WorldWideScience

Sample records for solar resource forecasts

  1. U.S. Department of Energy Workshop Report: Solar Resources and Forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Stoffel, T.

    2012-06-01

    This report summarizes the technical presentations, outlines the core research recommendations, and augments the information of the Solar Resources and Forecasting Workshop held June 20-22, 2011, in Golden, Colorado. The workshop brought together notable specialists in atmospheric science, solar resource assessment, solar energy conversion, and various stakeholders from industry and academia to review recent developments and provide input for planning future research in solar resource characterization, including measurement, modeling, and forecasting.

  2. Solar Resource Assessment with Sky Imagery and a Virtual Testbed for Sky Imager Solar Forecasting

    Science.gov (United States)

    Kurtz, Benjamin Bernard

    In recent years, ground-based sky imagers have emerged as a promising tool for forecasting solar energy on short time scales (0 to 30 minutes ahead). Following the development of sky imager hardware and algorithms at UC San Diego, we present three new or improved algorithms for sky imager forecasting and forecast evaluation. First, we present an algorithm for measuring irradiance with a sky imager. Sky imager forecasts are often used in conjunction with other instruments for measuring irradiance, so this has the potential to decrease instrumentation costs and logistical complexity. In particular, the forecast algorithm itself often relies on knowledge of the current irradiance which can now be provided directly from the sky images. Irradiance measurements are accurate to within about 10%. Second, we demonstrate a virtual sky imager testbed that can be used for validating and enhancing the forecast algorithm. The testbed uses high-quality (but slow) simulations to produce virtual clouds and sky images. Because virtual cloud locations are known, much more advanced validation procedures are possible with the virtual testbed than with measured data. In this way, we are able to determine that camera geometry and non-uniform evolution of the cloud field are the two largest sources of forecast error. Finally, with the assistance of the virtual sky imager testbed, we develop improvements to the cloud advection model used for forecasting. The new advection schemes are 10-20% better at short time horizons.

  3. Solar Resource Assessment

    Energy Technology Data Exchange (ETDEWEB)

    Renne, D.; George, R.; Wilcox, S.; Stoffel, T.; Myers, D.; Heimiller, D.

    2008-02-01

    This report covers the solar resource assessment aspects of the Renewable Systems Interconnection study. The status of solar resource assessment in the United States is described, and summaries of the availability of modeled data sets are provided.

  4. Online Short-term Solar Power Forecasting

    DEFF Research Database (Denmark)

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

    2011-01-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2015-08-05

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

  6. Online short-term solar power forecasting

    DEFF Research Database (Denmark)

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

    2009-01-01

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

  7. Status of mineral resources evaluation and forecast

    International Nuclear Information System (INIS)

    Ma Hanfeng; Li Ziying; Luo Yi; Li Shengxiang; Sun Wenpeng

    2007-01-01

    The work of resources evaluation and forecast is a focus to the governments of every country in the world, it is related to the establishment of strategic policy on the national mineral resources. In order to quantitatively evaluate the general potential of uranium resources in China and better forecast uranium deposits, this paper briefly introduces the method of evaluating total amount of mineral resources, especially 6 usual prospective methods which are recommended in international geology comparison programs, as well as principle of usual mineral resources quantitative prediction and its steps. The work history of mineral resources evaluation and forecast is reviewed concisely. Advantages and disadvantages of each method, their application field and condition are also explained briefly. At last, the history of uranium resources evaluation and forecast in China and its status are concisely outlined. (authors)

  8. Resources and Long-Range Forecasts

    Science.gov (United States)

    Smith, Waldo E.

    1973-01-01

    The author argues that forecasts of quick depletion of resources in the environment as a result of overpopulation and increased usage may not be free from error. Ignorance still exists in understanding the recovery mechanisms of nature. Long-range forecasts are likely to be wrong in such situations. (PS)

  9. Short-Term Solar Collector Power Forecasting

    DEFF Research Database (Denmark)

    Bacher, Peder; Madsen, Henrik; Perers, Bengt

    2011-01-01

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

  10. Evolutionary Forecast Engines for Solar Meteorology

    Science.gov (United States)

    Coimbra, C. F.

    2012-12-01

    A detailed comparison of non-stationary regression and stochastic learning methods based on k-Nearest Neighbor (kNN), Artificial Neural Networks (ANN) and Genetic Algorithm (GA) approaches is carried out in order to develop high-fidelity solar forecast engines for several time horizons of interest. A hybrid GA/ANN method emerges as the most robust stochastic learning candidate. The GA/ANN approach In general the following decisions need to be made when creating an ANN-based solar forecast model: the ANN architecture: number of layers, numbers of neurons per layer; the preprocessing scheme; the fraction and distribution between training and testing data, and the meteorological and radiometric inputs. ANNs are very well suited to handle multivariate forecasting models due to their overall flexibility and nonlinear pattern recognition abilities. However, the forecasting skill of ANNs depends on a new set of parameters to be optimized within the context of the forecast model, which is the selection of input variables that most directly impact the fidelity of the forecasts. In a data rich scenario where irradiation, meteorological, and cloud cover data are available, it is not always evident which variables to include in the model a priori. New variables can also arise from data preprocessing such as smoothing or spectral decomposition. One way to avoid time-consuming trial-and-error approaches that have limited chance to result in optimal ANN topology and input selection is to couple the ANN with some optimization algorithm that scans the solution space and "evolves" the ANN structure. Genetic Algorithms (GAs) are well suited for this task. Results and Discussion The models built upon the historical data of 2009 and 2010 are applied to the 2011 data without modifications or retraining. We consider 3 solar variability seasons or periods, which are subsets of the total error evaluation data set. The 3 periods are defined based on the solar variability study as: - a high

  11. Near-term Forecasting of Solar Total and Direct Irradiance for Solar Energy Applications

    Science.gov (United States)

    Long, C. N.; Riihimaki, L. D.; Berg, L. K.

    2012-12-01

    Integration of solar renewable energy into the power grid, like wind energy, is hindered by the variable nature of the solar resource. One challenge of the integration problem for shorter time periods is the phenomenon of "ramping events" where the electrical output of the solar power system increases or decreases significantly and rapidly over periods of minutes or less. Advance warning, of even just a few minutes, allows power system operators to compensate for the ramping. However, the ability for short-term prediction on such local "point" scales is beyond the abilities of typical model-based weather forecasting. Use of surface-based solar radiation measurements has been recognized as a likely solution for providing input for near-term (5 to 30 minute) forecasts of solar energy availability and variability. However, it must be noted that while fixed-orientation photovoltaic panel systems use the total (global) downwelling solar radiation, tracking photovoltaic and solar concentrator systems use only the direct normal component of the solar radiation. Thus even accurate near-term forecasts of total solar radiation will under many circumstances include inherent inaccuracies with respect to tracking systems due to lack of information of the direct component of the solar radiation. We will present examples and statistical analyses of solar radiation partitioning showing the differences in the behavior of the total/direct radiation with respect to the near-term forecast issue. We will present an overview of the possibility of using a network of unique new commercially available total/diffuse radiometers in conjunction with a near-real-time adaptation of the Shortwave Radiative Flux Analysis methodology (Long and Ackerman, 2000; Long et al., 2006). The results are used, in conjunction with persistence and tendency forecast techniques, to provide more accurate near-term forecasts of cloudiness, and both total and direct normal solar irradiance availability and

  12. Nonlinear solar cycle forecasting: theory and perspectives

    Science.gov (United States)

    Baranovski, A. L.; Clette, F.; Nollau, V.

    2008-02-01

    In this paper we develop a modern approach to solar cycle forecasting, based on the mathematical theory of nonlinear dynamics. We start from the design of a static curve fitting model for the experimental yearly sunspot number series, over a time scale of 306 years, starting from year 1700 and we establish a least-squares optimal pulse shape of a solar cycle. The cycle-to-cycle evolution of the parameters of the cycle shape displays different patterns, such as a Gleissberg cycle and a strong anomaly in the cycle evolution during the Dalton minimum. In a second step, we extract a chaotic mapping for the successive values of one of the key model parameters - the rate of the exponential growth-decrease of the solar activity during the n-th cycle. We examine piece-wise linear techniques for the approximation of the derived mapping and we provide its probabilistic analysis: calculation of the invariant distribution and autocorrelation function. We find analytical relationships for the sunspot maxima and minima, as well as their occurrence times, as functions of chaotic values of the above parameter. Based on a Lyapunov spectrum analysis of the embedded mapping, we finally establish a horizon of predictability for the method, which allows us to give the most probable forecasting of the upcoming solar cycle 24, with an expected peak height of 93±21 occurring in 2011/2012.

  13. Solar Radiation Forecasting, Accounting for Daily Variability

    Directory of Open Access Journals (Sweden)

    Roberto Langella

    2016-03-01

    Full Text Available Radiation forecast accounting for daily and instantaneous variability was pursued by means of a new bi-parametric statistical model that builds on a model previously proposed by the same authors. The statistical model is developed with direct reference to the Liu-Jordan clear sky theoretical expression but is not bound by a specific clear sky model; it accounts separately for the mean daily variability and for the variation of solar irradiance during the day by means of two corrective parameters. This new proposal allows for a better understanding of the physical phenomena and improves the effectiveness of statistical characterization and subsequent simulation of the introduced parameters to generate a synthetic solar irradiance time series. Furthermore, the analysis of the experimental distributions of the two parameters’ data was developed, obtaining opportune fittings by means of parametric analytical distributions or mixtures of more than one distribution. Finally, the model was further improved toward the inclusion of weather prediction information in the solar irradiance forecasting stage, from the perspective of overcoming the limitations of purely statistical approaches and implementing a new tool in the frame of solar irradiance prediction accounting for weather predictions over different time horizons.

  14. Nonlinear solar cycle forecasting: theory and perspectives

    Directory of Open Access Journals (Sweden)

    A. L. Baranovski

    2008-02-01

    Full Text Available In this paper we develop a modern approach to solar cycle forecasting, based on the mathematical theory of nonlinear dynamics. We start from the design of a static curve fitting model for the experimental yearly sunspot number series, over a time scale of 306 years, starting from year 1700 and we establish a least-squares optimal pulse shape of a solar cycle. The cycle-to-cycle evolution of the parameters of the cycle shape displays different patterns, such as a Gleissberg cycle and a strong anomaly in the cycle evolution during the Dalton minimum. In a second step, we extract a chaotic mapping for the successive values of one of the key model parameters – the rate of the exponential growth-decrease of the solar activity during the n-th cycle. We examine piece-wise linear techniques for the approximation of the derived mapping and we provide its probabilistic analysis: calculation of the invariant distribution and autocorrelation function. We find analytical relationships for the sunspot maxima and minima, as well as their occurrence times, as functions of chaotic values of the above parameter. Based on a Lyapunov spectrum analysis of the embedded mapping, we finally establish a horizon of predictability for the method, which allows us to give the most probable forecasting of the upcoming solar cycle 24, with an expected peak height of 93±21 occurring in 2011/2012.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2015-12-08

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

  16. 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.

