística e Investigación Operativa.

  9. The combined value of wind and solar power forecasting improvements and electricity storage

    Energy Technology Data Exchange (ETDEWEB)

    Hodge, Bri-Mathias; Brancucci Martinez-Anido, Carlo; Wang, Qin; Chartan, Erol; Florita, Anthony; Kiviluoma, Juha

    2018-03-01

    As the penetration rates of variable renewable energy increase, the value of power systems operation flexibility technology options, such as renewable energy forecasting improvements and electricity storage, is also assumed to increase. In this work, we examine the value of these two technologies, when used independently and concurrently, for two real case studies that represent the generation mixes for the California and Midcontinent Independent System Operators (CAISO and MISO). Since both technologies provide additional system flexibility they reduce operational costs and renewable curtailment for both generation mixes under study. Interestingly, the relative impacts are quite similar when both technologies are used together. Though both flexibility options can solve some of the same issues that arise with high penetration levels of renewables, they do not seem to significantly increase or decrease the economic potential of the other technology.

  10. Decision Support Tools for Electricity Retailers, Wind Power and CHP Plants Using Probabilistic Forecasts

    DEFF Research Database (Denmark)

    Zugno, Marco; Morales González, Juan Miguel; Madsen, Henrik

    2015-01-01

    This paper reviews a number of applications of optimization under uncertainty in energy markets resulting from the research project ENSYMORA. A general mathematical formulation applicable to problems of optimization under uncertainty in energy markets is presented. This formulation can......: trading for a price-maker wind power producer, management of heat and power systems, operation for retailers in a dynamic-price market. A selection of results shows the viability and appropriateness of the presented stochastic optimization approaches for managing energy systems under uncertainty....

  11. Coupling the Weather Research and Forecasting (WRF) model and Large Eddy Simulations with Actuator Disk Model: predictions of wind farm power production

    Science.gov (United States)

    Garcia Cartagena, Edgardo Javier; Santoni, Christian; Ciri, Umberto; Iungo, Giacomo Valerio; Leonardi, Stefano

    2015-11-01

    A large-scale wind farm operating under realistic atmospheric conditions is studied by coupling a meso-scale and micro-scale models. For this purpose, the Weather Research and Forecasting model (WRF) is coupled with an in-house LES solver for wind farms. The code is based on a finite difference scheme, with a Runge-Kutta, fractional step and the Actuator Disk Model. The WRF model has been configured using seven one-way nested domains where the child domain has a mesh size one third of its parent domain. A horizontal resolution of 70 m is used in the innermost domain. A section from the smallest and finest nested domain, 7.5 diameters upwind of the wind farm is used as inlet boundary condition for the LES code. The wind farm consists in six-turbines aligned with the mean wind direction and streamwise spacing of 10 rotor diameters, (D), and 2.75D in the spanwise direction. Three simulations were performed by varying the velocity fluctuations at the inlet: random perturbations, precursor simulation, and recycling perturbation method. Results are compared with a simulation on the same wind farm with an ideal uniform wind speed to assess the importance of the time varying incoming wind velocity. Numerical simulations were performed at TACC (Grant CTS070066). This work was supported by NSF, (Grant IIA-1243482 WINDINSPIRE).

  12. Bayesian Analysis of Hot Jupiter Radii Points to Ohmic Dissipation

    Science.gov (United States)

    Thorngren, Daniel; Fortney, Jonathan J.

    2017-10-01

    The cause of the unexpectedly large radii of hot Jupiters has been the subject of many hypotheses over the past 15 years and is one of the long-standing open issues in exoplanetary physics. In our work, we seek to examine the population of 300 hot Jupiters to identify a model that best explains their radii. Using a hierarchical Bayesian framework, we match structure evolution models to the observed giant planets’ masses, radii, and ages, with a prior for bulk composition based on the mass from Thorngren et al. (2016). We consider various models for the relationship between heating efficiency (the fraction of flux absorbed into the interior) and incident flux. For the first time, we are able to derive this heating efficiency as a function of planetary T_eq. Models in which the heating efficiency decreases at the higher temperatures (above ~1600 K) are strongly and statistically significantly preferred. Of the published models for the radius anomaly, only the Ohmic dissipation model predicts this feature, which it explains as being the result of magnetic drag reducing atmospheric wind speeds. We interpret our results as strong evidence in favor of the Ohmic dissipation model.

  13. Decision Support Tools for Electricity Retailers, Wind Power and CHP Plants Using Probabilistic Forecasts

    Directory of Open Access Journals (Sweden)

    Marco Zugno

    2015-06-01

    Full Text Available This paper reviews a number of applications of optimization under uncertainty in energy markets resulting from the research project ENSYMORA. A general mathematical formulation applicable to problems of optimization under uncertainty in energy markets is presented. This formulation can be effortlessly adapted to describe different approaches: the deterministic one (usable within a rolling horizon scheme, stochastic programming and robust optimization. The different features of this mathematical formulation are duly interpreted with a view to the energy applications reviewed in this paper: trading for a price-maker wind power producer, management of heat and power systems, operation for retailers in a dynamic-price market. A selection of results shows the viability and appropriateness of the presented stochastic optimization approaches for managing energy systems under uncertainty.

  14. Application of Ensemble Sensitivity Analysis to Observation Targeting for Short-term Wind Speed Forecasting in the Tehachapi Region Winter Season

    Energy Technology Data Exchange (ETDEWEB)

    Zack, John [AWS Truepower, LLC, Albany, NY (United States); Natenberg, Eddie [AWS Truepower, LLC, Albany, NY (United States); Young, Steve [AWS Truepower, LLC, Albany, NY (United States); Van Knowe, Glenn [AWS Truepower, LLC, Albany, NY (United States); Waight, Ken [AWS Truepower, LLC, Albany, NY (United States); Manobainco, John [AWS Truepower, LLC, Albany, NY (United States); Kamath, Chandrika [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

    2010-10-20

    This study extends the wind power forecast sensitivity work done by Zack et al. (2010a, b) in two prior research efforts. Zack et al. (2010a, b) investigated the relative predictive value and optimal combination of different variables/locations from correlated sensitivity patterns. Their work involved developing the Multiple Observation Optimization Algorithm (MOOA) and applying the algorithm to the results obtained from the Ensemble Sensitivity Analysis (ESA) method (Ancell and Hakim 2007; Torn and Hakim 2008).

  15. Sensitivity of Turbine-Height Wind Speeds to Parameters in Planetary Boundary-Layer and Surface-Layer Schemes in the Weather Research and Forecasting Model

    Science.gov (United States)

    Yang, Ben; Qian, Yun; Berg, Larry K.; Ma, Po-Lun; Wharton, Sonia; Bulaevskaya, Vera; Yan, Huiping; Hou, Zhangshuan; Shaw, William J.

    2017-01-01

    We evaluate the sensitivity of simulated turbine-height wind speeds to 26 parameters within the Mellor-Yamada-Nakanishi-Niino (MYNN) planetary boundary-layer scheme and MM5 surface-layer scheme of the Weather Research and Forecasting model over an area of complex terrain. An efficient sampling algorithm and generalized linear model are used to explore the multiple-dimensional parameter space and quantify the parametric sensitivity of simulated turbine-height wind speeds. The results indicate that most of the variability in the ensemble simulations is due to parameters related to the dissipation of turbulent kinetic energy (TKE), Prandtl number, turbulent length scales, surface roughness, and the von Kármán constant. The parameter associated with the TKE dissipation rate is found to be most important, and a larger dissipation rate produces larger hub-height wind speeds. A larger Prandtl number results in smaller nighttime wind speeds. Increasing surface roughness reduces the frequencies of both extremely weak and strong airflows, implying a reduction in the variability of wind speed. All of the above parameters significantly affect the vertical profiles of wind speed and the magnitude of wind shear. The relative contributions of individual parameters are found to be dependent on both the terrain slope and atmospheric stability.

  16. Diquark correlations from nucleon charge radii

    International Nuclear Information System (INIS)

    Carlson, Carl E.; Carone, Christopher D.; Kwee, Herry J.; Lebed, Richard F.

    2006-01-01

    We argue that precise measurements of charge and magnetic radii can meaningfully constrain diquark models of the nucleon. We construct properly symmetrized, nonrelativistic three-quark wave functions that interpolate between the limits of a pointlike diquark pair and no diquark correlation. We find that good fits to the data can be obtained for a wide range of diquark sizes, provided that the diquark wave functions are close to those that reduce to a purely scalar state in the pointlike limit. A modest improvement in the experimental uncertainties will render a fit to the charge radii a more telling diagnostic for the presence of spatially correlated quark pairs within the nucleon

  17. A preliminary study of the impact of the ERS 1 C band scatterometer wind data on the European Centre for Medium-Range Weather Forecasts global data assimilation system

    Science.gov (United States)

    Hoffman, Ross N.

    1993-01-01

    A preliminary assessment of the impact of the ERS 1 scatterometer wind data on the current European Centre for Medium-Range Weather Forecasts analysis and forecast system has been carried out. Although the scatterometer data results in changes to the analyses and forecasts, there is no consistent improvement or degradation. Our results are based on comparing analyses and forecasts from assimilation cycles. The two sets of analyses are very similar except for the low level wind fields over the ocean. Impacts on the analyzed wind fields are greater over the southern ocean, where other data are scarce. For the most part the mass field increments are too small to balance the wind increments. The effect of the nonlinear normal mode initialization on the analysis differences is quite small, but we observe that the differences tend to wash out in the subsequent 6-hour forecast. In the Northern Hemisphere, analysis differences are very small, except directly at the scatterometer locations. Forecast comparisons reveal large differences in the Southern Hemisphere after 72 hours. Notable differences in the Northern Hemisphere do not appear until late in the forecast. Overall, however, the Southern Hemisphere impacts are neutral. The experiments described are preliminary in several respects. We expect these data to ultimately prove useful for global data assimilation.

  18. Distribution of correlation radii in disordered ferroelectrics

    Czech Academy of Sciences Publication Activity Database

    Glinchuk, M. D.; Eliseev, E. A.; Stepanovich, V. A.; Jastrabík, Lubomír

    2002-01-01

    Roč. 81, č. 25 (2002), s. 4808-4810 ISSN 0003-6951 R&D Projects: GA MŠk LN00A015 Institutional research plan: CEZ:AV0Z1010914 Keywords : disordered ferroelectrics * distribution of correlation radii * polar nanoregions Subject RIV: BM - Solid Matter Physics ; Magnetism Impact factor: 4.207, year: 2002

  19. Internal machining accomplished at constant radii

    Science.gov (United States)

    Gollihugh, T. E.

    1966-01-01

    Device machines fluid passages in workpieces at constant radii through two adjacent surfaces that are at included angles up to approximately 120 degrees. This technique has been used extensively in fabricating engine parts where close control of fluid flow is a requirement.

  20. Investigation of boundary-layer wind predictions during nocturnal low-level jet events using the Weather Research and Forecasting model

    Energy Technology Data Exchange (ETDEWEB)

    Mirocha, Jeff D.; Simpson, Matthew D.; Fast, Jerome D.; Berg, Larry K.; Baskett, R.

    2016-04-01

    Simulations of two periods featuring three consecutive low level jet (LLJ) events in the US Upper Great Plains during the autumn of 2011 were conducted to explore the impacts of various setup configurations and physical process models on simulated flow parameters within the lowest 200 m above the surface, using the Weather Research and Forecasting (WRF) model. Sensitivities of simulated flow parameters to the horizontal and vertical grid spacing, planetary boundary layer (PBL) and land surface model (LSM) physics options, were assessed. Data from a Light Detection and Ranging (lidar) system, deployed to the Weather Forecast Improvement Project (WFIP; Finley et al. 2013) were used to evaluate the accuracy of simulated wind speed and direction at 80 m above the surface, as well as their vertical distributions between 120 and 40 m, covering the typical span of contemporary tall wind turbines. All of the simulations qualitatively captured the overall diurnal cycle of wind speed and stratification, producing LLJs during each overnight period, however large discrepancies occurred at certain times for each simulation in relation to the observations. 54-member ensembles encompassing changes of the above discussed configuration parameters displayed a wide range of simulated vertical distributions of wind speed and direction, and potential temperature, reflecting highly variable representations of stratification during the weakly stable overnight conditions. Root mean square error (RMSE) statistics show that different ensemble members performed better and worse in various simulated parameters at different times, with no clearly superior configuration . Simulations using a PBL parameterization designed specifically for the stable conditions investigated herein provided superior overall simulations of wind speed at 80 m, demonstrating the efficacy of targeting improvements of physical process models in areas of known deficiencies. However, the considerable magnitudes of the

  1. The influence of tropical wind data on the analysis and forecasts of the GLAS GCM for the Global Weather Experiment

    Science.gov (United States)

    Paegle, J.; Baker, W. E.