  17. Recent Progress of Solar Weather Forecasting at Naoc

    Science.gov (United States)

    He, Han; Wang, Huaning; Du, Zhanle; Zhang, Liyun; Huang, Xin; Yan, Yan; Fan, Yuliang; Zhu, Xiaoshuai; Guo, Xiaobo; Dai, Xinghua

    The history of solar weather forecasting services at National Astronomical Observatories, Chinese Academy of Sciences (NAOC) can be traced back to 1960s. Nowadays, NAOC is the headquarters of the Regional Warning Center of China (RWC-China), which is one of the members of the International Space Environment Service (ISES). NAOC is responsible for exchanging data, information and space weather forecasts of RWC-China with other RWCs. The solar weather forecasting services at NAOC cover short-term prediction (within two or three days), medium-term prediction (within several weeks), and long-term prediction (in time scale of solar cycle) of solar activities. Most efforts of the short-term prediction research are concentrated on the solar eruptive phenomena, such as flares, coronal mass ejections (CMEs) and solar proton events, which are the key driving sources of strong space weather disturbances. Based on the high quality observation data of the latest space-based and ground-based solar telescopes and with the help of artificial intelligence techniques, new numerical models with quantitative analyses and physical consideration are being developed for the predictions of solar eruptive events. The 3-D computer simulation technology is being introduced for the operational solar weather service platform to visualize the monitoring of solar activities, the running of the prediction models, as well as the presenting of the forecasting results. A new generation operational solar weather monitoring and forecasting system is expected to be constructed in the near future at NAOC.

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

    International Nuclear Information System (INIS)

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

    2013-01-01

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

  19. Intra-Hour Dispatch and Automatic Generator Control Demonstration with Solar Forecasting - Final Report

    Energy Technology Data Exchange (ETDEWEB)

    Coimbra, Carlos F. M. [Univ. of California, San Diego, CA (United States

    2016-02-25

    In this project we address multiple resource integration challenges associated with increasing levels of solar penetration that arise from the variability and uncertainty in solar irradiance. We will model the SMUD service region as its own balancing region, and develop an integrated, real-time operational tool that takes solar-load forecast uncertainties into consideration and commits optimal energy resources and reserves for intra-hour and intra-day decisions. The primary objectives of this effort are to reduce power system operation cost by committing appropriate amount of energy resources and reserves, as well as to provide operators a prediction of the generation fleet’s behavior in real time for realistic PV penetration scenarios. The proposed methodology includes the following steps: clustering analysis on the expected solar variability per region for the SMUD system, Day-ahead (DA) and real-time (RT) load forecasts for the entire service areas, 1-year of intra-hour CPR forecasts for cluster centers, 1-year of smart re-forecasting CPR forecasts in real-time for determination of irreducible errors, and uncertainty quantification for integrated solar-load for both distributed and central stations (selected locations within service region) PV generation.

  20. Solar Storm GIC Forecasting: Solar Shield Extension Development of the End-User Forecasting System Requirements

    Science.gov (United States)

    Pulkkinen, A.; Mahmood, S.; Ngwira, C.; Balch, C.; Lordan, R.; Fugate, D.; Jacobs, W.; Honkonen, I.

    2015-01-01

    A NASA Goddard Space Flight Center Heliophysics Science Division-led team that includes NOAA Space Weather Prediction Center, the Catholic University of America, Electric Power Research Institute (EPRI), and Electric Research and Management, Inc., recently partnered with the Department of Homeland Security (DHS) Science and Technology Directorate (S&T) to better understand the impact of Geomagnetically Induced Currents (GIC) on the electric power industry. This effort builds on a previous NASA-sponsored Applied Sciences Program for predicting GIC, known as Solar Shield. The focus of the new DHS S&T funded effort is to revise and extend the existing Solar Shield system to enhance its forecasting capability and provide tailored, timely, actionable information for electric utility decision makers. To enhance the forecasting capabilities of the new Solar Shield, a key undertaking is to extend the prediction system coverage across Contiguous United States (CONUS), as the previous version was only applicable to high latitudes. The team also leverages the latest enhancements in space weather modeling capacity residing at Community Coordinated Modeling Center to increase the Technological Readiness Level, or Applications Readiness Level of the system http://www.nasa.gov/sites/default/files/files/ExpandedARLDefinitions4813.pdf.

  1. Increasing the temporal resolution of direct normal solar irradiance forecasted series

    Science.gov (United States)

    Fernández-Peruchena, Carlos M.; Gastón, Martin; Schroedter-Homscheidt, Marion; Marco, Isabel Martínez; Casado-Rubio, José L.; García-Moya, José Antonio

    2017-06-01

    A detailed knowledge of the solar resource is a critical point in the design and control of Concentrating Solar Power (CSP) plants. In particular, accurate forecasting of solar irradiance is essential for the efficient operation of solar thermal power plants, the management of energy markets, and the widespread implementation of this technology. Numerical weather prediction (NWP) models are commonly used for solar radiation forecasting. In the ECMWF deterministic forecasting system, all forecast parameters are commercially available worldwide at 3-hourly intervals. Unfortunately, as Direct Normal solar Irradiance (DNI) exhibits a great variability due to the dynamic effects of passing clouds, 3-h time resolution is insufficient for accurate simulations of CSP plants due to their nonlinear response to DNI, governed by various thermal inertias due to their complex response characteristics. DNI series of hourly or sub-hourly frequency resolution are normally used for an accurate modeling and analysis of transient processes in CSP technologies. In this context, the objective of this study is to propose a methodology for generating synthetic DNI time series at 1-h (or higher) temporal resolution from 3-h DNI series. The methodology is based upon patterns as being defined with help of the clear-sky envelope approach together with a forecast of maximum DNI value, and it has been validated with high quality measured DNI data.

  2. 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.

  3. DIY Solar Market Analysis Webinar Series: Solar Resource and Technical

    Science.gov (United States)

    Series: Solar Resource and Technical Potential DIY Solar Market Analysis Webinar Series: Solar Resource and Technical Potential Wednesday, June 11, 2014 As part of a Do-It-Yourself Solar Market Analysis Potential | State, Local, and Tribal Governments | NREL DIY Solar Market Analysis Webinar

  4. Short term solar radiation forecasting: Island versus continental sites

    International Nuclear Information System (INIS)

    Boland, John; David, Mathieu; Lauret, Philippe

    2016-01-01

    Due its intermittency, the large-scale integration of solar energy into electricity grids is an issue and more specifically in an insular context. Thus, forecasting the output of solar energy is a key feature to efficiently manage the supply-demand balance. In this paper, three short term forecasting procedures are applied to island locations in order to see how they perform in situations that are potentially more volatile than continental locations. Two continental locations, one coastal and one inland are chosen for comparison. At the two time scales studied, ten minute and hourly, the island locations prove to be more difficult to forecast, as shown by larger forecast errors. It is found that the three methods, one purely statistical combining Fourier series plus linear ARMA models, one combining clear sky index models plus neural net models, and a third using a clear sky index plus ARMA, give similar forecasting results. It is also suggested that there is great potential of merging modelling approaches on different horizons. - Highlights: • Solar energy forecasting is more difficult for insular than continental sites. • Fourier series plus linear ARMA models are one forecasting method tested. • Clear sky index models plus neural net models are also tested. • Clear sky index models plus linear ARMA is also an option. • All three approaches have similar skill.

  5. Advancing solar energy forecasting through the underlying physics

    Science.gov (United States)

    Yang, H.; Ghonima, M. S.; Zhong, X.; Ozge, B.; Kurtz, B.; Wu, E.; Mejia, F. A.; Zamora, M.; Wang, G.; Clemesha, R.; Norris, J. R.; Heus, T.; Kleissl, J. P.

    2017-12-01

    As solar power comprises an increasingly large portion of the energy generation mix, the ability to accurately forecast solar photovoltaic generation becomes increasingly important. Due to the variability of solar power caused by cloud cover, knowledge of both the magnitude and timing of expected solar power production ahead of time facilitates the integration of solar power onto the electric grid by reducing electricity generation from traditional ancillary generators such as gas and oil power plants, as well as decreasing the ramping of all generators, reducing start and shutdown costs, and minimizing solar power curtailment, thereby providing annual economic value. The time scales involved in both the energy markets and solar variability range from intra-hour to several days ahead. This wide range of time horizons led to the development of a multitude of techniques, with each offering unique advantages in specific applications. For example, sky imagery provides site-specific forecasts on the minute-scale. Statistical techniques including machine learning algorithms are commonly used in the intra-day forecast horizon for regional applications, while numerical weather prediction models can provide mesoscale forecasts on both the intra-day and days-ahead time scale. This talk will provide an overview of the challenges unique to each technique and highlight the advances in their ongoing development which come alongside advances in the fundamental physics underneath.

  6. 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......High-speed solar wind streams emanating from coronal holes are frequently impinging on the Earth's magnetosphere causing recurrent, medium-level geomagnetic storm activity. Modeling high-speed solar wind streams is thus an essential element of successful space weather forecasting. Here we evaluate...... high-speed stream forecasts made by the empirical solar wind forecast (ESWF) and the semiempirical Wang-Sheeley-Arge (WSA) model based on the in situ plasma measurements from the Advanced Composition Explorer (ACE) spacecraft for the years 2011 to 2014. While the ESWF makes use of an empirical relation...

  7. Wind speed forecasting in the central California wind resource area

    Energy Technology Data Exchange (ETDEWEB)

    McCarthy, E.F. [Wind Economics & Technology, Inc., Martinez, CA (United States)

    1997-12-31

    A wind speed forecasting program was implemented in the summer seasons of 1985 - 87 in the Central California Wind Resource Area (WRA). The forecasting program is designed to use either meteorological observations from the WRA and local upper air observations or upper air observations alone to predict the daily average windspeed at two locations. Forecasts are made each morning at 6 AM and are valid for a 24 hour period. Ease of use is a hallmark of the program as the daily forecast can be made using data entered into a programmable HP calculator. The forecasting program was the first step in a process to examine whether the electrical energy output of an entire wind power generation facility or defined subsections of the same facility could be predicted up to 24 hours in advance. Analysis of the results of the summer season program using standard forecast verification techniques show the program has skill over persistence and climatology.

  8. Load Forecasting in Electric Utility Integrated Resource Planning

    Energy Technology Data Exchange (ETDEWEB)

    Carvallo, Juan Pablo [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Larsen, Peter H. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Sanstad, Alan H [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Goldman, Charles A. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

    2017-07-19

    Integrated resource planning (IRP) is a process used by many vertically-integrated U.S. electric utilities to determine least-cost/risk supply and demand-side resources that meet government policy objectives and future obligations to customers and, in many cases, shareholders. Forecasts of energy and peak demand are a critical component of the IRP process. There have been few, if any, quantitative studies of IRP long-run (planning horizons of two decades) load forecast performance and its relationship to resource planning and actual procurement decisions. In this paper, we evaluate load forecasting methods, assumptions, and outcomes for 12 Western U.S. utilities by examining and comparing plans filed in the early 2000s against recent plans, up to year 2014. We find a convergence in the methods and data sources used. We also find that forecasts in more recent IRPs generally took account of new information, but that there continued to be a systematic over-estimation of load growth rates during the period studied. We compare planned and procured resource expansion against customer load and year-to-year load growth rates, but do not find a direct relationship. Load sensitivities performed in resource plans do not appear to be related to later procurement strategies even in the presence of large forecast errors. These findings suggest that resource procurement decisions may be driven by other factors than customer load growth. Our results have important implications for the integrated resource planning process, namely that load forecast accuracy may not be as important for resource procurement as is generally believed, that load forecast sensitivities could be used to improve the procurement process, and that management of load uncertainty should be prioritized over more complex forecasting techniques.

  9. Probabilistic Forecasts of Solar Irradiance by Stochastic Differential Equations

    DEFF Research Database (Denmark)

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

    2014-01-01

    approach allows for characterizing both the interdependence structure of prediction errors of short-term solar irradiance and their predictive distribution. Three different stochastic differential equation models are first fitted to a training data set and subsequently evaluated on a one-year test set...... included in probabilistic forecasts may be paramount for decision makers to efficiently make use of this uncertain and variable generation. In this paper, a stochastic differential equation framework for modeling the uncertainty associated with the solar irradiance point forecast is proposed. This modeling...

  10. Solar activity monitoring and forecasting capabilities at Big Bear Solar Observatory

    Directory of Open Access Journals (Sweden)

    P. T. Gallagher

    2002-07-01

    Full Text Available The availability of full-disk, high-resolution Ha images from Big Bear Solar Observatory (USA, Kanzelhöhe Solar Observatory (Austria, and Yunnan Astronomical Observatory (China allows for the continual monitoring of solar activity with unprecedented spatial and temporal resolution. Typically, this Global Ha Network (GHN provides almost uninterrupted Ha images with a cadence of 1 min and an image scale of 1'' per pixel.  Every hour, GHN images are transferred to the web-based BBSO Active Region Monitor (ARM; www.bbso.njit.edu/arm, which includes the most recent EUV, continuum, and magnetogram data from the Solar and Heliospheric Observatory, together with magnetograms from the Global Oscillation Network Group. ARM also includes a variety of active region properties from the National Oceanic and Atmospheric Administration’s Space Environment Center, such as up-to-date active region positions, GOES 5-min X-ray data, and flare identification. Stokes I, V, Q, and U images are available from the recently operational BBSO Digital Vector Magnetograph and the Vector Magnetograph at the Huairou Solar Observing Station of Beijing Observatory. Vector magnetograms provide complete information on the photospheric magnetic field, and allow for magnetic flux gradients, electric currents, and shear forces to be calculated: these measurements are extremely sensitive to conditions resulting in flaring activity. Furthermore, we have developed a Flare Prediction System which estimates the probability for each region to produce C-, M-, or X-class flares based on nearly eight years of NOAA data from cycle 22. This, in addition to BBSO’s daily solar activity reports, has proven a useful resource for activity forecasting.Key words. Solar physics, astronomy and astrophysics (flares and mass ejections; instruments and techniques; photosphere and chromosphere

  11. Resource Information and Forecasting Group; Electricity, Resources, & Building Systems Integration (ERBSI) (Fact Sheet)

    Energy Technology Data Exchange (ETDEWEB)

    2009-11-01

    Researchers in the Resource Information and Forecasting group at NREL provide scientific, engineering, and analytical expertise to help characterize renewable energy resources and facilitate the integration of these clean energy sources into the electricity grid.

  12. Weather Forecasts are for Wimps. Why Water Resource Managers Do Not Use Climate Forecasts

    Energy Technology Data Exchange (ETDEWEB)

    Rayner, S. [James Martin Institute of Science and Civilization, Said Business School, University of Oxford, OX1 1HP (United Kingdom); Lach, D. [Oregon State University, Corvallis, OR, 97331-4501 (United States); Ingram, H. [School of Social Ecology, University of California Irvine, Irvine, CA, 92697-7075 (United States)

    2005-04-15

    Short-term climate forecasting offers the promise of improved hydrologic management strategies. However, water resource managers in the United States have proven reluctant to incorporate them in decision making. While managers usually cite poor reliability of the forecasts as the reason for this, they are seldom able to demonstrate knowledge of the actual performance of forecasts or to consistently articulate the level of reliability that they would require. Analysis of three case studies in California, the Pacific Northwest, and metro Washington DC identifies institutional reasons that appear to lie behind managers reluctance to use the forecasts. These include traditional reliance on large built infrastructure, organizational conservatism and complexity, mismatch of temporal and spatial scales of forecasts to management needs, political disincentives to innovation, and regulatory constraints. The paper concludes that wider acceptance of the forecasts will depend on their being incorporated in existing organizational routines and industrial codes and practices, as well as changes in management incentives to innovation. Finer spatial resolution of forecasts and the regional integration of multi-agency functions would also enhance their usability. The title of this article is taken from an advertising slogan for the Oldsmobile Bravura SUV.

  13. Sensor network based solar forecasting using a local vector autoregressive ridge framework

    Energy Technology Data Exchange (ETDEWEB)

    Xu, J. [Stony Brook Univ., NY (United States); Yoo, S. [Brookhaven National Lab. (BNL), Upton, NY (United States); Heiser, J. [Brookhaven National Lab. (BNL), Upton, NY (United States); Kalb, P. [Brookhaven National Lab. (BNL), Upton, NY (United States)

    2016-04-04

    The significant improvements and falling costs of photovoltaic (PV) technology make solar energy a promising resource, yet the cloud induced variability of surface solar irradiance inhibits its effective use in grid-tied PV generation. Short-term irradiance forecasting, especially on the minute scale, is critically important for grid system stability and auxiliary power source management. Compared to the trending sky imaging devices, irradiance sensors are inexpensive and easy to deploy but related forecasting methods have not been well researched. The prominent challenge of applying classic time series models on a network of irradiance sensors is to address their varying spatio-temporal correlations due to local changes in cloud conditions. We propose a local vector autoregressive framework with ridge regularization to forecast irradiance without explicitly determining the wind field or cloud movement. By using local training data, our learned forecast model is adaptive to local cloud conditions and by using regularization, we overcome the risk of overfitting from the limited training data. Our systematic experimental results showed an average of 19.7% RMSE and 20.2% MAE improvement over the benchmark Persistent Model for 1-5 minute forecasts on a comprehensive 25-day dataset.

  14. Solar irradiance forecasting at one-minute intervals for different sky conditions using sky camera images

    International Nuclear Information System (INIS)

    Alonso-Montesinos, J.; Batlles, F.J.; Portillo, C.

    2015-01-01

    Highlights: • The solar resource has been predicted for three hours at 1-min intervals. • Digital image levels and cloud motion vectors are joint for irradiance forecasting. • The three radiation components have been predicted under different sky conditions. • Diffuse and global radiation has an nRMSE value around 10% in all sky conditions. • Beam irradiance is predicted with an nRMSE value of about 15% in overcast skies. - Abstract: In the search for new techniques to predict atmospheric features that might be useful to solar power plant operators, we have carried out solar irradiance forecasting using emerging sky camera technology. Digital image levels are converted into irradiances and then the maximum cross-correlation method is applied to obtain future predictions. This methodology is a step forward in the study of the solar resource, essential to solar plant operators in adapting a plant’s operating procedures to atmospheric conditions and to improve electricity generation. The results are set out using different statistical parameters, in which beam, diffuse and global irradiances give a constant normalized root-mean-square error value over the time interval for all sky conditions. The average measure is 25.44% for beam irradiance; 11.60% for diffuse irradiance and 11.17% for global irradiance.

  15. Solar activity: nowcasting and forecasting at the SIDC

    Directory of Open Access Journals (Sweden)

    D. Berghmans

    2005-11-01

    Full Text Available The Solar Influences Data analysis Center (SIDC is the World Data Center for the production and the distribution of the International Sunspot Index, coordinating a network of about 80 stations worldwide. From this core activity, the SIDC has grown in recent years to a European center for nowcasting and forecasting of solar activity on all timescales. This paper reviews the services (data, forecasts, alerts, software that the SIDC currently offers to the scientific community. The SIDC operates instruments both on the ground and in space. The USET telescope in Brussels produces daily white light and Hα images. Several members of the SIDC are co-investigators of the EIT instrument onboard SOHO and are involved in the development of the next generation of Europe's solar weather monitoring capabilities. While the SIDC is staffed only during day-time (7 days/week, the monitoring service is a 24 h activity thanks to the implementation of autonomous software for data handling and analysis and the sending of automated alerts. We will give an overview of recently developed techniques for visualization and automated analysis of solar images and detection of events significant for space weather (e.g. CMEs or EIT waves. As part of the involvement of the SIDC in the ESA Pilot Project for Space Weather Applications we have developed services dedicated to the users of the Global Positioning System (GPS. As a Regional Warning Center (RWC of the International Space Environment Service (ISES, the SIDC produces daily forecasts of flaring probability, geomagnetic activity and 10.7 cm radio flux. The accuracy of these forecasts will be investigated through an in-depth quality analysis.

  16. Dynamic resource allocation using performance forecasting

    NARCIS (Netherlands)

    Moura, Paulo; Kon, Fabio; Voulgaris, Spyros; Van Steen, Maarten

    2016-01-01

    To benefit from the performance gains and cost savings enabled by elasticity in cloud IaaS environments, effective automated mechanisms for scaling are essential. This automation requires monitoring system status and defining criteria to trigger allocation and deallocation of resources. While these

  17. Solar energy prediction and verification using operational model forecasts and ground-based solar measurements

    International Nuclear Information System (INIS)

    Kosmopoulos, P.G.; Kazadzis, S.; Lagouvardos, K.; Kotroni, V.; Bais, A.