    1985-01-01

    Several densities of tropical divergent wind data were included in a fourth-order GCM to examine the effects on the accuracy of the model predictions. The experiments covered assimilation of all available tropical wind data, no tropical wind data between 20 deg N and 20 deg S, only westerly tropical wind data and only easterly tropical wind data. The predictions were all made for the 200 mb upper troposphere. Elimination of tropical data produced excessively strong upper tropospheric westerlies which in turn amplified the globally integrated rotational flow kinetic energy by around 10 percent and doubled the global divergent flow kinetic energy. Retaining only easterly wind data, ameliorated most of the error. Inclusion of all the tropical wind data however, did not lead to overall positive effects, as the data were linked to tropical wave energetics and ageostrophic winds which were already assimilated in the model.

  2. Radii of Redox Components from Absolute Redox Potentials Compared with Covalent and Aqueous Ionic Radii

    Czech Academy of Sciences Publication Activity Database

    Heyrovská, Raji

    2010-01-01

    Roč. 22, č. 9 (2010), s. 903-907 ISSN 1040-0397 Institutional support: RVO:68081707 Keywords : Electrochemistry * Absolute redox potentials * Radii of redox components Subject RIV: BO - Biophysics Impact factor: 2.721, year: 2010

  3. 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.

  4. Aspects of the determination of winds by means of scatterometry and of the utilization of vector wind data for meteorological forecasts

    Science.gov (United States)

    Pierson, W. J., Jr.; Sylvester, W. B.; Donelan, M. A.

    1986-01-01

    The present paper provides a description of four aspects of scatterometer winds and their uses. The theory of wave generation by the wind is considered along with an analysis of the properties of superobservations, and studies of intermittent versus continuous data assimilation methods for numerical weather predictions which use remotely sensed data. A comparison of the sum of squares versus the maximum likelihood method for recovering the vector winds is also conducted. Questions regarding wind speed, friction velocity, or normal stress are discussed and synoptic scale fields from Seasat-SASS data are examined.

  5. Medium-range fire weather forecasts

    Science.gov (United States)

    J.O. Roads; K. Ueyoshi; S.C. Chen; J. Alpert; F. Fujioka

    1991-01-01

    The forecast skill of theNational Meteorological Center's medium range forecast (MRF) numerical forecasts of fire weather variables is assessed for the period June 1,1988 to May 31,1990. Near-surface virtual temperature, relative humidity, wind speed and a derived fire weather index (FWI) are forecast well by the MRF model. However, forecast relative humidity has...

  6. A comparison of boundary-layer heights inferred from wind-profiler backscatter profiles with diagnostic calculations using regional model forecasts

    Energy Technology Data Exchange (ETDEWEB)

    Baltink, H.K.; Holtslag, A.A.M. [Royal Netherlands Meteorological Inst., KNMI, De Bilt (Netherlands)

    1997-10-01

    From October 1994 through January 1997 the Tropospheric Energy Budget Experiment (TEBEX) was executed by KNMI. The main objectives are to study boundary layer processes and cloud variability on the sub-grid scale of present Global Climate Models and to improve the related sub-grid parametrizations. A suite of instruments was deployed to measure a large number of variables. Measurements to characterize ABL processes were focussed around the 200 m high meteorological observation tower of the KNMI in Cabauw. In the framework of TEBEX a 1290 MHz wind-profiler/RASS was installed in July 1994 at 300 m from tower. Data collected during TEBEX are used to assess the performance of a Regional Atmospheric Climate Model (RACMO). This climate model runs also in a operational forecast mode once a day. The diagnostic ABL-height (h{sub model}) is calculated from the RACMO forecast output. A modified Richardson`s number method extended with an excess parcel temperature is applied for all stability conditions. We present the preliminary results of a comparison of h{sub model} from forecasts with measured h{sub TS} derived from profiler and sodar data for July 1995. (au)

  7. Impact of assimilating met-tower, turbine nacelle anemometer and other intensified wind farm observation systems on 0 - 12h wind energy prediction using the NCAR WRF-RTFDDA model

    Science.gov (United States)

    Liu, Y.; Cheng, W.; Liu, Y. W.; Wiener, G.; Frehlich, R.; Mahoney, W.; Warner, T.; Himelic, J.; Parks, K.; Early, S.

    2010-09-01

    In collaboration with Xcel Energy and Vasaila Inc., the National Center for Atmospheric Research (NCAR) conducts modeling study to evaluate the existing and the enhanced intensive observation systems for wind power nowcasting and short-range forecasting at a northern Colorado wind farm. The NCAR WRF (Weather Research and Forecasting model) based Real-Time Four-Dimensional Data Assimilation (RTFDDA) and forecasting system, which has been employed to support Xcel Energy operational wind forecast, was used in this study. The observational data include ten met-towers, a 915Hz wind profiler, a sodar and a Windcube Doppler lidar, besides the in-farm met-towers and wind speed and power reports from more than 300 of wind turbines. The WRF-RTFDDA 4-dimensioanl data assimilation algorithm allows to spread and propagate observation information in the WRF model space (x, y, z and time) with weighting functions built according to the observation location and time. The WRF-RTFDDA was set up to run with four nested domains with grid increments of 30, 10, 3.333 and 1.111km respectively. The standard and diverse non-conventional observations are assimilated on coarse grid domains along with the special wind farm observations. In this study, we investigate a) spread of surface observations in PBL according to PBL depth and regimes, b) optimization of horizontal influence radii and steep-terrain adjustment, and c) impact of different observation platforms and data types on 0 - 12 h wind prediction . It is found that PBL mixing and thermodynamic structures are greatly influenced by the PBL parameterization formulation. The range of the data assimilation effect on forecasts relies on weather and PBL regimes. In most cases, assimilation of in-farm and near-farm observations improves up to 12-hour wind power prediction and assimilation of in-farm data can significantly improves 0 - 6 hour forecasts.

  8. Regional Forecasting of Wind Speeds during Typhoon Landfall in Taiwan: A Case Study of Westward-Moving Typhoons

    Directory of Open Access Journals (Sweden)

    Chih-Chiang Wei

    2018-04-01

    Full Text Available Taiwan is located on a route where typhoons often strike. Each year, the strong winds accompanying typhoons are a substantial threat and cause significant damage. However, because the terrains of high mountains in Taiwan vary greatly, when a typhoon passes the Central Mountain Range (CMR, the wind speed of typhoons becomes difficult to predict. This research had two primary objectives: (1 to develop data-driven techniques and a powerful artificial neural network (ANN model to predict the highly complex nonlinear wind systems in western Taiwan; and, (2 to investigate the accuracy of wind speed predictions at various locations and for various durations in western Taiwan when the track of westward typhoons is affected by the complex geographical shelters and disturbances of the CMR. This study developed a typhoon wind speed prediction model that evaluated various typhoon tracks (covering Type 2, Type 3, and Type 4 tracks, as defined by the Central Weather Bureau, and evaluated the prediction accuracy at Hsinchu, Wuqi, and Kaohsiung Stations in western Taiwan. Back propagation neural networks (BPNNs were employed to establish wind speed prediction models, and a linear regression model was adopted as the benchmark to evaluate the strengths and weaknesses of the BPNNs. The results were as follows: (1 The BPNNs generally had favorable performance in predicting wind speeds and their performances were superior to linear regressions; (2 when absolute errors were adopted to evaluate the prediction performances, the predictions at Hsinchu Station were the most accurate, whereas those at Wuqi Station were the least accurate; however, when relative errors were adopted, the predictions at Hsinchu Station were again the most accurate, whereas those at Kaohsiung were the least accurate; and, (3 regarding the relative error rates for the maximum wind speed of Types 2, 3, and 4 typhoons, Wuqi, Kaohsiung, and Wuqi had the most accurate performance, respectively; as

  9. Theoretical Calculation of Absolute Radii of Atoms and Ions. Part 1. The Atomic Radii

    Directory of Open Access Journals (Sweden)

    Raka Biswas

    2002-02-01

    Full Text Available Abstract. A set of theoretical atomic radii corresponding to the principal maximum in the radial distribution function, 4πr2R2 for the outermost orbital has been calculated for the ground state of 103 elements of the periodic table using Slater orbitals. The set of theoretical radii are found to reproduce the periodic law and the Lother Meyer’s atomic volume curve and reproduce the expected vertical and horizontal trend of variation in atomic size in the periodic table. The d-block and f-block contractions are distinct in the calculated sizes. The computed sizes qualitatively correlate with the absolute size dependent properties like ionization potentials and electronegativity of elements. The radii are used to calculate a number of size dependent periodic physical properties of isolated atoms viz., the diamagnetic part of the atomic susceptibility, atomic polarizability and the chemical hardness. The calculated global hardness and atomic polarizability of a number of atoms are found to be close to the available experimental values and the profiles of the physical properties computed in terms of the theoretical atomic radii exhibit their inherent periodicity. A simple method of computing the absolute size of atoms has been explored and a large body of known material has been brought together to reveal how many different properties correlate with atomic size.

  10. Spins, moments and radii of Cd isotopes

    International Nuclear Information System (INIS)

    Hammen, Michael

    2013-01-01

    , the electric quadrupole moments, spins and changes in mean square charge radii are extracted. The obtained data reveal among other features an extremely linear behaviour of the quadrupole moments of the I=11/2 - isomeric states and a parabolic development in differences in mean square nuclear charge radii between ground and isomeric state. The development of charge radii between the shell closures is smooth, exposes a regular odd-even staggering and can be described and interpreted in the model of Zamick and Thalmi.

  11. Implementation of a generalized actuator disk wind turbine model into the weather research and forecasting model for large-eddy simulation applications

    Energy Technology Data Exchange (ETDEWEB)

    Mirocha, J. D. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Kosovic, B. [National Center for Atmospheric Research, Boulder, CO (United States); Aitken, M. L. [Univ. of Colorado, Boulder, CO (United States); Lundquist, J. K. [Univ. of Colorado, Boulder, CO (United States); National Renewable Energy Lab., Golden, CO (United States)

    2014-01-10

    A generalized actuator disk (GAD) wind turbine parameterization designed for large-eddy simulation (LES) applications was implemented into the Weather Research and Forecasting (WRF) model. WRF-LES with the GAD model enables numerical investigation of the effects of an operating wind turbine on and interactions with a broad range of atmospheric boundary layer phenomena. Numerical simulations using WRF-LES with the GAD model were compared with measurements obtained from the Turbine Wake and Inflow Characterization Study (TWICS-2011), the goal of which was to measure both the inflow to and wake from a 2.3-MW wind turbine. Data from a meteorological tower and two light-detection and ranging (lidar) systems, one vertically profiling and another operated over a variety of scanning modes, were utilized to obtain forcing for the simulations, and to evaluate characteristics of the simulated wakes. Simulations produced wakes with physically consistent rotation and velocity deficits. Two surface heat flux values of 20 W m–2 and 100 W m–2 were used to examine the sensitivity of the simulated wakes to convective instability. Simulations using the smaller heat flux values showed good agreement with wake deficits observed during TWICS-2011, whereas those using the larger value showed enhanced spreading and more-rapid attenuation. This study demonstrates the utility of actuator models implemented within atmospheric LES to address a range of atmospheric science and engineering applications. In conclusion, validated implementation of the GAD in a numerical weather prediction code such as WRF will enable a wide range of studies related to the interaction of wind turbines with the atmosphere and surface.

  12. Forecast of Wind Speed with a Backpropagation Artificial Neural Network in the Isthmus of Tehuantepec Region in the State of Oaxaca, Mexico

    Directory of Open Access Journals (Sweden)

    Orlando Lastres Danguillecourt

    2012-03-01

    Full Text Available Este trabajo presenta los resultados preliminares de la configuración de una red neuronal artificial (ANN, de tipo alimentación hacia adelante con el método de entrenamiento de retro-propagación para pronosticar la velocidad de viento en la región del Istmo de Tehuantepec, Oaxaca, México. La base de datos utilizada abarca los años comprendidos entre Junio 2008- Noviembre 2011, y fue suministrada por una estación meteorológica ubicada en la Universidad del Istmo campus Tehuantepec. Los experimentos se realizaron utilizando las siguientes variables: velocidad del viento, presión, temperatura y fecha. Al mismo tiempo se hicieron siete pruebas combinando estas variables, comparando su error cuadrático medio (MSE y el coeficiente de correlación r, con los datos de predicción y experimentales. En esta investigación, se propone una ANN de dos capas ocultas, para un pronóstico de 48 horas.This paper presents the preliminary results of setting up an artificial neural network (ANN of the feed forward type with the backpropagation training method for forecast wind speed in the region in the Isthmus of Tehuantepec, Oaxaca, Mexico. The database used covers the years from June 2008 - November 2011, and was supplied by a meteorological station located at the Isthmus University campus Tehuantepec. The experiments were done using the following variables: wind speed, pressure, temperature and date. At the same time were done seven tests combining these variables, comparing their mean square error (MSE and coefficient correlation r, with data the predicting and experimental. In this research, is proposed a ANN of two hidden layers, for a forecast of 48 hours.

  13. Forecasting Kp from solar wind data: input parameter study using 3-hour averages and 3-hour range values

    Directory of Open Access Journals (Sweden)

    Wintoft Peter

    2017-01-01

    Full Text Available We have developed neural network models that predict Kp from upstream solar wind data. We study the importance of various input parameters, starting with the magnetic component Bz, particle density n, and velocity V and then adding total field B and the By component. As we also notice a seasonal and UT variation in average Kp we include functions of day-of-year and UT. Finally, as Kp is a global representation of the maximum range of geomagnetic variation over 3-hour UT intervals we conclude that sudden changes in the solar wind can have a big effect on Kp, even though it is a 3-hour value. Therefore, 3-hour solar wind averages will not always appropriately represent the solar wind condition, and we introduce 3-hour maxima and minima values to some degree address this problem. We find that introducing total field B and 3-hour maxima and minima, derived from 1-minute solar wind data, have a great influence on the performance. Due to the low number of samples for high Kp values there can be considerable variation in predicted Kp for different networks with similar validation errors. We address this issue by using an ensemble of networks from which we use the median predicted Kp. The models (ensemble of networks provide prediction lead times in the range 20–90 min given by the time it takes a solar wind structure to travel from L1 to Earth. Two models are implemented that can be run with real time data: (1 IRF-Kp-2017-h3 uses the 3-hour averages of the solar wind data and (2 IRF-Kp-2017 uses in addition to the averages, also the minima and maxima values. The IRF-Kp-2017 model has RMS error of 0.55 and linear correlation of 0.92 based on an independent test set with final Kp covering 2 years using ACE Level 2 data. The IRF-Kp-2017-h3 model has RMSE = 0.63 and correlation = 0.89. We also explore the errors when tested on another two-year period with real-time ACE data which gives RMSE = 0.59 for IRF-Kp-2017 and RMSE = 0.73 for IRF

  14. Forecasting Kp from solar wind data: input parameter study using 3-hour averages and 3-hour range values

    Science.gov (United States)

    Wintoft, Peter; Wik, Magnus; Matzka, Jürgen; Shprits, Yuri

    2017-11-01

    We have developed neural network models that predict Kp from upstream solar wind data. We study the importance of various input parameters, starting with the magnetic component Bz, particle density n, and velocity V and then adding total field B and the By component. As we also notice a seasonal and UT variation in average Kp we include functions of day-of-year and UT. Finally, as Kp is a global representation of the maximum range of geomagnetic variation over 3-hour UT intervals we conclude that sudden changes in the solar wind can have a big effect on Kp, even though it is a 3-hour value. Therefore, 3-hour solar wind averages will not always appropriately represent the solar wind condition, and we introduce 3-hour maxima and minima values to some degree address this problem. We find that introducing total field B and 3-hour maxima and minima, derived from 1-minute solar wind data, have a great influence on the performance. Due to the low number of samples for high Kp values there can be considerable variation in predicted Kp for different networks with similar validation errors. We address this issue by using an ensemble of networks from which we use the median predicted Kp. The models (ensemble of networks) provide prediction lead times in the range 20-90 min given by the time it takes a solar wind structure to travel from L1 to Earth. Two models are implemented that can be run with real time data: (1) IRF-Kp-2017-h3 uses the 3-hour averages of the solar wind data and (2) IRF-Kp-2017 uses in addition to the averages, also the minima and maxima values. The IRF-Kp-2017 model has RMS error of 0.55 and linear correlation of 0.92 based on an independent test set with final Kp covering 2 years using ACE Level 2 data. The IRF-Kp-2017-h3 model has RMSE = 0.63 and correlation = 0.89. We also explore the errors when tested on another two-year period with real-time ACE data which gives RMSE = 0.59 for IRF-Kp-2017 and RMSE = 0.73 for IRF-Kp-2017-h3. The errors as function

  15. Photoelectric method for determination of the relative radii of microparticles

    Science.gov (United States)

    Lapitsky, D. S.

    2018-01-01

    Experimental results of relative radii of microparticles determination by the analysis of scattered on particles radiation are presented. Due to the monotonous dependence of the indicatrix of particle scattered radiation on particle radius at a specific angle, it becomes possible to determine the relative radii of particles in cloud of particles by one camera picture. Radii of three types of monodisperse powders of melamine formaldehyde particles with known sizes were analyzed by means of Mie theory. The results showed the possibility to determine relative radii of particles to each other in suspended clouds of particles.

  16. One-level modeling for diagnosing surface winds over complex terrain. II - Applicability to short-range forecasting

    Science.gov (United States)

    Alpert, P.; Getenio, B.; Zak-Rosenthal, R.

    1988-01-01

    The Alpert and Getenio (1988) modification of the Mass and Dempsey (1985) one-level sigma-surface model was used to study four synoptic events that included two winter cases (a Cyprus low and a Siberian high) and two summer cases. Results of statistical verification showed that the model is not only capable of diagnosing many details of surface mesoscale flow, but might also be useful for various applications which require operative short-range prediction of the diurnal changes of high-resolution surface flow over complex terrain, for example, in locating wildland fires, determining the dispersion of air pollutants, and predicting changes in wind energy or of surface wind for low-level air flights.

  17. What shapes stellar metallicity gradients of massive galaxies at large radii?

    Science.gov (United States)

    Hirschmann, Michaela

    2017-03-01

    We investigate the differential impact of physical mechanisms, mergers and internal energetic phenomena, on the evolution of stellar metallicity gradients in massive, present-day galaxies employing sets of high-resolution, cosmological zoom simulations. We demonstrate that negative metallicity gradients at large radii (>2Reff) originate from the accretion of metal-poor stellar systems. At larger radii, galaxies become typically more dominated by stars accreted from satellite galaxies in major and minor mergers. However, only strong galactic, stellar-driven winds can sufficiently reduce the metallicity content of the accreted stars to realistically steepen the outer metallicity gradients in agreement with observations. In contrast, the gradients of the models without winds are inconsistent with observations. Moreover, we discuss the impact of additional AGN feedback. This analysis greatly highlights the importance of both energetic processes and merger events for stellar population properties of massive galaxies at large radii. Our results are expected to significantly contribute to the interpretation of current and up-coming IFU surveys (e.g. MaNGA, CALIFA).

  18. Impact of Scatterometer Ocean Wind Vector Data on NOAA Operations

    Science.gov (United States)

    Jelenak, Z.; Chang, P.; Brennan, M. J.; Sienkiewicz, J. M.

    2015-12-01

    Near real-time measurements of ocean surface vector winds (OSVW), including both wind speed and direction from non-NOAA satellites, are being widely used in critical operational NOAA forecasting and warning activities. The scatterometer wind data data have had major operational impact in: a) determining wind warning areas for mid-latitude systems (gale, storm,hurricane force); b) determining tropical cyclone 34-knot and 50-knot wind radii. c) tracking the center location of tropical cyclones, including the initial identification of their formation. d) identifying and warning of extreme gap and jet wind events at all latitudes. e) identifying the current location of frontal systems and high and low pressure centers. f) improving coastal surf and swell forecasts Much has been learned about the importance and utility of satellite OSVW data in operational weather forecasting and warning by exploiting OSVW research satellites in near real-time. Since December 1999 when first data from QuikSCAT scatterometer became available in near real time NOAA operations have been benefiting from ASCAT scatterometer observations on MetOp-A and B, Indian OSCAT scatterometer on OceanSat-3 and lately NASA's RapidScat mission on International Space Station. With oceans comprising over 70 percent of the earth's surface, the impacts of these data have been tremendous in serving society's needs for weather and water information and in supporting the nation's commerce with information for safe, efficient, and environmentally sound transportation and coastal preparedness. The satellite OSVW experience that has been gained over the past decade by users in the operational weather community allows for realistic operational OSVW requirements to be properly stated for future missions. Successful model of transitioning research data into operation implemented by Ocean Winds Team in NOAA's NESDIS/STAR office and subsequent data impacts will be presented and discussed.

  19. Table of nuclear root mean square charge radii. Summary

    International Nuclear Information System (INIS)

    Paviotti-Corcuera, R.; McLaughlin, P.K.

    1999-01-01

    This document describes a table of nuclear root-mean-square (rms) charge radii evaluated by two different procedures. The data are available from the IAEA Nuclear Data Section via INTERNET or on PC diskettes upon request. This document supersedes the previous IAEA-NDS-163, 1990, 'Nuclear Charge Radii'. (author)

  20. Systematics of experimental charge radii of elements and elementary particles

    Energy Technology Data Exchange (ETDEWEB)

    Antony, M.S.; Britz, J.

    1987-02-01

    The systematics of experimental charge radii of elements and elementary particles ..pi../sup -/, K/sup -/, K/sup 0/, p and n is discussed. The root-meansquare charge radius of a quark core in nucleous derived from the systematics is estimated to be 0.3 fm. Charge radii evaluated from Coulomb displacement energies are also tabulated.

  1. A method of optimized neural network by L-M algorithm to transformer winding hot spot temperature forecasting

    Science.gov (United States)

    Wei, B. G.; Wu, X. Y.; Yao, Z. F.; Huang, H.

    2017-11-01

    Transformers are essential devices of the power system. The accurate computation of the highest temperature (HST) of a transformer’s windings is very significant, as for the HST is a fundamental parameter in controlling the load operation mode and influencing the life time of the insulation. Based on the analysis of the heat transfer processes and the thermal characteristics inside transformers, there is taken into consideration the influence of factors like the sunshine, external wind speed etc. on the oil-immersed transformers. Experimental data and the neural network are used for modeling and protesting of the HST, and furthermore, investigations are conducted on the optimization of the structure and algorithms of neutral network are conducted. Comparison is made between the measured values and calculated values by using the recommended algorithm of IEC60076 and by using the neural network algorithm proposed by the authors; comparison that shows that the value computed with the neural network algorithm approximates better the measured value than the value computed with the algorithm proposed by IEC60076.

  2. High-speed Solar Wind Stream Forecast Based on Coronal Hole Index Derived from Solar EUV Images

    Science.gov (United States)

    Gong, J.; Luo, B.; Bu, X.; Liu, S.

    2017-12-01

    High-speed streams (HSS), which originate from coronal holes on the Sun, are interplanetary sources of recurrent geospace environment disturbances such as geomagnetic storms, relativistic electron enhancements at the geosynchronous orbit, and thermosphere density enhancements which increase the orbit decay rate for low orbit satellites. People have been searching for good indices which can be used as proxies of coronal hole to predict HSS. Among these indices, the Pch reported by Luo et al. [2008], reflected both the area and the brightness contributions of coronal hole and showed potential in predicting HSS. In this study, we evaluate the performance of the Pch index in predict the solar wind speed at L1, using Solar Dynamics Observatory (SDO)/Atmospheric Imaging Assembly (AIA) images for years 2011 to 2016. On verification of the predicting capability, we focus on the event-based analysis of the predicted arrival times and amplitudes of high-speed streams (considered as HSS events). It is found that the Pch index is capable of predicting the large-scale high-speed stream features about 4 days in advance, with uncertainties in the HSS arrival time of about 1 day and uncertainties in the speed of about 100 km/s.

  3. Correlating radii and electric monopole transitions of atomic nuclei.

    Science.gov (United States)

    Zerguine, S; Van Isacker, P; Bouldjedri, A; Heinze, S

    2008-07-11

    A systematic analysis of the spherical-to-deformed shape phase transition in even-even rare-earth nuclei from 58Ce to 74W is carried out in the framework of the interacting boson model. These results are then used to calculate nuclear radii and electric monopole (E0) transitions with the same effective operator. The influence of the hexadecapole degree of freedom (g boson) on the correlation between radii and E0 transitions thus established is discussed.

  4. Consistent van der Waals radii for the whole main group.

    Science.gov (United States)

    Mantina, Manjeera; Chamberlin, Adam C; Valero, Rosendo; Cramer, Christopher J; Truhlar, Donald G

    2009-05-14

    Atomic radii are not precisely defined but are nevertheless widely used parameters in modeling and understanding molecular structure and interactions. The van der Waals radii determined by Bondi from molecular crystals and data for gases are the most widely used values, but Bondi recommended radius values for only 28 of the 44 main-group elements in the periodic table. In the present Article, we present atomic radii for the other 16; these new radii were determined in a way designed to be compatible with Bondi's scale. The method chosen is a set of two-parameter correlations of Bondi's radii with repulsive-wall distances calculated by relativistic coupled-cluster electronic structure calculations. The newly determined radii (in A) are Be, 1.53; B, 1.92; Al, 1.84; Ca, 2.31; Ge, 2.11; Rb, 3.03; Sr, 2.49; Sb, 2.06; Cs, 3.43; Ba, 2.68; Bi, 2.07; Po, 1.97; At, 2.02; Rn, 2.20; Fr, 3.48; and Ra, 2.83.

  5. Charge radii of argon isotopes in the $f_{7/2}$ shell and radii systematics in the Ca-region

    CERN Document Server

    Blaum, K; Lassen, J; Lievens, P; Marinova, K; Neugart, R

    2005-01-01

    Changes in mean square (ms) nuclear charge radii of Ar isotopes accross the 1$\\scriptstyle{f}_{7/2}$ shell are studied by fast beam collinear laser spectroscopy using an ultra-sensitive detection method based on optical pumping and state selective collisional ionisation. The new data set on Ar, in combination with the known charge radii of K, Ca and Ti in the $\

  6. Nuclear charge radii: density functional theory meets Bayesian neural networks

    Science.gov (United States)

    Utama, R.; Chen, Wei-Chia; Piekarewicz, J.