    2015-01-01

    The present study focuses on the predictions and verification of these predictions of solar energy using ground-based solar measurements from the Hellenic Network for Solar Energy and the National Observatory of Athens network, as well as solar radiation operational forecasts provided by the MM5 mesoscale model. The evaluation was carried out independently for the different networks, for two forecast horizons (1 and 2 days ahead), for the seasons of the year, for varying solar elevation, for the indicative energy potential of the area, and for four classes of cloud cover based on the calculated clearness index (k_t): CS (clear sky), SC (scattered clouds), BC (broken clouds) and OC (overcast). The seasonal dependence presented relative rRMSE (Root Mean Square Error) values ranging from 15% (summer) to 60% (winter), while the solar elevation dependence revealed a high effectiveness and reliability near local noon (rRMSE ∼30%). An increment of the errors with cloudiness was also observed. For CS with mean GHI (global horizontal irradiance) ∼ 650 W/m"2 the errors are 8%, for SC 20% and for BC and OC the errors were greater (>40%) but correspond to much lower radiation levels (<120 W/m"2) of consequently lower energy potential impact. The total energy potential for each ground station ranges from 1.5 to 1.9 MWh/m"2, while the mean monthly forecast error was found to be consistently below 10%. - Highlights: • Long term measurements at different atmospheric cases are needed for energy forecasting model evaluations. • The total energy potential at the Greek sites presented ranges from 1.5 to 1.9 MWh/m"2. • Mean monthly energy forecast errors are within 10% for all cases analyzed. • Cloud presence results of an additional forecast error that varies with the cloud cover.

  18. Efficient Resources Provisioning Based on Load Forecasting in Cloud

    Directory of Open Access Journals (Sweden)

    Rongdong Hu

    2014-01-01

    Full Text Available Cloud providers should ensure QoS while maximizing resources utilization. One optimal strategy is to timely allocate resources in a fine-grained mode according to application’s actual resources demand. The necessary precondition of this strategy is obtaining future load information in advance. We propose a multi-step-ahead load forecasting method, KSwSVR, based on statistical learning theory which is suitable for the complex and dynamic characteristics of the cloud computing environment. It integrates an improved support vector regression algorithm and Kalman smoother. Public trace data taken from multitypes of resources were used to verify its prediction accuracy, stability, and adaptability, comparing with AR, BPNN, and standard SVR. Subsequently, based on the predicted results, a simple and efficient strategy is proposed for resource provisioning. CPU allocation experiment indicated it can effectively reduce resources consumption while meeting service level agreements requirements.

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

    Directory of Open Access Journals (Sweden)

    B. Jency Paulin

    2016-01-01

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

  20. Short-range solar radiation forecasts over Sweden

    Directory of Open Access Journals (Sweden)

    T. Landelius

    2018-04-01

    Full Text Available In this article the performance for short-range solar radiation forecasts by the global deterministic and ensemble models from the European Centre for Medium-Range Weather Forecasts (ECMWF is compared with an ensemble of the regional mesoscale model HARMONIE-AROME used by the national meteorological services in Sweden, Norway and Finland. Note however that only the control members and the ensemble means are included in the comparison. The models resolution differs considerably with 18 km for the ECMWF ensemble, 9 km for the ECMWF deterministic model, and 2.5 km for the HARMONIE-AROME ensemble.The models share the same radiation code. It turns out that they all underestimate systematically the Direct Normal Irradiance (DNI for clear-sky conditions. Except for this shortcoming, the HARMONIE-AROME ensemble model shows the best agreement with the distribution of observed Global Horizontal Irradiance (GHI and DNI values. During mid-day the HARMONIE-AROME ensemble mean performs best. The control member of the HARMONIE-AROME ensemble also scores better than the global deterministic ECMWF model. This is an interesting result since mesoscale models have so far not shown good results when compared to the ECMWF models.Three days with clear, mixed and cloudy skies are used to illustrate the possible added value of a probabilistic forecast. It is shown that in these cases the mesoscale ensemble could provide decision support to a grid operator in terms of forecasts of both the amount of solar power and its probabilities.

  1. Distributed energy resources scheduling considering real-time resources forecast

    DEFF Research Database (Denmark)

    Silva, M.; Sousa, T.; Ramos, S.

    2014-01-01

    grids and considering day-ahead, hour-ahead and realtime time horizons. This method considers that energy resources are managed by a VPP which establishes contracts with their owners. The full AC power flow calculation included in the model takes into account network constraints. In this paper......, distribution function errors are used to simulate variations between time horizons, and to measure the performance of the proposed methodology. A 33-bus distribution network with large number of distributed resources is used....

  2. 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

  3. Wind and solar resource data sets

    DEFF Research Database (Denmark)

    Clifton, Andrew; Hodge, Bri-Mathias; Draxl, Caroline

    2017-01-01

    The range of resource data sets spans from static cartography showing the mean annual wind speed or solar irradiance across a region to high temporal and high spatial resolution products that provide detailed information at a potential wind or solar energy facility. These data sets are used...... to support continental-scale, national, or regional renewable energy development; facilitate prospecting by developers; and enable grid integration studies. This review first provides an introduction to the wind and solar resource data sets, then provides an overview of the common methods used...... for their creation and validation. A brief history of wind and solar resource data sets is then presented, followed by areas for future research. For further resources related to this article, please visit the WIREs website....

  4. A Novel Forecasting System for Solar Particle Events and Flares (FORSPEF)

    International Nuclear Information System (INIS)

    Papaioannou, A; Anastasiadis, A; Sandberg, I; Tsiropoula, G; Tziotziou, K; Georgoulis, M K; Jiggens, P; Hilgers, A

    2015-01-01

    Solar Energetic Particles (SEPs) result from intense solar eruptive events such as solar flares and coronal mass ejections (CMEs) and pose a significant threat for both personnel and infrastructure in space conditions. In this work, we present FORSPEF (Forecasting Solar Particle Events and Flares), a novel dual system, designed to perform forecasting of SEPs based on forecasting of solar flares, as well as independent SEP nowcasting. An overview of flare and SEP forecasting methods of choice is presented. Concerning SEP events, we make use for the first time of the newly re-calibrated GOES proton data within the energy range 6.0-243 MeV and we build our statistics on an extensive time interval that includes roughly 3 solar cycles (1984-2013). A new comprehensive catalogue of SEP events based on these data has been compiled including solar associations in terms of flare (magnitude, location) and CME (width, velocity) characteristics. (paper)

  5. Short-term solar irradiation forecasting based on Dynamic Harmonic Regression

    International Nuclear Information System (INIS)

    Trapero, Juan R.; Kourentzes, Nikolaos; Martin, A.

    2015-01-01

    Solar power generation is a crucial research area for countries that have high dependency on fossil energy sources and is gaining prominence with the current shift to renewable sources of energy. In order to integrate the electricity generated by solar energy into the grid, solar irradiation must be reasonably well forecasted, where deviations of the forecasted value from the actual measured value involve significant costs. The present paper proposes a univariate Dynamic Harmonic Regression model set up in a State Space framework for short-term (1–24 h) solar irradiation forecasting. Time series hourly aggregated as the Global Horizontal Irradiation and the Direct Normal Irradiation will be used to illustrate the proposed approach. This method provides a fast automatic identification and estimation procedure based on the frequency domain. Furthermore, the recursive algorithms applied offer adaptive predictions. The good forecasting performance is illustrated with solar irradiance measurements collected from ground-based weather stations located in Spain. The results show that the Dynamic Harmonic Regression achieves the lowest relative Root Mean Squared Error; about 30% and 47% for the Global and Direct irradiation components, respectively, for a forecast horizon of 24 h ahead. - Highlights: • Solar irradiation forecasts at short-term are required to operate solar power plants. • This paper assesses the Dynamic Harmonic Regression to forecast solar irradiation. • Models are evaluated using hourly GHI and DNI data collected in Spain. • The results show that forecasting accuracy is improved by using the model proposed

  6. Deep Learning Based Solar Flare Forecasting Model. I. Results for Line-of-sight Magnetograms

    Science.gov (United States)

    Huang, Xin; Wang, Huaning; Xu, Long; Liu, Jinfu; Li, Rong; Dai, Xinghua

    2018-03-01

    Solar flares originate from the release of the energy stored in the magnetic field of solar active regions, the triggering mechanism for these flares, however, remains unknown. For this reason, the conventional solar flare forecast is essentially based on the statistic relationship between solar flares and measures extracted from observational data. In the current work, the deep learning method is applied to set up the solar flare forecasting model, in which forecasting patterns can be learned from line-of-sight magnetograms of solar active regions. In order to obtain a large amount of observational data to train the forecasting model and test its performance, a data set is created from line-of-sight magnetogarms of active regions observed by SOHO/MDI and SDO/HMI from 1996 April to 2015 October and corresponding soft X-ray solar flares observed by GOES. The testing results of the forecasting model indicate that (1) the forecasting patterns can be automatically reached with the MDI data and they can also be applied to the HMI data; furthermore, these forecasting patterns are robust to the noise in the observational data; (2) the performance of the deep learning forecasting model is not sensitive to the given forecasting periods (6, 12, 24, or 48 hr); (3) the performance of the proposed forecasting model is comparable to that of the state-of-the-art flare forecasting models, even if the duration of the total magnetograms continuously spans 19.5 years. Case analyses demonstrate that the deep learning based solar flare forecasting model pays attention to areas with the magnetic polarity-inversion line or the strong magnetic field in magnetograms of active regions.

  7. Forecasting of integral parameters of solar cosmic ray events according to initial characteristics of an event

    International Nuclear Information System (INIS)

    Belovskij, M.N.; Ochelkov, Yu.P.

    1981-01-01

    The forecasting method for an integral proton flux of solar cosmic rays (SCR) based on the initial characteristics of the phe-- nomenon is proposed. The efficiency of the method is grounded. The accuracy of forecasting is estimated and the retrospective forecasting of real events is carried out. The parameters of the universal function describing the time progress of the SCR events are pre-- sented. The proposed method is suitable for forecasting practically all the SCR events. The timeliness of the given forecasting is not worse than that of the forecasting based on utilization of the SCR propagation models [ru

  8. Resource Letter OSE-1: Observing Solar Eclipses

    Science.gov (United States)

    Pasachoff, Jay M.; Fraknoi, Andrew

    2017-07-01

    This Resource Letter provides a guide to the available literature, listing selected books, articles, and online resources about scientific, cultural, and practical issues related to observing solar eclipses. It is timely, given that a total solar eclipse will cross the continental United States on August 21, 2017. The next total solar eclipse path crossing the U.S. and Canada will be on April 8, 2024. In 2023, the path of annularity of an annular eclipse will cross Mexico, the United States, and Canada, with partial phases visible throughout those countries.