    2016-11-01

    The distribution of electric charge in atomic nuclei is fundamental to our understanding of the complex nuclear dynamics and a quintessential observable to validate nuclear structure models. The aim of this study is to explore a novel approach that combines sophisticated models of nuclear structure with Bayesian neural networks (BNN) to generate predictions for the charge radii of thousands of nuclei throughout the nuclear chart. A class of relativistic energy density functionals is used to provide robust predictions for nuclear charge radii. In turn, these predictions are refined through Bayesian learning for a neural network that is trained using residuals between theoretical predictions and the experimental data. Although predictions obtained with density functional theory provide a fairly good description of experiment, our results show significant improvement (better than 40%) after BNN refinement. Moreover, these improved results for nuclear charge radii are supplemented with theoretical error bars. We have successfully demonstrated the ability of the BNN approach to significantly increase the accuracy of nuclear models in the predictions of nuclear charge radii. However, as many before us, we failed to uncover the underlying physics behind the intriguing behavior of charge radii along the calcium isotopic chain.

  7. Assessment of storm forecast

    DEFF Research Database (Denmark)

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

    at analysing the ability of existing forecast tools to predict storms at the Horns Rev 2 wind farm. The focus will be on predicting the time where the wind turbine will need to shut down to protect itself, e.g. the time where wind speed exceeds 25 m/s. At the same time, the planned shut-down should cost...... storms was analysed based on historical meteorological data available at Risø DTU and dynamically down-scaled to the Horns Rev 2 wind farm level. This solution was chosen due to the lack of measurements. Moreover, since the project started, there were four events during which Horns Rev 2 wind farm...

  8. The measurement of dynamic radii for passenger car tyre

    Science.gov (United States)

    Anghelache, G.; Moisescu, R.

    2017-10-01

    The tyre dynamic rolling radius is an extremely important parameter for vehicle dynamics, for operation of safety systems as ESP, ABS, TCS, etc., for road vehicle research and development, as well as for validation or as an input parameter of automotive simulations and models. The paper investigates the dynamic rolling radii of passenger car tyre and the influence of rolling speed and inflation pressure on their magnitude. The measurement of dynamic rolling radii has been performed on a chassis dynamometer test rig. The dynamic rolling radii have been measured indirectly, using longitudinal rolling speed and angular velocity of wheel. Due to the subtle effects that the parameters have on rolling radius magnitude, very accurate equipment has to be used. Two different methods have been chosen for measuring the wheel angular velocity: the stroboscopic lamp and the incremental rotary encoder. The paper shows that the stroboscopic lamp has an insufficient resolution, therefore it was no longer used for experimental investigation. The tyre dynamic rolling radii increase with rolling speed and with tyre inflation pressure, but the effect of pressure is more significant. The paper also makes considerations on the viability of simplified formulae from literature for calculating the tyre dynamic rolling radius.

  9. Soil Failure Crescent Radii Measurement for Draft in Tillage Study ...

    African Journals Online (AJOL)

    Field clay loam and sandy loam soils were tilled with a chisel shaped tine at different tillage geometries. Soil cracks and the extent of their propagations in the front and to the sides of the tillage tool were observed and measured. These measurements provided the failure crescent radii and the soil furrow geometry used in ...

  10. Form factors and charge radii in a quantum chromodynamics ...

    Indian Academy of Sciences (India)

    The results show that charge radii and form factors are quite small for the Coulombic parent compared to the linear parent. Also, the analysis leads to a lower as well as upper bounds on the four-momentum transfer 2, hinting at a workable range of 2 within this approach, which may be useful in future experimental ...

  11. Form factors and charge radii in a quantum chromodynamics ...

    Indian Academy of Sciences (India)

    Abstract. We use variationally improved perturbation theory (VIPT) for calculating the elas- tic form factors and charge radii of D, Ds,B,Bs and Bc mesons in a quantum chromodynamics. (QCD)-inspired potential model. For that, we use linear-cum-Coulombic potential and opt the. Coulombic part first as parent and then the ...

  12. From short term power forecasting to nowcasting - Benefiting from meteorological forecasts and measurements

    Science.gov (United States)

    Mey, Britta; Braun, Axel; Good, Garrett; Vogt, Stephan; Wessel, Arne; Dobschinski, Jan

    2016-04-01

    Today, wind and solar power forecasts with time horizons from zero to about three hours are essential for the reliable grid and market integration of wind and solar energy. With respect to closure times of German intra-day markets, power forecasts with time horizons of about one to two hours and an update frequency of 15 minutes are required for final trading activities, reducing the uncertainty of the day-ahead forecast of the previous day. Regarding grid security aspects, grid operators utilize such forecasts to create continuous intra-day grid congestion forecasts. In addition to these preventive measures, wind and solar power become more and more important for the provision of ancillary services by wind and solar farm operators. This use case mainly requires power forecasts with time horizons of less than one hour. In general, forecasts with time horizons below three hours are investigated within the nowcasting research area. Nowcasting models are mainly based on current observations and extrapolation methods. With respect to wind and solar power forecasts with horizons of up to three hours, it has been shown in studies that real-time power measurements have the highest information content as compared to other potential model input parameters. We will present results from studies focusing on the benefit of meteorological data (forecasts and/or measurements) in the field of solar and wind power forecasts with time horizons of up to a few hours. Wind farm forecast errors are for example reduced by using numerical weather prediction (NWP) data in the wind power prediction model along with real-time wind farm power measurements. Furthermore, spatially distributed NWP data in combination with German total wind power measurements helped in the reduction of extreme forecast errors. By using global radiation forecasts as an input for wind power forecasts, forecast error during sunrise and sunset could be reduced. In the field of German total solar power, nowcasting

  13. On the Predictability of Hub Height Winds

    DEFF Research Database (Denmark)

    Draxl, Caroline

    of power output at wind farms. Since the power available in the wind is proportional to the wind speed cubed, even small wind forecast errors result in large power prediction errors. Accurate wind forecasts are worth billions of dollars annually; forecast improvements will result in reduced costs....... This is particularly relevant with offshore facilities, which represent a significant portion of new wind farms being constructed. Furthermore, a novel aspect to this study is the presentation of a verification methodology that takes into account wind at different heights where turbines operate. The increasing number...... grids. These systems require forecasts with temporal scales of tens of minutes to a few days in advance at wind farm locations. Traditionally these forecasts predict the wind at turbine hub heights; this information is then converted by transmission system operators and energy companies into predictions...

  14. Integrated Wind Power Planning Tool

    DEFF Research Database (Denmark)

    Rosgaard, Martin H.; Hahmann, Andrea N.; Nielsen, Torben S.

    This poster presents the Public Service Obligation (PSO) funded project PSO 10464 "Integrated Wind Power Planning Tool". The project goal is to integrate a Numerical Weather Prediction (NWP) model with statistical tools in order to assess wind power fluctuations, with focus on short term...... forecasting for existing wind farms, as well as long term power system planning for future wind farms....

  15. Forecasting Skill

    Science.gov (United States)

    1981-01-01

    indicated that forecasting experience has little relationship to forecasting performance. In the latter three studies, neophyte forecasters became... Europe . Within a few months after a new commander was assigned, this unit’s performance rose to first place in the theater and remained there

  16. Some challenges of wind modelling for modern wind turbines: The Weibull distribution

    DEFF Research Database (Denmark)

    Gryning, Sven-Erik; Batchvarova, Ekatarina; Floors, Rogier

    2012-01-01

    Wind power assessments, as well as forecast of wind energy production, are key issues in wind energy and grid related studies. However the hub height of today’s wind turbines is well above the surface layer. Wind profiles studies based on mast data show that the wind profile above the surface layer...

  17. Forecasting Turbine Icing Events

    DEFF Research Database (Denmark)

    Davis, Neil; Hahmann, Andrea N.; Clausen, Niels-Erik

    2012-01-01

    In this study, we present a method for forecasting icing events. The method is validated at two European wind farms in with known icing events. The icing model used was developed using current ice accretion methods, and newly developed ablation algorithms. The model is driven by inputs from the WRF...... mesoscale model, allowing for both climatological estimates of icing and short term icing forecasts. The current model was able to detect periods of icing reasonably well at the warmer site. However at the cold climate site, the model was not able to remove ice quickly enough leading to large ice...... accumulations, which have not been seen in observations. In addition to the model evaluation we were able to investigate the potential occurrence of ice induced power loss at two wind parks in Europe using observed data. We found that the potential loss during an icing event is large even when the turbine...

  18. Spins, moments and charge radii beyond $^{48}$Ca

    CERN Multimedia

    Neyens, G; Rajabali, M M; Hammen, M; Blaum, K; Froemmgen, N E; Garcia ruiz, R F; Kreim, K D; Budincevic, I

    Laser spectroscopy of $^{49-54}$Ca is proposed as a continuation of the experimental theme initiated with IS484 “Ground-state properties of K-isotopes from laser and $\\beta$-NMR spectroscopy” and expanded in INTC-I-117 “Moments, Spins and Charge Radii Beyond $^{48}$Ca.” It is anticipated that the charge radii of these isotopes can show strong evidence for the existence of a sub-shell closure at N=32 and could provide a first tentative investigation into the existence of a shell effect at N=34. Furthermore the proposed experiments will simultaneously provide model-independent measurements of the spins, magnetic moments and quadrupole moments of $^{51,53}$Ca permitting existing and future excitation spectra to be pinned to firm unambiguous ground states.

  19. Table of nuclear root mean square charge radii

    International Nuclear Information System (INIS)

    Angeli, I.

    1999-09-01

    Nuclear root-mean-square (rms) charge radii have been compiled, selected and evaluated using two different procedures: a refined and a simple one. Running them on the same database, the results for weighted average radii are very close to each other: 91% of the differences are within ± 1/2 of the combined error. This confirms our earlier conclusion that the results are generally more sensitive to the decision which data we include in the averaging procedure, and less sensitive to the way how the selected data are weighted to form an average. The resulting weighted averages - updated in May 1999 - are presented in Appendix IV. All background data and program files together with a complete bibliography are also included in the Appendices. In addition to the data tables, simple radius formulae are also given with updated parameters. These formulae may be useful to estimate unmeasured radii ('intelligent interpolation') or to perform analytic calculations with the functions r(A) and r(Z,A). (author)

  20. The 3H–3He Charge Radii Difference

    Directory of Open Access Journals (Sweden)

    Myers L. S.

    2016-01-01

    Full Text Available The upcoming E12-14-009 [1] experiment at Jefferson Lab will determine the ratio of the electric form factors for the A=3 mirror nuclei 3He and 3H. The measurement will use a 1.1 GeV electron beam, a special collimator plate to allow for simultaneous optics measurements, and the low-activity tritium target being prepared for Jefferson Lab. By observing the dependence of the form factor ratio as a function of Q2 over 0.05–0.09 GeV2, the dependence of the radii extraction on the shape of the form factors is minimized. As a result, we anticipate the uncertainty of the extracted charge radii difference to be 0.03 fm, a reduction of 70% from the current measurement. Using precise measurements of the 3He charge radius from isotopic shift or μHe measurements [2–4], we can deduce the absolute 3H charge radius. The results will provide a direct comparison to recent calculations of the charge radii.

  1. Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond

    DEFF Research Database (Denmark)

    Hong, Tao; Pinson, Pierre; Fan, Shu

    2016-01-01

    The energy industry has been going through a significant modernization process over the last decade. Its infrastructure is being upgraded rapidly. The supply, demand and prices are becoming more volatile and less predictable than ever before. Even its business model is being challenged fundamenta...... competition with four tracks on load, price, wind and solar forecasting, which attracted 581 participants from 61 countries. We conclude the paper with 12 predictions for the next decade of energy forecasting.......The energy industry has been going through a significant modernization process over the last decade. Its infrastructure is being upgraded rapidly. The supply, demand and prices are becoming more volatile and less predictable than ever before. Even its business model is being challenged...... fundamentally. In this competitive and dynamic environment, many decision-making processes rely on probabilistic forecasts to quantify the uncertain future. Although most of the papers in the energy forecasting literature focus on point or singlevalued forecasts, the research interest in probabilistic energy...

  2. From wind ensembles to probabilistic information about future wind power production - results from an actual application

    DEFF Research Database (Denmark)

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

    2006-01-01

    horizon we aim at supplying quantiles of the wind power production conditional on the information available at the time at which the forecast is generated. This involves: (i) transformation of meteorological ensemble forecasts into wind power ensemble forecasts and (ii) calculation of quantiles based......Meteorological ensemble forecasts aim at quantifying the uncertainty of the future development of the weather by supplying several possible scenarios of this development. Here we address the use of such scenarios in probabilistic forecasting of wind power production. Specifically, for each forecast...... on the wind power ensemble forecasts. Given measurements of power production, representing a region or a single wind farm, we have developed methods applicable for these two steps. While (ii) should in principle be a simple task we found that the probabilistic information contained in the wind power ensembles...

  3. Drivers of imbalance cost of wind power

    DEFF Research Database (Denmark)

    Obersteiner, C.; Siewierski, T.; Andersen, Anders

    2010-01-01

    In Europe an increasing share of wind power is sold on the power market. Therefore more and more wind power generators become balancing responsible and face imbalance cost that reduce revenues from selling wind power. A comparison of literature illustrates that the imbalance cost of wind power...... varies in a wide range. To explain differences we indentify parameters influencing imbalance cost and compare them for case studies in Austria, Denmark and Poland. Besides the wind power forecast error also the correlation between imbalance and imbalance price influences imbalance cost significantly...... of imperfect forecast is better suited to reflect real cost incurred due to inaccurate wind power forecasts....

  4. 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

  5. Using the WSR-88D to Forecast Downburst Winds at Cape Canaveral Air Station and the Kennedy Space Center (CCAS/KSC)

    National Research Council Canada - National Science Library

    Sullivva, Gerald

    1999-01-01

    .... This technique, the Echo Top/Vertically Integrated Liquid Wind Gust Potential (ET/VIL WGP), has not previously been evaluated for use in the Cape Canaveral Air Station and Kennedy Space Center (CCAS/KSC) locale...

  6. Determining the Radii of Sixteen Transneptunian Bodies Through Thermal Modeling

    Science.gov (United States)

    Brucker, Melissa; Grundy, W. M.; Stansberry, J.; Spencer, J.; Buie, M.; Chiang, E.; Wasserman, L. H.

    2007-10-01

    We will present an analysis of Spitzer Space Telescope observations with the MIPS 24mm and 70mm channels of thermal radiation from 15 Kuiper Belt Objects and one Neptune Trojan. Objects were chosen to explore differences between hot and cold classical KBOs. The observed fluxes, along with the absolute visual magnitude, are input into a program that finds the best fitting radius and albedo using a fast-rotator Standard Thermal Model (STM). We will discuss our extensive comparison of the STM to a rough surface thermophysical model (TPM) in order to estimate the error in radius caused by using the simpler STM. Using a wide range of thermal parameters, the fast-rotator STM more closely approximated the TPM than did the slow-rotator STM. For rotational periods on the order of hours, the radii determined by the fast STM were within 4% of the TPM radii and for a six day period (similar to Pluto) the radii were within 7%. Implementing the STM is advantageous since the specific thermal parameters of KBOs cannot be determined at this time and its computer run time is much faster than the TPM. Monte Carlo techniques have been employed to interpret observations with low signal-to-noise ratios (SNR) on a case-by-case basis and to derive the overall error in radius. Once all objects were analyzed, we looked for trends in radius and albedo with other dynamical properties. Spitzer Space Telescope is operated by the Jet Propulsion Laboratory, California Institute of Technology under NASA contract 1407. This work was supported by NASA through JPL/Caltech Contract Number #1265877.

  7. The inflated radii of M dwarfs in the Pleiades

    Science.gov (United States)

    Jackson, R. J.; Deliyannis, Constantine P.; Jeffries, R. D.

    2018-05-01

    Rotation periods obtained with the Kepler satellite have been combined with precise measurements of projected rotation velocity from the WIYN 3.5-m telescope to determine the distribution of projected radii for several hundred low-mass (0.1 ≤ M/M⊙ ≤ 0.8), fast-rotating members of the Pleiades cluster. A maximum likelihood modelling technique, that takes account of observational uncertainties, selection effects and censored data, and considers the effects of differential rotation and unresolved binarity, has been used to find that the average radius of these stars is 14 ± 2 per cent larger at a given luminosity than predicted by current evolutionary models of Dotter et al. and Baraffe et al. The same models are a reasonable match to the interferometric radii of older, magnetically inactive field M dwarfs, suggesting that the over-radius may be associated with the young, magnetically active nature of the Pleiades objects. No evidence is found for any change in this over-radius above and below the boundary marking the transition to full convection. Published evolutionary models that incorporate either the effects of magnetic inhibition of convection or the blocking of flux by dark star-spots do not individually explain the radius inflation, but a combination of the two effects might. The distribution of projected radii is consistent with the adopted hypothesis of a random spatial orientation of spin axes; strong alignments of the spin vectors into cones with an opening semi-angle <30° can be ruled out. Any plausible but weaker alignment would increase the inferred over-radius.

  8. Calculation of the radii of neutron rich light exotic nuclei

    International Nuclear Information System (INIS)

    Charagi, S.K.; Gupta, S.K.

    1991-01-01

    The interaction cross section of a few unstable neutron rich nuclei have been measured using exotic isotope beams produced through the projectile fragmentation process in high energy heavy-ion collisions. Interaction cross section of He, Li, Be and B isotope projectiles with Be, C and Al targets have thus been measured at 790 MeV/nucleon. We have made a comprehensive analysis of the data on the interaction cross section, to extract the radii of these neutron rich light nuclei. 7 refs., 1 fig., 3 tabs

  9. On the extended and Allan spectra and topological radii

    Directory of Open Access Journals (Sweden)

    Hugo Arizmendi-Peimbert

    2012-01-01

    Full Text Available In this paper we prove that the extended spectrum \\(\\Sigma(x\\, defined by W. Żelazko, of an element \\(x\\ of a pseudo-complete locally convex unital complex algebra \\(A\\ is a subset of the spectrum \\(\\sigma_A(x\\, defined by G.R. Allan. Furthermore, we prove that they coincide when \\(\\Sigma(x\\ is closed. We also establish some order relations between several topological radii of \\(x\\, among which are the topological spectral radius \\(R_t(x\\ and the topological radius of boundedness \\(\\beta_t(x\\.

  10. School Science Inspired by Improving Weather Forecasts

    Science.gov (United States)

    Reid, Heather; Renfrew, Ian A.; Vaughan, Geraint

    2014-01-01

    High winds and heavy rain are regular features of the British weather, and forecasting these events accurately is a major priority for the Met Office and other forecast providers. This is the challenge facing DIAMET, a project involving university groups from Manchester, Leeds, Reading, and East Anglia, together with the Met Office. DIAMET is part…

  11. Recent Advances in Energy Time Series Forecasting

    Directory of Open Access Journals (Sweden)

    Francisco Martínez-Álvarez

    2017-06-01

    Full Text Available This editorial summarizes the performance of the special issue entitled Energy Time Series Forecasting, which was published in MDPI’s Energies journal. The special issue took place in 2016 and accepted a total of 21 papers from twelve different countries. Electrical, solar, or wind energy forecasting were the most analyzed topics, introducing brand new methods with very sound results.

  12. Unexpectedly large charge radii of neutron-rich calcium isotopes

    CERN Document Server

    Garcia Ruiz, R F; Blaum, K; Ekström, A; Frömmgen, N; Hagen, G; Hammen, M; Hebeler, K; Holt, J D; Jansen, G R; Kowalska, M; Kreim, K; Nazarewicz, W; Neugart, R; Neyens, G; Nörtershäuser, W; Papenbrock, T; Papuga, J; Schwenk, A; Simonis, J; Wendt, K A; Yordanov, D T

    2016-01-01

    Despite being a complex many-body system, the atomic nucleus exhibits simple structures for certain ‘magic’ numbers of protons and neutrons. The calcium chain in particular is both unique and puzzling: evidence of doubly magic features are known in 40,48Ca, and recently suggested in two radioactive isotopes, 52,54Ca. Although many properties of experimentally known calcium isotopes have been successfully described by nuclear theory, it is still a challenge to predict the evolution of their charge radii. Here we present the first measurements of the charge radii of 49,51,52Ca, obtained from laser spectroscopy experiments at ISOLDE, CERN. The experimental results are complemented by state-of-the-art theoretical calculations. The large and unexpected increase of the size of the neutron-rich calcium isotopes beyond N = 28 challenges the doubly magic nature of 52Ca and opens new intriguing questions on the evolution of nuclear sizes away from stability, which are of importance for our understanding of neutron-...

  13. Charge radii of neon isotopes across the sd neutron shell

    CERN Document Server

    Marinova, K; Kowalska, M; Kotrotsios, G; Kloos, S; Neugart, R; Blaum, K; Simon, H; Keim, M; Lievens, P; Wilbert, S; Kappertz, S

    2011-01-01

    We report on the changes in mean square charge radii of unstable neon nuclei relative to the stable (20)Ne, based on the measurement of optical isotope shifts. The studies were carried out using collinear laser spectroscopy on a fast beam of neutral neon atoms. High sensitivity on short-lived isotopes was achieved thanks to nonoptical detection based on optical pumping and state-selective collisional ionization, which was complemented by an accurate determination of the beam kinetic energy. The new results provide information on the structural changes in the sequence of neon isotopes all across the neutron sd shell, ranging from the proton drip line nucleus and halo candidate (17)Ne up to the neutron-rich (28)Ne in the vicinity of the ``island of inversion.{''} Within this range the charge radius is smallest for (24)Ne with N = 14 corresponding to the closure of the neutron d(5/2) shell, while it increases toward both neutron shell closures, N = 8 and N = 20. The general trend of the charge radii correlates w...

  14. Alfvenic Turbulence from the Sun to 65 Solar Radii: Numerical predictions.

    Science.gov (United States)

    Perez, J. C.; Chandran, B. D. G.

    2015-12-01

    The upcoming NASA Solar Probe Plus (SPP) mission will fly to within 9 solar radii from the solar surface, about 7 times closer to the Sun than any previous spacecraft has ever reached. This historic mission will gather unprecedented remote-sensing data and the first in-situ measurements of the plasma in the solar atmosphere, which will revolutionize our knowledge and understanding of turbulence and other processes that heat the solar corona and accelerate the solar wind. This close to the Sun the background solar-wind properties are highly inhomogeneous. As a result, outward-propagating Alfven waves (AWs) arising from the random motions of the photospheric magnetic-field footpoints undergo strong non-WKB reflections and trigger a vigorous turbulent cascade. In this talk I will discuss recent progress in the understanding of reflection-driven Alfven turbulence in this scenario by means of high-resolution numerical simulations, with the goal of predicting the detailed nature of the velocity and magnetic field fluctuations that the SPP mission will measure. In particular, I will place special emphasis on relating the simulations to relevant physical mechanisms that might govern the radial evolution of the turbulence spectra of outward/inward-propagating fluctuations and discuss the conditions that lead to universal power-laws.

  15. Using C-Band Dual-Polarization Radar Signatures to Improve Convective Wind Forecasting at Cape Canaveral Air Force Station and NASA Kennedy Space Center

    Science.gov (United States)

    Amiot, Corey G.; Carey, Lawrence D.; Roeder, William P.; McNamara, Todd M.; Blakeslee, Richard J.

    2017-01-01

    The United States Air Force's 45th Weather Squadron (45WS) is the organization responsible for monitoring atmospheric conditions at Cape Canaveral Air Force Station and NASA Kennedy Space Center (CCAFS/KSC) and issuing warnings for hazardous weather conditions when the need arises. One such warning is issued for convective wind events, for which lead times of 30 and 60 minutes are desired for events with peak wind gusts of 35 knots or greater (i.e., Threshold-1) and 50 knots or greater (i.e., Threshold-2), respectively (Roeder et al. 2014).

  16. 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.

  17. 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...... is illustrated with data from the Horns Rev 1 wind farm, located in the North Sea. To conclude, an overview of ongoing and future research in the Radar@Sea experiment is given. This experiment aims at improving offshore wind power predictability and controllability through the increased use of meteorological...... information, and particularly weather radar images....

  18. A new perspective on charge radii around Z = 82

    Energy Technology Data Exchange (ETDEWEB)

    Cocolios, T. E., E-mail: thomas.cocolios@kuleuven.be [KU Leuven, Instituut voor Kern- en Stralingsfysica (Belgium)

    2017-11-15

    In the last 35 years, a large amount of data on the changes in the mean-square charge radii, δ〈r{sup 2}〉, around the lead region has been gathered. Isotopic chains are often normalised and compared to reduce the impact of systematic uncertainties of the extracted δ〈r{sup 2}〉 from the isotope shifts. However, this biased picture can obscure other interesting effects that are apparent in absolute scale. In this contribution, we review the extent of the knowledge on the δ〈r{sup 2}〉 in the lead region in addition to observations on the absolute scale.

  19. Measurement of nuclear moments and radii by collinear laser spectroscopy

    CERN Multimedia

    Geithner, W R; Lievens, P; Kotrotsios, G; Silverans, R; Kappertz, S

    2002-01-01

    %IS304 %title\\\\ \\\\Collinear laser spectroscopy on a fast beam has proven to be a widely applicable and very efficient tool for measurements of changes in mean square nuclear charge radii, nuclear spins, magnetic dipole and electric quadrupole moments. Recent developments of extremely sensitive non-optical detection schemes enabled for some elements the extension of the measurements towards the very short-lived isotopes in the far wings of the ISOLDE production curves. The gain in sensitivity opens up new perspectives, particularly for measurements on lighter nuclei whose ground-state properties can be interpreted by large scale microscopic calculations instead of the more phenomenologic models used for heavier nuclei.\\\\ \\\\ For the sequence of argon isotopes $^{32-40}$Ar and $^{46}$Ar isotope shifts and nuclear moments were measured by optical pumping followed by state selective collisional ionization and detection of the $\\beta$-decay. Similarly, the low-background $\\alpha$-detection was used to extend earlie...