  9. Forecasting the peak of the present solar activity cycle 24

    Science.gov (United States)

    Hamid, R. H.; Marzouk, B. A.

    2018-06-01

    Solar forecasting of the level of sun Activity is very important subject for all space programs. Most predictions are based on the physical conditions prevailing at or before the solar cycle minimum preceding the maximum in question. Our aim is to predict the maximum peak of cycle 24 using precursor techniques in particular those using spotless event, geomagnetic aamin. index and solar flux F10.7. Also prediction of exact date of the maximum (Tr) is taken in consideration. A study of variation over previous spotless event for cycles 7-23 and that for even cycles (8-22) are carried out for the prediction. Linear correlation between maximum of solar cycles (RM) and spotless event around the preceding minimum gives R24t = 88.4 with rise time Tr = 4.6 years. For the even cycles R24E = 77.9 with rise time Tr = 4.5 y's. Based on the average aamin. index for cycles (12-23), we estimate the expected amplitude for cycle 24 to be Raamin = 99.4 and 98.1 with time rise of Traamin = 4.04 & 4.3 years for both the total and even cycles in consecutive. The application of the data of solar flux F10.7 which cover only cycles (19-23) was taken in consideration and gives predicted maximum amplitude R24 10.7 = 126 with rise time Tr107 = 3.7 years, which are over estimation. Our result indicating to somewhat weaker of cycle 24 as compared to cycles 21-23.

  10. Workplace Electric Vehicle Solar Smart Charging based on Solar Irradiance Forecasting

    OpenAIRE

    Almquist, Isabelle; Lindblom, Ellen; Birging, Alfred

    2017-01-01

    The purpose of this bachelor thesis is to investigate different outcomes of the usage of photovoltaic (PV) power for electric vehicle (EV) charging adjacent to workplaces. In the investigated case, EV charging stations are assumed to be connected to photovoltaic systems as well as the electricity grid. The model used to simulate different scenarios is based on a goal of achieving constant power exchange with the grid by adjusting EV charging to a solar irradiance forecast. The model is implem...

  11. 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.

  12. National forecast for geothermal resource exploration and development with techniques for policy analysis and resource assessment

    Energy Technology Data Exchange (ETDEWEB)

    Cassel, T.A.V.; Shimamoto, G.T.; Amundsen, C.B.; Blair, P.D.; Finan, W.F.; Smith, M.R.; Edeistein, R.H.

    1982-03-31

    The backgrund, structure and use of modern forecasting methods for estimating the future development of geothermal energy in the United States are documented. The forecasting instrument may be divided into two sequential submodels. The first predicts the timing and quality of future geothermal resource discoveries from an underlying resource base. This resource base represents an expansion of the widely-publicized USGS Circular 790. The second submodel forecasts the rate and extent of utilization of geothermal resource discoveries. It is based on the joint investment behavior of resource developers and potential users as statistically determined from extensive industry interviews. It is concluded that geothermal resource development, especially for electric power development, will play an increasingly significant role in meeting US energy demands over the next 2 decades. Depending on the extent of R and D achievements in related areas of geosciences and technology, expected geothermal power development will reach between 7700 and 17300 Mwe by the year 2000. This represents between 8 and 18% of the expected electric energy demand (GWh) in western and northwestern states.

  13. Probabilistic forecasting of the solar irradiance with recursive ARMA and GARCH models

    DEFF Research Database (Denmark)

    David, M.; Ramahatana, F.; Trombe, Pierre-Julien

    2016-01-01

    Forecasting of the solar irradiance is a key feature in order to increase the penetration rate of solar energy into the energy grids. Indeed, the anticipation of the fluctuations of the solar renewables allows a better management of the production means of electricity and a better operation...... sky index show some similarities with that of financial time series. The aim of this paper is to assess the performances of a commonly used combination of two linear models (ARMA and GARCH) in econometrics in order to provide probabilistic forecasts of solar irradiance. In addition, a recursive...... regarding the statistical distribution of the error, the reliability of the probabilistic forecasts stands in the same order of magnitude as other works done in the field of solar forecasting....

  14. A Multi-scale, Multi-Model, Machine-Learning Solar Forecasting Technology

    Energy Technology Data Exchange (ETDEWEB)

    Hamann, Hendrik F. [IBM, Yorktown Heights, NY (United States). Thomas J. Watson Research Center

    2017-05-31

    The goal of the project was the development and demonstration of a significantly improved solar forecasting technology (short: Watt-sun), which leverages new big data processing technologies and machine-learnt blending between different models and forecast systems. The technology aimed demonstrating major advances in accuracy as measured by existing and new metrics which themselves were developed as part of this project. Finally, the team worked with Independent System Operators (ISOs) and utilities to integrate the forecasts into their operations.

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

    DEFF Research Database (Denmark)

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

    2017-01-01

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

  16. Evaluating the spatio-temporal performance of sky imager based solar irradiance analysis and forecasts

    Science.gov (United States)

    Schmidt, T.; Kalisch, J.; Lorenz, E.; Heinemann, D.

    2015-10-01

    Clouds are the dominant source of variability in surface solar radiation and uncertainty in its prediction. However, the increasing share of solar energy in the world-wide electric power supply increases the need for accurate solar radiation forecasts. In this work, we present results of a shortest-term global horizontal irradiance (GHI) forecast experiment based on hemispheric sky images. A two month dataset with images from one sky imager and high resolutive GHI measurements from 99 pyranometers distributed over 10 km by 12 km is used for validation. We developed a multi-step model and processed GHI forecasts up to 25 min with an update interval of 15 s. A cloud type classification is used to separate the time series in different cloud scenarios. Overall, the sky imager based forecasts do not outperform the reference persistence forecasts. Nevertheless, we find that analysis and forecast performance depend strongly on the predominant cloud conditions. Especially convective type clouds lead to high temporal and spatial GHI variability. For cumulus cloud conditions, the analysis error is found to be lower than that introduced by a single pyranometer if it is used representatively for the whole area in distances from the camera larger than 1-2 km. Moreover, forecast skill is much higher for these conditions compared to overcast or clear sky situations causing low GHI variability which is easier to predict by persistence. In order to generalize the cloud-induced forecast error, we identify a variability threshold indicating conditions with positive forecast skill.

  17. Solmap: Project In India's Solar Resource Assessment

    Directory of Open Access Journals (Sweden)

    Indradip Mitra

    2014-12-01

    Full Text Available India launched Jawaharlal Nehru National Solar Mission in 2009, which aims to set up 20 000 MW of grid connected solar power, besides 2 000 MW equivalent of off-grid applications and cumulative growth of solar thermal collector area to 20 million m2 by 2022. Availability of reliable and accurate solar radiation data is crucial to achieve the targets. As a result of this initiative, Ministry of New and Renewable Energy (MNRE of Government of India (GoI has awarded a project to Centre for Wind Energy Technology (C-WET, Chennai in the year 2011 to set up 51 Solar Radiation Resource Assessment (SRRA stations using the state-of-the-art equipment in various parts of the country, especially the sites with high potential for solar power. The GoI project has synergy with SolMap project, which is implemented by the Deutsche GesellschaftfürInternationaleZusammenarbeit (GIZ in cooperation with the MNRE. SolMap project is contributing to SRRA project in establishing quality checks on the data obtained as per International protocols and helping data processing to generate investment grade data. The paper highlights the details of SRRA stations and an attempt has been made to present some of the important results of quality control and data analysis with respect to GHI and DNI. While our analysis of the data over one year finds that intensity and profile of the insolation are not uniform across the geographic regions, the variability in DNI is particularly high. Strong influence of monsoon is also identified. SRRA infrastructure aims to develop investment grade solar radiation resource information to assist project activities under the National Solar Mission of India.

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

    OpenAIRE

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

    2015-01-01

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

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

    OpenAIRE

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

    2015-01-01

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

  20. 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

  1. Nonlinear techniques for forecasting solar activity directly from its time series

    Science.gov (United States)

    Ashrafi, S.; Roszman, L.; Cooley, J.

    1993-01-01

    This paper presents numerical techniques for constructing nonlinear predictive models to forecast solar flux directly from its time series. This approach makes it possible to extract dynamical in variants of our system without reference to any underlying solar physics. We consider the dynamical evolution of solar activity in a reconstructed phase space that captures the attractor (strange), give a procedure for constructing a predictor of future solar activity, and discuss extraction of dynamical invariants such as Lyapunov exponents and attractor dimension.

  2. Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications: Second Edition

    Energy Technology Data Exchange (ETDEWEB)

    Sengupta, Manajit [National Renewable Energy Lab. (NREL), Golden, CO (United States); Habte, Aron [National Renewable Energy Lab. (NREL), Golden, CO (United States); Gueymard, Christian [Solar Consulting Services, Daytona Beach, FL (United States); Wilbert, Stefan [German Aerospace Center (DLR), Cologne (Germany); Renne, Dave [Dave Renne Renewables, LLC, Boulder, CO (United States)

    2017-12-01

    As the world looks for low-carbon sources of energy, solar power stands out as the single most abundant energy resource on Earth. Harnessing this energy is the challenge for this century. Photovoltaics, solar heating and cooling, and concentrating solar power (CSP) are primary forms of energy applications using sunlight. These solar energy systems use different technologies, collect different fractions of the solar resource, and have different siting requirements and production capabilities. Reliable information about the solar resource is required for every solar energy application. This holds true for small installations on a rooftop as well as for large solar power plants; however, solar resource information is of particular interest for large installations, because they require substantial investment, sometimes exceeding 1 billion dollars in construction costs. Before such a project is undertaken, the best possible information about the quality and reliability of the fuel source must be made available. That is, project developers need reliable data about the solar resource available at specific locations, including historic trends with seasonal, daily, hourly, and (preferably) subhourly variability to predict the daily and annual performance of a proposed power plant. Without this data, an accurate financial analysis is not possible. Additionally, with the deployment of large amounts of distributed photovoltaics, there is an urgent need to integrate this source of generation to ensure the reliability and stability of the grid. Forecasting generation from the various sources will allow for larger penetrations of these generation sources because utilities and system operators can then ensure stable grid operations. Developed by the foremost experts in the field who have come together under the umbrella of the International Energy Agency's Solar Heating and Cooling Task 46, this handbook summarizes state-of-the-art information about all the above topics.