  20. Hypergravity effects on normal and avulsed developing avian radii

    Science.gov (United States)

    Negulesco, J. A.; Clark, D. L.

    1976-01-01

    Rhode Island red female chicks were subjected to complete closed fracture of the right radius at 2 weeks post-hatching. The animals were allowed to heal for 1 week at either earth-gravity or 2-G-hypergravity state with control and estrogen-injected groups. Intact and fractured radial length, weight, average epiphysial-diaphysial diameters, and length, width, and weight of healing fracture callus were measured. Daily 2000 IU estrogen administration for 7 d increased intact radial length. Estrogen augments the effects of the 2-G state by inhibiting growth and depleting the mass of both intact and fractured radii and by decreasing the average distal epiphysial diameter of fractured bones. Animals exposed to the hypergravity state without hormonal treatment showed decreased fractured radial length, weight, and smaller proximal epiphysial diameters. The measurable parameters of the fracture callus (width, length, and weight) were depressed by the hypergravity state regardless of whether the animal was untreated or supplemented with estrogen.

  1. Strategic Forecasting

    DEFF Research Database (Denmark)

    Duus, Henrik Johannsen

    2016-01-01

    as a rebirth of long range planning, albeit with new methods and theories. Firms should make the building of strategic forecasting capability a priority. Research limitations/implications: The article subdivides strategic forecasting into three research avenues and suggests avenues for further research efforts......Purpose: The purpose of this article is to present an overview of the area of strategic forecasting and its research directions and to put forward some ideas for improving management decisions. Design/methodology/approach: This article is conceptual but also informed by the author’s long contact...... and collaboration with various business firms. It starts by presenting an overview of the area and argues that the area is as much a way of thinking as a toolbox of theories and methodologies. It then spells out a number of research directions and ideas for management. Findings: Strategic forecasting is seen...

  2. Exposure Forecaster

    Data.gov (United States)

    U.S. Environmental Protection Agency — The Exposure Forecaster Database (ExpoCastDB) is EPA's database for aggregating chemical exposure information and can be used to help with chemical exposure...

  3. Probabilistic Forecasting of the Wave Energy Flux

    DEFF Research Database (Denmark)

    Pinson, Pierre; Reikard, G.; Bidlot, J.-R.

    2012-01-01

    Wave energy will certainly have a significant role to play in the deployment of renewable energy generation capacities. As with wind and solar, probabilistic forecasts of wave power over horizons of a few hours to a few days are required for power system operation as well as trading in electricity...... markets. A methodology for the probabilistic forecasting of the wave energy flux is introduced, based on a log-Normal assumption for the shape of predictive densities. It uses meteorological forecasts (from the European Centre for Medium-range Weather Forecasts – ECMWF) and local wave measurements...

  4. Overview and Meteorological Validation of the Wind Integration National Dataset toolkit

    Energy Technology Data Exchange (ETDEWEB)

    Draxl, C. [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Hodge, B. M. [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Clifton, A. [National Renewable Energy Laboratory (NREL), Golden, CO (United States); McCaa, J. [3TIER by VAisala, Seattle, WA (United States)

    2015-04-13

    The Wind Integration National Dataset (WIND) Toolkit described in this report fulfills these requirements, and constitutes a state-of-the-art national wind resource data set covering the contiguous United States from 2007 to 2013 for use in a variety of next-generation wind integration analyses and wind power planning. The toolkit is a wind resource data set, wind forecast data set, and wind power production and forecast data set derived from the Weather Research and Forecasting (WRF) numerical weather prediction model. WIND Toolkit data are available online for over 116,000 land-based and 10,000 offshore sites representing existing and potential wind facilities.

  5. The impact of wind power on electricity prices

    Energy Technology Data Exchange (ETDEWEB)

    Brancucci Martinez-Anido, Carlo; Brinkman, Greg; Hodge, Bri-Mathias

    2016-08-01

    This paper investigates the impact of wind power on electricity prices using a production cost model of the Independent System Operator - New England power system. Different scenarios in terms of wind penetration, wind forecasts, and wind curtailment are modeled in order to analyze the impact of wind power on electricity prices for different wind penetration levels and for different levels of wind power visibility and controllability. The analysis concludes that electricity price volatility increases even as electricity prices decrease with increasing wind penetration levels. The impact of wind power on price volatility is larger in the shorter term (5-min compared to hour-to-hour). The results presented show that over-forecasting wind power increases electricity prices while under-forecasting wind power reduces them. The modeling results also show that controlling wind power by allowing curtailment increases electricity prices, and for higher wind penetrations it also reduces their volatility.

  6. Mapping 3D plasma structure in the solar wind with the L1 constellation: joint observations from Wind, ACE, DSCOVR, and SoHO

    Science.gov (United States)

    Stevens, M. L.; Kasper, J. C.; Case, A. W.; Korreck, K. E.; Szabo, A.; Biesecker, D. A.; Prchlik, J.

    2017-12-01

    At this moment in time, four observatories with similar instrumentation- Wind, ACE, DSCOVR, and SoHO- are stationed directly upstream of the Earth and making continuous observations. They are separated by drift-time baselines of seconds to minutes, timescales on which MHD instabilities in the solar wind are known to grow and evolve, and spatial baselines of tens to 200 earth radii, length scales relevant to the Earth's magnetosphere. By comparing measurements of matched solar wind structures from the four vantage points, the form of structures and associated dynamics on these scales is illuminated. Our targets include shocks and MHD discontinuities, stream fronts, locii of reconnection and exhaust flow boundary layers, plasmoids, and solitary structures born of nonlinear instability. We use the tetrahedral quality factors and other conventions adopted for Cluster to identify periods where the WADS constellation is suitably non-degenerate and arranged in such a way as to enable specific types of spatial, temporal, or spatiotemporal inferences. We present here an overview of the geometries accessible to the L1 constellation and timing-based and plasma-based observations of solar wind structures from 2016-17. We discuss the unique potential of the constellation approach for space physics and space weather forecasting at 1 AU.

  7. 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...

  8. Empirical investigation on using wind speed volatility to estimate the operation probability and power output of wind turbines

    International Nuclear Information System (INIS)

    Liu, Heping; Shi, Jing; Qu, Xiuli

    2013-01-01

    Highlights: ► Ten-minute wind speed and power generation data of an offshore wind turbine are used. ► An ARMA–GARCH-M model is built to simultaneously forecast wind speed mean and volatility. ► The operation probability and expected power output of the wind turbine are predicted. ► The integrated approach produces more accurate wind power forecasting than other conventional methods. - Abstract: In this paper, we introduce a quantitative methodology that performs the interval estimation of wind speed, calculates the operation probability of wind turbine, and forecasts the wind power output. The technological advantage of this methodology stems from the empowered capability of mean and volatility forecasting of wind speed. Based on the real wind speed and corresponding wind power output data from an offshore wind turbine, this methodology is applied to build an ARMA–GARCH-M model for wind speed forecasting, and then to compute the operation probability and the expected power output of the wind turbine. The results show that the developed methodology is effective, the obtained interval estimation of wind speed is reliable, and the forecasted operation probability and expected wind power output of the wind turbine are accurate

  9. Small radii of neutron stars as an indication of novel in-medium effects

    International Nuclear Information System (INIS)

    Jiang, Wei-Zhou; Li, Bao-An; Fattoyev, F.J.

    2015-01-01

    At present, neutron star radii from both observations and model predictions remain very uncertain. Whereas different models can predict a wide range of neutron star radii, it is not possible for most models to predict radii that are smaller than about 10 km, thus if such small radii are established in the future they will be very difficult to reconcile with model estimates. By invoking a new term in the equation of state that enhances the energy density, but leaves the pressure unchanged we simulate the current uncertainty in the neutron star radii. This new term can be possibly due to the exchange of the weakly interacting light U-boson with appropriate in-medium parameters, which does not compromise the success of the conventional nuclear models. The validity of this new scheme will be tested eventually by more precise measurements of neutron star radii. (orig.)

  10. Baade-Wesselink radii for NGC 1866 Cepheids

    Science.gov (United States)

    Cote, Patrick; Welch, Douglas L.; Fischer, Philippe; Mateo, Mario; Madore, Barry F.

    1991-01-01

    Radial velocities and CCD photometry suitable for a Baade-Wesselink analysis are now available for six short-period Cepheids (HV12198, HV12199, HV12202, HV12203, HV12204, and V4) in the LMC cluster NGC 1866. As a prerequisite for such an analysis, O-C diagrams have been constructed and examined for seven of the NGC 1866 Cepheids (HV12197, HV12198, HV12199, HV12200, HV12202, HV12203, and HV12204), yielding improved periods and estimates of the rate of period change in these coeval variables. One star, HV12198, shows possible evidence for period change, but the phase mismatch due to that change is small enough to have negligible effect on its Baade-Wesselink radius. The computed radii are in good agreement with the Baade-Wesselink, theoretical and cluster/association period-radius relations of Fernie (1984) and the Galactic relation of Coulson and Caldwell (1989). The Flower (1977) color-effective temperature relation and the Becker et al. (1977) mass-luminosity relation lead to mean masses of 4.9 + or - 0.5 solar masses and 4.6 + or - 0.5 solar masses for the respective metallicities of (Y,Z) = (0.0273,0.0016) and (0.026,0.02). Pulsation constants are also computed for these stars.

  11. Rotating neutron stars with exotic cores: masses, radii, stability

    Energy Technology Data Exchange (ETDEWEB)

    Haensel, P.; Bejger, M.; Fortin, M.; Zdunik, L. [Polish Academy of Sciences, N. Copernicus Astronomical Center, Warszawa (Poland)

    2016-03-15

    A set of theoretical mass-radius relations for rigidly rotating neutron stars with exotic cores, obtained in various theories of dense matter, is reviewed. Two basic observational constraints are used: the largest measured rotation frequency (716Hz) and the maximum measured mass (2M {sub CircleDot}). The present status of measuring the radii of neutron stars is described. The theory of rigidly rotating stars in general relativity is reviewed and limitations of the slow rotation approximation are pointed out. Mass-radius relations for rotating neutron stars with hyperon and quark cores are illustrated using several models. Problems related to the non-uniqueness of the crust-core matching are mentioned. Limits on rigid rotation resulting from the mass-shedding instability and the instability with respect to the axisymmetric perturbations are summarized. The problem of instabilities and of the back-bending phenomenon are discussed in detail. Metastability and instability of a neutron star core in the case of a first-order phase transition, both between pure phases, and into a mixed-phase state, are reviewed. The case of two disjoint families (branches) of rotating neutron stars is discussed and generic features of neutron-star families and of core-quakes triggered by the instabilities are considered. (orig.)

  12. Uniformity of cylindrical imploding underwater shockwaves at very small radii

    Science.gov (United States)

    Yanuka, D.; Rososhek, A.; Bland, S. N.; Krasik, Ya. E.

    2017-11-01

    We compare the convergent shockwaves generated from underwater, cylindrical arrays of copper wire exploded by multiple kilo-ampere current pulses on nanosecond and microsecond scales. In both cases, the pulsed power devices used for the experiments had the same stored energy (˜500 J) and the wire mass was adjusted to optimize energy transfer to the shockwave. Laser backlit framing images of the shock front were achieved down to the radius of 30 μm. It was found that even in the case of initial azimuthal non-symmetry, the shock wave self-repairs in the final stages of its motion, leading to a highly uniform implosion. In both these and previous experiments, interference fringes have been observed in streak and framing images as the shockwave approached the axis. We have been able to accurately model the origin of the fringes, which is due to the propagation of the laser beam diffracting off the uniform converging shock front. The dynamics of the shockwave and its uniformity at small radii indicate that even with only 500 J stored energies, this technique should produce pressures above 1010 Pa on the axis, with temperatures and densities ideal for warm dense matter research.

  13. MEASURING NEUTRON STAR RADII VIA PULSE PROFILE MODELING WITH NICER

    Energy Technology Data Exchange (ETDEWEB)

    Özel, Feryal; Psaltis, Dimitrios; Bauböck, Michi [Astronomy Department, University of Arizona, 933 N. Cherry Ave., Tucson, AZ 85721 (United States); Arzoumanian, Zaven [Center for Research and Exploration in Space Science and Technology/USRA, NASA Goddard Space Flight Center, Code 662, Greenbelt, MD 20771 (United States); Morsink, Sharon, E-mail: fozel@email.arizona.edu [Department of Physics, University of Alberta, 11455 Saskatchewan Drive, Edmonton, AB T6G 2E1 (Canada)

    2016-11-20

    The Neutron-star Interior Composition Explorer is an X-ray astrophysics payload that will be placed on the International Space Station . Its primary science goal is to measure with high accuracy the pulse profiles that arise from the non-uniform thermal surface emission of rotation-powered pulsars. Modeling general relativistic effects on the profiles will lead to measuring the radii of these neutron stars and to constraining their equation of state. Achieving this goal will depend, among other things, on accurate knowledge of the source, sky, and instrument backgrounds. We use here simple analytic estimates to quantify the level at which these backgrounds need to be known in order for the upcoming measurements to provide significant constraints on the properties of neutron stars. We show that, even in the minimal-information scenario, knowledge of the background at a few percent level for a background-to-source countrate ratio of 0.2 allows for a measurement of the neutron star compactness to better than 10% uncertainty for most of the parameter space. These constraints improve further when more realistic assumptions are made about the neutron star emission and spin, and when additional information about the source itself, such as its mass or distance, are incorporated.