  3. Verification of ECMWF and ECMWF/MACC's global and direct irradiance forecasts with respect to solar electricity production forecasts

    Directory of Open Access Journals (Sweden)

    M. Schroedter-Homscheidt

    2017-02-01

    Full Text Available The successful electricity grid integration of solar energy into day-ahead markets requires at least hourly resolved 48 h forecasts. Technologies as photovoltaics and non-concentrating solar thermal technologies make use of global horizontal irradiance (GHI forecasts, while all concentrating technologies both from the photovoltaic and the thermal sector require direct normal irradiances (DNI. The European Centre for Medium-Range Weather Forecasts (ECMWF has recently changed towards providing direct as well as global irradiances. Additionally, the MACC (Monitoring Atmospheric Composition & Climate near-real time services provide daily analysis and forecasts of aerosol properties in preparation of the upcoming European Copernicus programme. The operational ECMWF/IFS (Integrated Forecast System forecast system will in the medium term profit from the Copernicus service aerosol forecasts. Therefore, within the MACC‑II project specific experiment runs were performed allowing for the assessment of the performance gain of these potential future capabilities. Also the potential impact of providing forecasts with hourly output resolution compared to three-hourly resolved forecasts is investigated. The inclusion of the new aerosol climatology in October 2003 improved both the GHI and DNI forecasts remarkably, while the change towards a new radiation scheme in 2007 only had minor and partly even unfavourable impacts on the performance indicators. For GHI, larger RMSE (root mean square error values are found for broken/overcast conditions than for scattered cloud fields. For DNI, the findings are opposite with larger RMSE values for scattered clouds compared to overcast/broken cloud situations. The introduction of direct irradiances as an output parameter in the operational IFS version has not resulted in a general performance improvement with respect to biases and RMSE compared to the widely used Skartveit et al. (1998 global to direct irradiance

  4. Forecasting disk resource requirements for a Usenet server

    International Nuclear Information System (INIS)

    Swartz, K.L.

    1993-09-01

    Three Years ago the Stanford Linear Accelerator Center (SLAC) decided to embrace netnews as a site-wide, multi-platform communications tool for the laboratory's diverse user community. The Usenet newsgroups as well as other world-wide newsgroup hierarchies were appealing for their unique ability to tap a broad pool of information, while the availability of the software on a number of platforms provided a way to communicate to and amongst the computing community. The previous way of doing this ran only on the VM mainframe system and had become increasingly ineffective as users migrated to other platforms. The increasing dependence on netnews brought with it the requirement that the service be reliable. This was dramatically demonstrated when the long-neglected netnews service collapsed under the load of the traditional fall surge in Usenet traffic and the site was without news service for a week while an upgraded system was installed. One result of that painful event was that efforts were made to forecast growth and the accompanying hardware requirements so that equipment could be acquired and installed before problems became visible to the users. This paper describes the major on-disk databases associated with news software, then presents an analysis of the storage requirements for these databases based on data collected at SLAC. A model is developed from this data which permits forecasting of disk resource requirements for a full feed as a function of time and local policies. Suggestions are also made as to how to modify this model for sites which do not carry a full feed

  5. Study of hourly and daily solar irradiation forecast using diagonal recurrent wavelet neural networks

    International Nuclear Information System (INIS)

    Cao Jiacong; Lin Xingchun

    2008-01-01

    An accurate forecast of solar irradiation is required for various solar energy applications and environmental impact analyses in recent years. Comparatively, various irradiation forecast models based on artificial neural networks (ANN) perform much better in accuracy than many conventional prediction models. However, the forecast precision of most existing ANN based forecast models has not been satisfactory to researchers and engineers so far, and the generalization capability of these networks needs further improving. Combining the prominent dynamic properties of a recurrent neural network (RNN) with the enhanced ability of a wavelet neural network (WNN) in mapping nonlinear functions, a diagonal recurrent wavelet neural network (DRWNN) is newly established in this paper to perform fine forecasting of hourly and daily global solar irradiance. Some additional steps, e.g. applying historical information of cloud cover to sample data sets and the cloud cover from the weather forecast to network input, are adopted to help enhance the forecast precision. Besides, a specially scheduled two phase training algorithm is adopted. As examples, both hourly and daily irradiance forecasts are completed using sample data sets in Shanghai and Macau, and comparisons between irradiation models show that the DRWNN models are definitely more accurate

  6. Evaluating Solar Resource Data Obtained from Multiple Radiometers Deployed at the National Renewable Energy Laboratory: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Habte, A.; Sengupta, M.; Andreas, A.; Wilcox, S.; Stoffel, T.

    2014-09-01

    Solar radiation resource measurements from radiometers are used to predict and evaluate the performance of photovoltaic and concentrating solar power systems, validate satellite-based models for estimating solar resources, and advance research in solar forecasting and climate change. This study analyzes the performance of various commercially available radiometers used for measuring global horizontal irradiances (GHI) and direct normal irradiances (DNI). These include pyranometers, pyrheliometers, rotating shadowband irradiometers, and a pyranometer with a shading ring deployed at the National Renewable Energy Laboratory's Solar Radiation Research Laboratory (SRRL). The radiometers in this study were deployed for one year (from April 1, 2011, through March 31, 2012) and compared to measurements from radiometers with the lowest values of estimated measurement uncertainties for producing reference GHI and DNI.

  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. A stochastic post-processing method for solar irradiance forecasts derived from NWPs models

    Science.gov (United States)

    Lara-Fanego, V.; Pozo-Vazquez, D.; Ruiz-Arias, J. A.; Santos-Alamillos, F. J.; Tovar-Pescador, J.

    2010-09-01

    Solar irradiance forecast is an important area of research for the future of the solar-based renewable energy systems. Numerical Weather Prediction models (NWPs) have proved to be a valuable tool for solar irradiance forecasting with lead time up to a few days. Nevertheless, these models show low skill in forecasting the solar irradiance under cloudy conditions. Additionally, climatic (averaged over seasons) aerosol loading are usually considered in these models, leading to considerable errors for the Direct Normal Irradiance (DNI) forecasts during high aerosols load conditions. In this work we propose a post-processing method for the Global Irradiance (GHI) and DNI forecasts derived from NWPs. Particularly, the methods is based on the use of Autoregressive Moving Average with External Explanatory Variables (ARMAX) stochastic models. These models are applied to the residuals of the NWPs forecasts and uses as external variables the measured cloud fraction and aerosol loading of the day previous to the forecast. The method is evaluated for a set one-moth length three-days-ahead forecast of the GHI and DNI, obtained based on the WRF mesoscale atmospheric model, for several locations in Andalusia (Southern Spain). The Cloud fraction is derived from MSG satellite estimates and the aerosol loading from the MODIS platform estimates. Both sources of information are readily available at the time of the forecast. Results showed a considerable improvement of the forecasting skill of the WRF model using the proposed post-processing method. Particularly, relative improvement (in terms of the RMSE) for the DNI during summer is about 20%. A similar value is obtained for the GHI during the winter.

  9. Wind and solar resource data sets: Wind and solar resource data sets

    Energy Technology Data Exchange (ETDEWEB)

    Clifton, Andrew [National Renewable Energy Laboratory, Golden CO USA; Hodge, Bri-Mathias [National Renewable Energy Laboratory, Golden CO USA; Power Systems Engineering Center, National Renewable Energy Laboratory, Golden CO USA; Draxl, Caroline [National Renewable Energy Laboratory, Golden CO USA; National Wind Technology Center, National Renewable Energy Laboratory, Golden CO USA; Badger, Jake [Department of Wind Energy, Danish Technical University, Copenhagen Denmark; Habte, Aron [National Renewable Energy Laboratory, Golden CO USA; Power Systems Engineering Center, National Renewable Energy Laboratory, Golden CO USA

    2017-12-05

    The range of resource data sets spans from static cartography showing the mean annual wind speed or solar irradiance across a region to high temporal and high spatial resolution products that provide detailed information at a potential wind or solar energy facility. These data sets are used to support continental-scale, national, or regional renewable energy development; facilitate prospecting by developers; and enable grid integration studies. This review first provides an introduction to the wind and solar resource data sets, then provides an overview of the common methods used for their creation and validation. A brief history of wind and solar resource data sets is then presented, followed by areas for future research.

  10. Problems in the forecasting of solar particle events for manned missions

    International Nuclear Information System (INIS)

    Feynman, J.; Ruzmaikin, A.

    1999-01-01

    Manned spacecraft will require a much improved ability to forecast solar particle events. The lead time required will depend on the use to which the forecast is put. Here we discuss problems of forecasting with the lead times of hours to weeks. Such forecasts are needed for scheduling and carrying out activities. Our present capabilities with these lead times is extremely limited. To improve our capability we must develop an ability to predict fast coronal mass ejections (CMEs). It is not sufficient to observe that a CME has already taken place since by that time it is already too late to make predictions with these lead times. Both to learn how to predict CMEs and to carry out forecasts on time scales of several days to weeks, observations of the other side of the Sun are required. We describe a low-cost space mission of this type that would further the development of an hours-to-weeks forecast capability

  11. Optimizing Re-planning Operation for Smart House Applying Solar Radiation Forecasting

    Directory of Open Access Journals (Sweden)

    Atsushi Yona

    2014-08-01

    Full Text Available This paper proposes the re-planning operation method using Tabu Search for direct current (DC smart house with photovoltaic (PV, solar collector (SC, battery and heat pump system. The proposed method is based on solar radiation forecasting using reported weather data, Fuzzy theory and Recurrent Neural Network. Additionally, the re-planning operation method is proposed with consideration of solar radiation forecast error, battery and inverter losses. In this paper, it is assumed that the installation location for DC smart house is Okinawa, which is located in Southwest Japan. The validity of proposed method is confirmed by comparing the simulation results.

  12. 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.

  13. Forecast Method of Solar Irradiance with Just-In-Time Modeling

    Science.gov (United States)

    Suzuki, Takanobu; Goto, Yusuke; Terazono, Takahiro; Wakao, Shinji; Oozeki, Takashi

    PV power output mainly depends on the solar irradiance which is affected by various meteorological factors. So, it is required to predict solar irradiance in the future for the efficient operation of PV systems. In this paper, we develop a novel approach for solar irradiance forecast, in which we introduce to combine the black-box model (JIT Modeling) with the physical model (GPV data). We investigate the predictive accuracy of solar irradiance over wide controlled-area of each electric power company by utilizing the measured data on the 44 observation points throughout Japan offered by JMA and the 64 points around Kanto by NEDO. Finally, we propose the application forecast method of solar irradiance to the point which is difficulty in compiling the database. And we consider the influence of different GPV default time on solar irradiance prediction.

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

    Science.gov (United States)

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

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

  15. Comprehensive Solutions for Integration of Solar Resources into Grid Operations

    Energy Technology Data Exchange (ETDEWEB)

    Pennock, Kenneth [AWS Truepower, LLC, Albany, NY (United States); Makarov, Yuri V. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Rajagopal, Sankaran [Siemens Energy, Erlangen (Germany); Loutan, Clyde [California Independent System Operator; Etingov, Pavel V. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Miller, Laurie E. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Lu, Bo [Siemens Energy, Erlangen (Germany); Mansingh, Ashmin [Siemens Energy, Erlangen (Germany); Zack, John [MESO, Inc., Raleigh, NC (United States); Sherick, Robert [Southern California Edison, Rosemead, CA (United States); Romo, Abraham [Southern California Edison; Habibi-Ashrafi, Farrokh [Southern California Edison; Johnson, Raymond [Southern California Edison

    2016-01-14

    The need for proactive closed-loop integration of uncertainty information into system operations and probability-based controls is widely recognized, but rarely implemented in system operations. Proactive integration for this project means that the information concerning expected uncertainty ranges for net load and balancing requirements, including required balancing capacity, ramping and ramp duration characteristics, will be fed back into the generation commitment and dispatch algorithms to modify their performance so that potential shortages of these characteristics can be prevented. This basic, yet important, premise is the motivating factor for this project. The achieved project goal is to demonstrate the benefit of such a system. The project quantifies future uncertainties, predicts additional system balancing needs including the prediction intervals for capacity and ramping requirements of future dispatch intervals, evaluates the impacts of uncertainties on transmission including the risk of overloads and voltage problems, and explores opportunities for intra-hour generation adjustments helping to provide more flexibility for system operators. The resulting benefits culminate in more reliable grid operation in the face of increased system uncertainty and variability caused by solar power. The project identifies that solar power does not require special separate penetration level restrictions or penalization for its intermittency. Ultimately, the collective consideration of all sources of intermittency distributed over a wide area unified with the comprehensive evaluation of various elements of balancing process, i.e. capacity, ramping, and energy requirements, help system operators more robustly and effectively balance generation against load and interchange. This project showed that doing so can facilitate more solar and other renewable resources on the grid without compromising reliability and control performance. Efforts during the project included

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

    International Nuclear Information System (INIS)

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

    2015-01-01

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

  17. Application and verification of the NMMB/BSC-CTM forecast for solar energy

    Science.gov (United States)

    Soret, Albert; Serradell, Kim; Piot, Matthias; Ortega, Daniel; Obiso, Vincenzo; Jorba, Oriol

    2016-04-01

    In the beginning of April 2014, northern Europe was affected by a mineral dust intrusion. On 4 April 2014, the power prediction for German solar installations was estimated as 21 GW, whereas the measured power production merely reached 11 GW. This strong overestimation significantly affected the hourly price in the wholesale electricity market: prices were firstly assessed at around 27 € /MWh but rapidly reached a level close to 150 € /MWh after recognizing the lack of solar output. It has been found that a large proportion of the uncertainty of existing NWP models can be attributed to the lack of accurate aerosol data used in order to model solar radiation. Despite the advancements in the modelling of aerosol-cloud interactions, current meteorological models use parameterizations made mostly for climate considerations (generally monthly-based). In this contribution, we analyse model results of the direct radiative effect of mineral dust over Germany at the beginning of April 2014. For that, the NMMB/BSC Chemical Transport Model (NMMB/BSC-CTM) is applied on a regional domain at 0.1° horizontal resolution. The NMMB/BSC-CTM is a new on-line chemical weather prediction system coupling atmospheric and chemistry processes. In the radiation module of the model, mineral dust is treated as a radiatively active substance interacting both short and longwave radiation. The impact of the mineral dust outbreaks on meteorology is discussed by comparing model forecasts meteorological observations. The analysis focuses on the performance of the NMMB/BSC-CTM to simulate the radiative effects of a mineral dust intrusion far from source regions. Model results would help to illustrate the added value of on-line models for long term analysis of solar resource. On-going developments: integration of anthropogenic sources and implementation of indirect radiative effects will be also presented.

  18. Forecasting hourly global solar radiation using hybrid k-means and nonlinear autoregressive neural network models

    International Nuclear Information System (INIS)

    Benmouiza, Khalil; Cheknane, Ali

    2013-01-01

    Highlights: • An unsupervised clustering algorithm with a neural network model was explored. • The forecasting results of solar radiation time series and the comparison of their performance was simulated. • A new method was proposed combining k-means algorithm and NAR network to provide better prediction results. - Abstract: In this paper, we review our work for forecasting hourly global horizontal solar radiation based on the combination of unsupervised k-means clustering algorithm and artificial neural networks (ANN). k-Means algorithm focused on extracting useful information from the data with the aim of modeling the time series behavior and find patterns of the input space by clustering the data. On the other hand, nonlinear autoregressive (NAR) neural networks are powerful computational models for modeling and forecasting nonlinear time series. Taking the advantage of both methods, a new method was proposed combining k-means algorithm and NAR network to provide better forecasting results

  19. Evaluating the spatio-temporal performance of sky-imager-based solar irradiance analysis and forecasts

    Science.gov (United States)

    Schmidt, Thomas; Kalisch, John; Lorenz, Elke; Heinemann, Detlev

    2016-03-01

    Clouds are the dominant source of small-scale variability in surface solar radiation and uncertainty in its prediction. However, the increasing share of solar energy in the worldwide electric power supply increases the need for accurate solar radiation forecasts. In this work, we present results of a very short term global horizontal irradiance (GHI) forecast experiment based on hemispheric sky images. A 2-month data set with images from one sky imager and high-resolution GHI measurements from 99 pyranometers distributed over 10 km by 12 km is used for validation. We developed a multi-step model and processed GHI forecasts up to 25 min with an update interval of 15 s. A cloud type classification is used to separate the time series into different cloud scenarios. Overall, the sky-imager-based forecasts do not outperform the reference persistence forecasts. Nevertheless, we find that analysis and forecast performance depends strongly on the predominant cloud conditions. Especially convective type clouds lead to high temporal and spatial GHI variability. For cumulus cloud conditions, the analysis error is found to be lower than that introduced by a single pyranometer if it is used representatively for the whole area in distances from the camera larger than 1-2 km. Moreover, forecast skill is much higher for these conditions compared to overcast or clear sky situations causing low GHI variability, which is easier to predict by persistence. In order to generalize the cloud-induced forecast error, we identify a variability threshold indicating conditions with positive forecast skill.

  20. Evaluating the spatio-temporal performance of sky-imager-based solar irradiance analysis and forecasts

    Directory of Open Access Journals (Sweden)

    T. Schmidt

    2016-03-01

    Full Text Available Clouds are the dominant source of small-scale variability in surface solar radiation and uncertainty in its prediction. However, the increasing share of solar energy in the worldwide electric power supply increases the need for accurate solar radiation forecasts. In this work, we present results of a very short term global horizontal irradiance (GHI forecast experiment based on hemispheric sky images. A 2-month data set with images from one sky imager and high-resolution GHI measurements from 99 pyranometers distributed over 10 km by 12 km is used for validation. We developed a multi-step model and processed GHI forecasts up to 25 min with an update interval of 15 s. A cloud type classification is used to separate the time series into different cloud scenarios. Overall, the sky-imager-based forecasts do not outperform the reference persistence forecasts. Nevertheless, we find that analysis and forecast performance depends strongly on the predominant cloud conditions. Especially convective type clouds lead to high temporal and spatial GHI variability. For cumulus cloud conditions, the analysis error is found to be lower than that introduced by a single pyranometer if it is used representatively for the whole area in distances from the camera larger than 1–2 km. Moreover, forecast skill is much higher for these conditions compared to overcast or clear sky situations causing low GHI variability, which is easier to predict by persistence. In order to generalize the cloud-induced forecast error, we identify a variability threshold indicating conditions with positive forecast skill.

  1. Hourly forecasting of global solar radiation based on multiscale decomposition methods: A hybrid approach

    International Nuclear Information System (INIS)

    Monjoly, Stéphanie; André, Maïna; Calif, Rudy; Soubdhan, Ted

    2017-01-01

    This paper introduces a new approach for the forecasting of solar radiation series at 1 h ahead. We investigated on several techniques of multiscale decomposition of clear sky index K_c data such as Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD) and Wavelet Decomposition. From these differents methods, we built 11 decomposition components and 1 residu signal presenting different time scales. We performed classic forecasting models based on linear method (Autoregressive process AR) and a non linear method (Neural Network model). The choice of forecasting method is adaptative on the characteristic of each component. Hence, we proposed a modeling process which is built from a hybrid structure according to the defined flowchart. An analysis of predictive performances for solar forecasting from the different multiscale decompositions and forecast models is presented. From multiscale decomposition, the solar forecast accuracy is significantly improved, particularly using the wavelet decomposition method. Moreover, multistep forecasting with the proposed hybrid method resulted in additional improvement. For example, in terms of RMSE error, the obtained forecasting with the classical NN model is about 25.86%, this error decrease to 16.91% with the EMD-Hybrid Model, 14.06% with the EEMD-Hybid model and to 7.86% with the WD-Hybrid Model. - Highlights: • Hourly forecasting of GHI in tropical climate with many cloud formation processes. • Clear sky Index decomposition using three multiscale decomposition methods. • Combination of multiscale decomposition methods with AR-NN models to predict GHI. • Comparison of the proposed hybrid model with the classical models (AR, NN). • Best results using Wavelet-Hybrid model in comparison with classical models.

  2. Very Short-term Nonparametric Probabilistic Forecasting of Renewable Energy Generation - with Application to Solar Energy

    DEFF Research Database (Denmark)

    Golestaneh, Faranak; Pinson, Pierre; Gooi, Hoay Beng

    2016-01-01

    Due to the inherent uncertainty involved in renewable energy forecasting, uncertainty quantification is a key input to maintain acceptable levels of reliability and profitability in power system operation. A proposal is formulated and evaluated here for the case of solar power generation, when only...... approach to generate very short-term predictive densities, i.e., for lead times between a few minutes to one hour ahead, with fast frequency updates. We rely on an Extreme Learning Machine (ELM) as a fast regression model, trained in varied ways to obtain both point and quantile forecasts of solar power...... generation. Four probabilistic methods are implemented as benchmarks. Rival approaches are evaluated based on a number of test cases for two solar power generation sites in different climatic regions, allowing us to show that our approach results in generation of skilful and reliable probabilistic forecasts...