  14. The Wind Integration National Dataset (WIND) toolkit (Presentation)

    Energy Technology Data Exchange (ETDEWEB)

    Caroline Draxl: NREL

    2014-01-01

    Regional wind integration studies require detailed wind power output data at many locations to perform simulations of how the power system will operate under high penetration scenarios. The wind datasets that serve as inputs into the study must realistically reflect the ramping characteristics, spatial and temporal correlations, and capacity factors of the simulated wind plants, as well as being time synchronized with available load profiles.As described in this presentation, the WIND Toolkit fulfills these requirements by providing a state-of-the-art national (US) wind resource, power production and forecast dataset.

  15. Selection of Models for Ingestion Pathway and Relocation Radii Determination

    International Nuclear Information System (INIS)

    Blanchard, A.

    1998-01-01

    The distance at which intermediate phase protective actions (such as food interdiction and relocation) may be needed following postulated accidents at three Savannah River Site nonreactor nuclear facilities will be determined by modeling. The criteria used to select dispersion/deposition models are presented. Several models were considered, including ARAC, MACCS, HOTSPOT, WINDS (coupled with PUFF-PLUME), and UFOTRI. Although ARAC and WINDS are expected to provide more accurate modeling of atmospheric transport following an actual release, analyses consistent with regulatory guidance for planning purposes may be accomplished with comparatively simple dispersion models such as HOTSPOT and UFOTRI. A recommendation is made to use HOTSPOT for non-tritium facilities and UFOTRI for tritium facilities

  16. Wind Power Prediction using Ensembles

    DEFF Research Database (Denmark)

    Giebel, Gregor; Badger, Jake; Landberg, Lars

    2005-01-01

    offshore wind farm and the whole Jutland/Funen area. The utilities used these forecasts for maintenance planning, fuel consumption estimates and over-the-weekend trading on the Leipzig power exchange. Othernotable scientific results include the better accuracy of forecasts made up from a simple...... superposition of two NWP provider (in our case, DMI and DWD), an investigation of the merits of a parameterisation of the turbulent kinetic energy within thedelivered wind speed forecasts, and the finding that a “naïve” downscaling of each of the coarse ECMWF ensemble members with higher resolution HIRLAM did...

  17. The Observational and Theoretical Tidal Radii of Globular Clusters in M87

    Science.gov (United States)

    Webb, Jeremy J.; Sills, Alison; Harris, William E.

    2012-02-01

    Globular clusters have linear sizes (tidal radii) which theory tells us are determined by their masses and by the gravitational potential of their host galaxy. To explore the relationship between observed and expected radii, we utilize the globular cluster population of the Virgo giant M87. Unusually deep, high signal-to-noise images of M87 are used to measure the effective and limiting radii of approximately 2000 globular clusters. To compare with these observations, we simulate a globular cluster population that has the same characteristics as the observed M87 cluster population. Placing these simulated clusters in the well-studied tidal field of M87, the orbit of each cluster is solved and the theoretical tidal radius of each cluster is determined. We compare the predicted relationship between cluster size and projected galactocentric distance to observations. We find that for an isotropic distribution of cluster velocities, theoretical tidal radii are approximately equal to observed limiting radii for R gc < 10 kpc. However, the isotropic simulation predicts a steep increase in cluster size at larger radii, which is not observed in large galaxies beyond the Milky Way. To minimize the discrepancy between theory and observations, we explore the effects of orbital anisotropy on cluster sizes, and suggest a possible orbital anisotropy profile for M87 which yields a better match between theory and observations. Finally, we suggest future studies which will establish a stronger link between theoretical tidal radii and observed radii.

  18. THE OBSERVATIONAL AND THEORETICAL TIDAL RADII OF GLOBULAR CLUSTERS IN M87

    International Nuclear Information System (INIS)

    Webb, Jeremy J.; Sills, Alison; Harris, William E.

    2012-01-01

    Globular clusters have linear sizes (tidal radii) which theory tells us are determined by their masses and by the gravitational potential of their host galaxy. To explore the relationship between observed and expected radii, we utilize the globular cluster population of the Virgo giant M87. Unusually deep, high signal-to-noise images of M87 are used to measure the effective and limiting radii of approximately 2000 globular clusters. To compare with these observations, we simulate a globular cluster population that has the same characteristics as the observed M87 cluster population. Placing these simulated clusters in the well-studied tidal field of M87, the orbit of each cluster is solved and the theoretical tidal radius of each cluster is determined. We compare the predicted relationship between cluster size and projected galactocentric distance to observations. We find that for an isotropic distribution of cluster velocities, theoretical tidal radii are approximately equal to observed limiting radii for R gc < 10 kpc. However, the isotropic simulation predicts a steep increase in cluster size at larger radii, which is not observed in large galaxies beyond the Milky Way. To minimize the discrepancy between theory and observations, we explore the effects of orbital anisotropy on cluster sizes, and suggest a possible orbital anisotropy profile for M87 which yields a better match between theory and observations. Finally, we suggest future studies which will establish a stronger link between theoretical tidal radii and observed radii.

  19. Operational models for forecasting Dst

    Science.gov (United States)

    Watanabe, S.; Sagawa, E.; Ohtaka, K.; Shimazu, H.

    We have constructed operational models for forecasting the geomagnetic storm index (Dst) two hours in advance from six parameters: the velocity and density of the solar wind, the magnitude of the interplanetary magnetic field (IMF), and the x, y, and z components of the IMF. Our models use an Elman-type neural network, and we forecast space weather by using real-time solar-wind data from the Advanced Composition Explorer spacecraft.The models have worked well since April of 1998 and the Dst values forecast using them have been made available to the public at http://www.crl.go.jp/uk/uk223/service/nnw/index.html. From February to October 1998 there were 11 storms with minimum Dst values below -80 nT, and for ten the difference between the forecast minimum Dst and the Dst calculated from data measured by ground stations was less than 23%.For the storm starting on 19 October, however, the difference was 40% because of the weak correlation between the ACE environment and the earth's environment during this event.The Dst depends on the orientation of the IMF relative to the solar magnetospheric x-y plane and seems to be relatively large when the y component of the IMF is positive and perhaps also when the x component is positive.

  20. Wind Resource Assessment of Gujarat (India)

    Energy Technology Data Exchange (ETDEWEB)

    Draxl, C.; Purkayastha, A.; Parker, Z.

    2014-07-01

    India is one of the largest wind energy markets in the world. In 1986 Gujarat was the first Indian state to install a wind power project. In February 2013, the installed wind capacity in Gujarat was 3,093 MW. Due to the uncertainty around existing wind energy assessments in India, this analysis uses the Weather Research and Forecasting (WRF) model to simulate the wind at current hub heights for one year to provide more precise estimates of wind resources in Gujarat. The WRF model allows for accurate simulations of winds near the surface and at heights important for wind energy purposes. While previous resource assessments published wind power density, we focus on average wind speeds, which can be converted to wind power densities by the user with methods of their choice. The wind resource estimates in this study show regions with average annual wind speeds of more than 8 m/s.

  1. Using Seasonal Forecasting Data for Vessel Routing

    Science.gov (United States)

    Bell, Ray; Kirtman, Ben

    2017-04-01

    We present an assessment of seasonal forecasting of surface wind speed, significant wave height and ocean surface current speed in the North Pacific for potential use of vessel routing from Singapore to San Diego. WaveWatchIII is forced with surface winds and ocean surface currents from the Community Climate System Model 4 (CCSM4) retrospective forecasts for the period of 1982-2015. Several lead time forecasts are used from zero months to six months resulting in 2,720 model years, ensuring the findings from this study are robust. July surface wind speed and significant wave height can be skillfully forecast with a one month lead time, with the western North Pacific being the most predictable region. Beyond May initial conditions (lead time of two months) the El Niño Southern Oscillation (ENSO) Spring predictability barrier limits skill of significant wave height but there is skill for surface wind speed with January initial conditions (lead time of six months). In a separate study of vessel routing between Norfolk, Virginia and Gibraltar we demonstrate the benefit of a multimodel approach using the North American Multimodel Ensemble (NMME). In collaboration with Charles River Analytics an all-encompassing forecast is presented by using machine learning on the various ensembles which can be using used for industry applications.

  2. Short-term heat load forecasting for single family houses

    DEFF Research Database (Denmark)

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

    2013-01-01

    This paper presents a method for forecasting the load for space heating in a single-family house. The forecasting model is built using data from sixteen houses located in Sønderborg, Denmark, combined with local climate measurements and weather forecasts. Every hour the hourly heat load for each...... house the following two days is forecasted. The forecast models are adaptive linear time-series models and the climate inputs used are: ambient temperature, global radiation and wind speed. A computationally efficient recursive least squares scheme is used. The models are optimized to fit the individual...

  3. Toward a global description of nuclear charge radii: Exploring the Fayans energy density functional

    Science.gov (United States)

    Reinhard, P.-G.; Nazarewicz, W.

    2017-06-01

    Background: Binding energies and charge radii are fundamental properties of atomic nuclei. When inspecting their particle-number dependence, both quantities exhibit pronounced odd-even staggering. While the odd-even effect in binding energy can be attributed to nucleonic pairing, the origin of staggering in charge radii is less straightforward to ascertain. Purpose: In this work, we study the odd-even effect in binding energies and charge radii, and systematic behavior of differential radii, to identify the underlying components of the effective nuclear interaction. Method: We apply nuclear density functional theory using a family of Fayans and Skyrme energy density functionals fitted to similar data sets but using different optimization protocols. We inspect various correlations between differential charge radii, odd-even staggering in energies and radii, and nuclear matter properties. The Fayans functional is assumed to be in the local FaNDF0 form. Detailed analysis is carried out for medium-mass and heavy semimagic nuclei with a particular focus on the Ca chain. Results: By making the surface and pairing terms dependent on density gradients, the Fayans functional offers the superb simultaneous description of odd-even staggering effects in energies and charge radii. Conversely, when the data on differential radii are added to the pool of fit observables, the coupling constants determining the strengths of the gradient terms of Fayans functional are increased by orders of magnitude. The Skyrme functional optimized in this work with the generalized Fayans pairing term offers results of similar quality. We quantify these findings by performing correlation analysis based on the statistical linear regression technique. The nuclear matter parameters characterizing Fayans and Skyrme functionals optimized to similar data sets are fairly close. Conclusion: The Fayans paring functional, with its generalized density dependence, significantly improves the description of

  4. Weather forecasting based on hybrid neural model

    Science.gov (United States)

    Saba, Tanzila; Rehman, Amjad; AlGhamdi, Jarallah S.

    2017-11-01

    Making deductions and expectations about climate has been a challenge all through mankind's history. Challenges with exact meteorological directions assist to foresee and handle problems well in time. Different strategies have been investigated using various machine learning techniques in reported forecasting systems. Current research investigates climate as a major challenge for machine information mining and deduction. Accordingly, this paper presents a hybrid neural model (MLP and RBF) to enhance the accuracy of weather forecasting. Proposed hybrid model ensure precise forecasting due to the specialty of climate anticipating frameworks. The study concentrates on the data representing Saudi Arabia weather forecasting. The main input features employed to train individual and hybrid neural networks that include average dew point, minimum temperature, maximum temperature, mean temperature, average relative moistness, precipitation, normal wind speed, high wind speed and average cloudiness. The output layer composed of two neurons to represent rainy and dry weathers. Moreover, trial and error approach is adopted to select an appropriate number of inputs to the hybrid neural network. Correlation coefficient, RMSE and scatter index are the standard yard sticks adopted for forecast accuracy measurement. On individual standing MLP forecasting results are better than RBF, however, the proposed simplified hybrid neural model comes out with better forecasting accuracy as compared to both individual networks. Additionally, results are better than reported in the state of art, using a simple neural structure that reduces training time and complexity.

  5. Advancements in Wind Integration Study Data Modeling: The Wind Integration National Dataset (WIND) Toolkit; Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Draxl, C.; Hodge, B. M.; Orwig, K.; Jones, W.; Searight, K.; Getman, D.; Harrold, S.; McCaa, J.; Cline, J.; Clark, C.

    2013-10-01

    Regional wind integration studies in the United States require detailed wind power output data at many locations to perform simulations of how the power system will operate under high-penetration scenarios. The wind data sets that serve as inputs into the study must realistically reflect the ramping characteristics, spatial and temporal correlations, and capacity factors of the simulated wind plants, as well as be time synchronized with available load profiles. The Wind Integration National Dataset (WIND) Toolkit described in this paper fulfills these requirements. A wind resource dataset, wind power production time series, and simulated forecasts from a numerical weather prediction model run on a nationwide 2-km grid at 5-min resolution will be made publicly available for more than 110,000 onshore and offshore wind power production sites.

  6. Wave modelling for the North Indian Ocean using MSMR analysed winds

    Digital Repository Service at National Institute of Oceanography (India)

    Vethamony, P.; Sudheesh, K.; Rupali, S.P.; Babu, M.T.; Jayakumar, S.; Saran, A.K.; Basu, S.K.; Kumar, R.; Sarkar, A.

    NCMRWF (National Centre for Medium Range Weather Forecast) winds assimilated with MSMR (Multi-channel Scanning Microwave Radiometer) winds are used as input to MIKE21 Offshore Spectral Wave model (OSW) which takes into account wind induced wave...

  7. Trend chart: wind power. Third quarter 2016

    International Nuclear Information System (INIS)

    Reynaud, Didier

    2016-11-01

    This publication presents the wind energy situation of continental France and overseas territories during the third quarter 2016: total connected load, new connected facilities, regional distribution of wind power production, evolution of quarterly production, distribution of facilities versus power, evolution forecasts of the French wind power park, projects in progress, detailed regional results, methodology used

  8. Trend chart: wind power. Second quarter 2016

    International Nuclear Information System (INIS)

    Reynaud, Didier

    2016-08-01

    This publication presents the wind energy situation of continental France and overseas territories during the second quarter 2016: total connected load, new connected facilities, regional distribution of wind power production, evolution of quarterly production, distribution of facilities versus power, evolution forecasts of the French wind power park, projects in progress, detailed regional results, methodology used

  9. Trend chart: wind power. First quarter 2017

    International Nuclear Information System (INIS)

    2017-05-01

    This publication presents the wind energy situation of continental France and overseas territories during the first quarter 2017: total connected load, new connected facilities, regional distribution of wind power production, evolution of quarterly production, distribution of facilities versus power, evolution forecasts of the French wind power park, projects in progress, detailed regional results, methodology used

  10. Trend chart: wind power. First quarter 2016

    International Nuclear Information System (INIS)

    Reynaud, Didier

    2016-05-01

    This publication presents the wind energy situation of continental France and overseas territories during the first quarter 2016: total connected load, new connected facilities, regional distribution of wind power production, evolution of quarterly production, distribution of facilities versus power, evolution forecasts of the French wind power park, projects in progress, detailed regional results, methodology used

  11. Trend chart: wind power. Third quarter 2015

    International Nuclear Information System (INIS)

    Reynaud, Didier

    2015-11-01

    This publication presents the wind energy situation of continental France and overseas territories during the third quarter 2015: total connected load, new connected facilities, regional distribution of wind power production, evolution of quarterly production, distribution of facilities versus power, evolution forecasts of the French wind power park, projects in progress, detailed regional results, methodology used

  12. Trend chart: wind power. Forth quarter 2015

    International Nuclear Information System (INIS)

    Reynaud, Didier

    2016-02-01

    This publication presents the wind energy situation of continental France and overseas territories during the forth quarter 2015: total connected load, new connected facilities, regional distribution of wind power production, evolution of quarterly production, distribution of facilities versus power, evolution forecasts of the French wind power park, projects in progress, detailed regional results, methodology used

  13. Trend chart: wind power. Forth quarter 2016

    International Nuclear Information System (INIS)

    Coltier, Yves

    2017-02-01

    This publication presents the wind energy situation of continental France and overseas territories during the forth quarter 2016: total connected load, new connected facilities, regional distribution of wind power production, evolution of quarterly production, distribution of facilities versus power, evolution forecasts of the French wind power park, projects in progress, detailed regional results, methodology used

  14. Trend chart: wind power. Fourth quarter 2017

    International Nuclear Information System (INIS)

    Moreau, Sylvain

    2018-02-01

    This publication presents the wind energy situation of continental France and overseas territories during the fourth quarter 2017: total connected load, new connected facilities, regional distribution of wind power production, evolution of quarterly production, distribution of facilities versus power, evolution forecasts of the French wind power park, projects in progress, detailed regional results, revision of results

  15. Trend chart: wind power. Third quarter 2017

    International Nuclear Information System (INIS)

    2017-11-01

    This publication presents the wind energy situation of continental France and overseas territories during the third quarter 2017: total connected load, new connected facilities, regional distribution of wind power production, evolution of quarterly production, distribution of facilities versus power, evolution forecasts of the French wind power park, projects in progress, detailed regional results, revision of results

  16. Trend chart: wind power. Second quarter 2017

    International Nuclear Information System (INIS)

    2017-08-01

    This publication presents the wind energy situation of continental France and overseas territories during the second quarter 2017: total connected load, new connected facilities, regional distribution of wind power production, evolution of quarterly production, distribution of facilities versus power, evolution forecasts of the French wind power park, projects in progress, detailed regional results, revision of results

  17. Index Bioclimatic "Wind-Chill"

    Directory of Open Access Journals (Sweden)

    Teodoreanu Elena

    2015-05-01

    Full Text Available This paper presents an important bioclimatic index which shows the influence of wind on the human body thermoregulation. When the air temperature is high, the wind increases thermal comfort. But more important for the body is the wind when the air temperature is low. When the air temperature is lower and wind speed higher, the human body is threatening to freeze faster. Cold wind index is used in Canada, USA, Russia (temperature "equivalent" to the facial skin etc., in the weather forecast every day in the cold season. The index can be used and for bioclimatic regionalization, in the form of skin temperature index.

  18. Doppler Wind Lidar Measurements and Scalability to Space

    Data.gov (United States)

    National Aeronautics and Space Administration — Global measurements of wind speed and direction from Doppler wind lidars, if available, would significantly improve forecasting of severe weather events such as...

  19. Interaction radii of proton-rich radioactive nuclei at A=60-80

    International Nuclear Information System (INIS)

    Lima, G. F.; Lepine-Szily, A.; Lichtenthaler, R.; Villari, A. C. C.; Mittig, W.; Casandjian, J. M.; Lewitowicz, M.; Chartier, M.; Hirata, D.; Angelique, J. C.; Orr, N. A.; Audi, G.; Cunsolo, A.; Foti, A.; Donzeaud, C.; MacCormick, M.; Stephan, C.; Suomijarvi, T.; Tassan-Got, L.; Gillibert, A.

    1998-01-01

    The interaction radii of proton-rich, radioactive 31 Ga, 32 Ge, 33 As, 34 Se, 35 Br isotopes were measured using the direct method. The secondary beams were produced using a 78 Kr primary beam of 73 MeV/nucleon in conjunction with SISSI and the SPEG spectrometers at GANIL. Most elements show reduced radii which vary with N, with a minimum around N=36-38. The experimental reduced radii are compared to theoretical values obtained from Glauber reaction cross-section calculations based on Relativistic Mean Field (RMF) nuclear densities

  20. Interaction radii of proton-rich radioactive nuclei at A=60 - 80

    International Nuclear Information System (INIS)

    Lima, G.F.; Lepine-Szily, A.; Villari, A.C.; Lichtenthaler, R.; Villari, A.C.; Mittig, W.; Chartier, M.; Casandjian, J.M.; Lewitowicz, M.; Ostrowski, A.N.; Hirata, D.; Angelique, J.C.; Orr, N.A.; Audi, G.; Cunsolo, A.; Foti, A.; Donzeaud, C.; MacCormick, M.; Stephan, C.; Suomijarvi, T.; Tassan-Got, L.; Gillibert, A.; Lukyanov, S.; Chartier, M.; Morrissey, D.J.; Sherrill, B.M.; Ostrowski, A.N.; Vieira, D.J.; Wouters, J.M.

    1998-01-01

    The interaction radii of proton-rich, radioactive 31 Ga, 32 Ge, 33 As, 34 Se, 35 Br isotopes were measured using the direct method. The secondary beams were produced using a 78 Kr primary beam of 73 MeV/nucleon in conjunction with SISSI and the SPEG spectrometers at GANIL. Most elements show reduced radii which vary with N, with a minimum around N=36 - 38. The experimental reduced radii are compared to theoretical values obtained from Glauber reaction cross-section calculations based on Relativistic Mean Field (RMF) nuclear densities. copyright 1998 American Institute of Physics

  1. Summertime wind climate in Yerevan: valley wind systems

    Science.gov (United States)

    Gevorgyan, Artur

    2017-03-01

    1992-2014 wind climatology analysis in Yerevan is presented with particular focus given to the summertime thermally induced valley wind systems. Persistence high winds are observed in Yerevan during July-August months when the study region is strongly affected by a heat-driven plain-plateau circulation. The local valley winds arrive in Yerevan in the evening hours, generally, from 1500 to 1800 UTC, leading to rapid enhancement of wind speeds and dramatic changes in wind direction. Valley-winds significantly impact the local climate of Yerevan, which is a densely populated city. These winds moderate evening temperatures after hot and dry weather conditions observed during summertime afternoons. On the other hand, valley winds result in significantly higher nocturnal temperatures and more frequent occurrence of warm nights (tn90p) in Yerevan due to stronger turbulent mixing of boundary layer preventing strong surface cooling and temperature drop in nighttime and morning hours. The applied WRF-ARW limited area model is able to simulate the key features of the observed spatial pattern of surface winds in Armenia associated with significant terrain channeling, wind curls, etc. By contrast, ECMWF EPS global model fails to capture mesoscale and local wind systems over Armenia. However, the results of statistical verification of surface winds in Yerevan showed that substantial biases are present in WRF 18-h wind forecasts, as well as, the temporal variability of observed surface winds is not reproduced adequately in WRF-ARW model.

  2. Observability of wind power

    International Nuclear Information System (INIS)

    Gonot, J.P.; Fraisse, J.L.

    2009-01-01

    The total installed capacity of wind power grows from a few hundred MW at the beginning of 2005 to 3400 MW at the end of 2008. With such a trend, a total capacity of 7000 MW could be reached by 2010. The natural variability of wind power and the difficulty of its predictability require a change in the traditional way of managing supply/demand balance, day-ahead margins and the control of electrical flows. As a consequence, RTE operators should be informed quickly and reliably of the real time output power of wind farms and of its evolvement some hours or days ahead to ensure the reliability of the French electrical power system. French specificities are that wind farms are largely spread over the territory, that 95 % of wind farms have an output power below 10 MW and that they are connected to the distribution network. In this context, new tools were necessary to acquire as soon as possible data concerning wind power. In two years long, RTE set up an observatory of wind production 'IPES system' enable to get an access to the technical characteristics of the whole wind farms, to observe in real time 75 % of the wind generation and to implement a forecast model related to wind generation. (authors)

  3. Strategic wind power trading considering rival wind power production

    DEFF Research Database (Denmark)

    Exizidis, Lazaros; Kazempour, Jalal; Pinson, Pierre

    2016-01-01

    In an electricity market with high share of wind power, it is expected that wind power producers may exercise market power. However, wind producers have to cope with wind’s uncertain nature in order to optimally offer their generation, whereas in a market with more than one wind producers......, uncertainty of rival wind power generation should also be considered. Under this context, this paper addresses the impact of rival wind producers on the offering strategy and profits of a pricemaker wind producer. A stochastic day-ahead market setup is considered, which optimizes the day-ahead schedules...... considering a number of foreseen real-time scenarios. The results indicate that strategic wind producer is more likely to exercise market power having a mid-mean or low-mean forecast distribution, rather than having a high-mean one. Furthermore, it is observed that its offering strategy varies considerably...

  4. Richardson's Barotropic Forecast: A Reappraisal.

    Science.gov (United States)

    Lynch, Peter

    1992-01-01

    To elucidate his numerical technique and to examine the effectiveness of geostrophic initial winds, Lewis Fry Richardson carried out an idealized forecast using the linear shallow-water equations and simple analytical pressure and velocity fields. This barotropic forecast has been repeated and extended using a global numerical model, and the results are presented in this paper. Richardson's conclusions regarding the use of geostrophic winds as initial data are reconsidered.An analysis of Richardson's data into normal modes shows that almost 85% of the energy is accounted for by a single eigenmode, the gravest symmetric rotational Hough mode, which travels westward with a period of about five days. This five-day wave has been detected in analyses of stratospheric data. It is striking that the fields chosen by Richardson on considerations of smoothness should so closely resemble a natural oscillation of the atmosphere.The numerical model employed in this study uses an implicit differencing technique, which is stable for large time steps. The numerical instability that would have destroyed Richardson's barotropic forecast, had it been extended, is thereby circumvented. It is sometimes said that computational instability was the cause of the failure of Richardson's baroclinic forecast, for which he obtained a pressure tendency value two orders of magnitude too large. However, the initial tendency is independent of the time step (at least for the explicit scheme used by Richardson). In fact, the spurious tendency resulted from the presence of unrealistically large high-frequency gravity-wave components in the initial fields.High-frequency oscillations are also found in the evolution starting from the idealized data in the barotropic forecast. They are shown to be due to the gravity-wave components of the initial data. These oscillations may be removed by a slight modification of the initial fields. This initialization is effected by means of a simple digital filtering

  5. ktup Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  6. ktop Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  7. kfxe Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  8. kmsy Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  9. kroc Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  10. kacv Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  11. katw Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  12. klax Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  13. ksea Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  14. pafa Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...