  3. The Value of Seasonal Climate Forecasts in Managing Energy Resources.

    Science.gov (United States)

    Brown Weiss, Edith

    1982-04-01

    Research and interviews with officials of the United States energy industry and a systems analysis of decision making in a natural gas utility lead to the conclusion that seasonal climate forecasts would only have limited value in fine tuning the management of energy supply, even if the forecasts were more reliable and detailed than at present.On the other hand, reliable forecasts could be useful to state and local governments both as a signal to adopt long-term measures to increase the efficiency of energy use and to initiate short-term measures to reduce energy demand in anticipation of a weather-induced energy crisis.To be useful for these purposes, state governments would need better data on energy demand patterns and available energy supplies, staff competent to interpret climate forecasts, and greater incentive to conserve. The use of seasonal climate forecasts is not likely to be constrained by fear of legal action by those claiming to be injured by a possible incorrect forecast.

  4. 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.

  5. 1991 Pacific Northwest loads and resources study, Pacific Northwest economic and electricity use forecast

    International Nuclear Information System (INIS)

    1992-01-01

    This publication provides detailed documentation of the load forecast scenarios and assumptions used in preparing BPA's 1991 Pacific Northwest Loads and Resources Study (the Study). This is one of two technical appendices to the Study; the other appendix details the utility-specific loads and resources used in the Study. The load forecasts and assumption were developed jointly by Bonneville Power Administration (BPA) and Northwest Power Planning Council (Council) staff. This forecast is also used in the Council's 1991 Northwest Conservation and Electric Power Plan (1991 Plan)

  6. 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.

  7. Forecasting optimal solar energy supply in Jiangsu Province (China): a systematic approach using hybrid of weather and energy forecast models.

    Science.gov (United States)

    Zhao, Xiuli; Asante Antwi, Henry; Yiranbon, Ethel

    2014-01-01

    The idea of aggregating information is clearly recognizable in the daily lives of all entities whether as individuals or as a group, since time immemorial corporate organizations, governments, and individuals as economic agents aggregate information to formulate decisions. Energy planning represents an investment-decision problem where information needs to be aggregated from credible sources to predict both demand and supply of energy. To do this there are varying methods ranging from the use of portfolio theory to managing risk and maximizing portfolio performance under a variety of unpredictable economic outcomes. The future demand for energy and need to use solar energy in order to avoid future energy crisis in Jiangsu province in China require energy planners in the province to abandon their reliance on traditional, "least-cost," and stand-alone technology cost estimates and instead evaluate conventional and renewable energy supply on the basis of a hybrid of optimization models in order to ensure effective and reliable supply. Our task in this research is to propose measures towards addressing optimal solar energy forecasting by employing a systematic optimization approach based on a hybrid of weather and energy forecast models. After giving an overview of the sustainable energy issues in China, we have reviewed and classified the various models that existing studies have used to predict the influences of the weather influences and the output of solar energy production units. Further, we evaluate the performance of an exemplary ensemble model which combines the forecast output of two popular statistical prediction methods using a dynamic weighting factor.

  8. Forecasting Optimal Solar Energy Supply in Jiangsu Province (China: A Systematic Approach Using Hybrid of Weather and Energy Forecast Models

    Directory of Open Access Journals (Sweden)

    Xiuli Zhao

    2014-01-01

    Full Text Available The idea of aggregating information is clearly recognizable in the daily lives of all entities whether as individuals or as a group, since time immemorial corporate organizations, governments, and individuals as economic agents aggregate information to formulate decisions. Energy planning represents an investment-decision problem where information needs to be aggregated from credible sources to predict both demand and supply of energy. To do this there are varying methods ranging from the use of portfolio theory to managing risk and maximizing portfolio performance under a variety of unpredictable economic outcomes. The future demand for energy and need to use solar energy in order to avoid future energy crisis in Jiangsu province in China require energy planners in the province to abandon their reliance on traditional, “least-cost,” and stand-alone technology cost estimates and instead evaluate conventional and renewable energy supply on the basis of a hybrid of optimization models in order to ensure effective and reliable supply. Our task in this research is to propose measures towards addressing optimal solar energy forecasting by employing a systematic optimization approach based on a hybrid of weather and energy forecast models. After giving an overview of the sustainable energy issues in China, we have reviewed and classified the various models that existing studies have used to predict the influences of the weather influences and the output of solar energy production units. Further, we evaluate the performance of an exemplary ensemble model which combines the forecast output of two popular statistical prediction methods using a dynamic weighting factor.

  9. Short-Term Solar Irradiance Forecasts Using Sky Images and Radiative Transfer Model

    Directory of Open Access Journals (Sweden)

    Juan Du

    2018-05-01

    Full Text Available In this paper, we propose a novel forecast method which addresses the difficulty in short-term solar irradiance forecasting that arises due to rapidly evolving environmental factors over short time periods. This involves the forecasting of Global Horizontal Irradiance (GHI that combines prediction sky images with a Radiative Transfer Model (RTM. The prediction images (up to 10 min ahead are produced by a non-local optical flow method, which is used to calculate the cloud motion for each pixel, with consecutive sky images at 1 min intervals. The Direct Normal Irradiance (DNI and the diffuse radiation intensity field under clear sky and overcast conditions obtained from the RTM are then mapped to the sky images. Through combining the cloud locations on the prediction image with the corresponding instance of image-based DNI and diffuse radiation intensity fields, the GHI can be quantitatively forecasted for time horizons of 1–10 min ahead. The solar forecasts are evaluated in terms of root mean square error (RMSE and mean absolute error (MAE in relation to in-situ measurements and compared to the performance of the persistence model. The results of our experiment show that GHI forecasts using the proposed method perform better than the persistence model.

  10. Tailored vs Black-Box Models for Forecasting Hourly Average Solar Irradiance

    Czech Academy of Sciences Publication Activity Database

    Brabec, Marek; Paulescu, M.; Badescu, V.

    2015-01-01

    Roč. 111, January (2015), s. 320-331 ISSN 0038-092X R&D Projects: GA MŠk LD12009 Grant - others:European Cooperation in Science and Technology(XE) COST ES1002 Institutional support: RVO:67985807 Keywords : solar irradiance * forecasting * tilored statistical models Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 3.685, year: 2015

  11. A Comparative Verification of Forecasts from Two Operational Solar Wind Models

    Science.gov (United States)

    2010-12-16

    knowing how much confidence to place on predicted parameters. Cost /benefit information is provided to administrators who decide to sustain or...components of the magnetic field vector in the geocentric solar magnetospheric (GSM) coordinate system at each hour of forecast time. For an example of a

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2016-04-19

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

  13. Solar activity simulation and forecast with a flux-transport dynamo

    Science.gov (United States)

    Macario-Rojas, Alejandro; Smith, Katharine L.; Roberts, Peter C. E.

    2018-06-01

    We present the assessment of a diffusion-dominated mean field axisymmetric dynamo model in reproducing historical solar activity and forecast for solar cycle 25. Previous studies point to the Sun's polar magnetic field as an important proxy for solar activity prediction. Extended research using this proxy has been impeded by reduced observational data record only available from 1976. However, there is a recognised need for a solar dynamo model with ample verification over various activity scenarios to improve theoretical standards. The present study aims to explore the use of helioseismology data and reconstructed solar polar magnetic field, to foster the development of robust solar activity forecasts. The research is based on observationally inferred differential rotation morphology, as well as observed and reconstructed polar field using artificial neural network methods via the hemispheric sunspot areas record. Results show consistent reproduction of historical solar activity trends with enhanced results by introducing a precursor rise time coefficient. A weak solar cycle 25, with slow rise time and maximum activity -14.4% (±19.5%) with respect to the current cycle 24 is predicted.

  14. Sensitivity analysis of numerical weather prediction radiative schemes to forecast direct solar radiation over Australia

    Science.gov (United States)

    Mukkavilli, S. K.; Kay, M. J.; Taylor, R.; Prasad, A. A.; Troccoli, A.

    2014-12-01

    The Australian Solar Energy Forecasting System (ASEFS) project requires forecasting timeframes which range from nowcasting to long-term forecasts (minutes to two years). As concentrating solar power (CSP) plant operators are one of the key stakeholders in the national energy market, research and development enhancements for direct normal irradiance (DNI) forecasts is a major subtask. This project involves comparing different radiative scheme codes to improve day ahead DNI forecasts on the national supercomputing infrastructure running mesoscale simulations on NOAA's Weather Research & Forecast (WRF) model. ASEFS also requires aerosol data fusion for improving accurate representation of spatio-temporally variable atmospheric aerosols to reduce DNI bias error in clear sky conditions over southern Queensland & New South Wales where solar power is vulnerable to uncertainities from frequent aerosol radiative events such as bush fires and desert dust. Initial results from thirteen years of Bureau of Meteorology's (BOM) deseasonalised DNI and MODIS NASA-Terra aerosol optical depth (AOD) anomalies demonstrated strong negative correlations in north and southeast Australia along with strong variability in AOD (~0.03-0.05). Radiative transfer schemes, DNI and AOD anomaly correlations will be discussed for the population and transmission grid centric regions where current and planned CSP plants dispatch electricity to capture peak prices in the market. Aerosol and solar irradiance datasets include satellite and ground based assimilations from the national BOM, regional aerosol researchers and agencies. The presentation will provide an overview of this ASEFS project task on WRF and results to date. The overall goal of this ASEFS subtask is to develop a hybrid numerical weather prediction (NWP) and statistical/machine learning multi-model ensemble strategy that meets future operational requirements of CSP plant operators.

  15. Wind/solar resource in Texas

    Energy Technology Data Exchange (ETDEWEB)

    Nelson, V.; Starcher, K.; Gaines, H. [West Texas A& M Univ., Canyon, TX (United States)

    1997-12-31

    Data are being collected at 17 sites to delineate a baseline for the wind and solar resource across Texas. Wind data are being collected at 10, 25, and 40 m (in some cases at 50 m) to determine wind shear and power at hub heights of large turbines. Many of the sites are located in areas of predicted terrain enhancement. The typical day in a month for power and wind turbine output was calculated for selected sites and combination of sites; distributed systems. Major result to date is that there is the possibility of load matching in South Texas during the summer months, even though the average values by month indicate a low wind potential.

  16. Solar radio bursts as a tool for space weather forecasting

    Science.gov (United States)

    Klein, Karl-Ludwig; Matamoros, Carolina Salas; Zucca, Pietro

    2018-01-01

    The solar corona and its activity induce disturbances that may affect the space environment of the Earth. Noticeable disturbances come from coronal mass ejections (CMEs), which are large-scale ejections of plasma and magnetic fields from the solar corona, and solar energetic particles (SEPs). These particles are accelerated during the explosive variation of the coronal magnetic field or at the shock wave driven by a fast CME. In this contribution, it is illustrated how full Sun microwave observations can lead to (1) an estimate of CME speeds and of the arrival time of the CME at the Earth, (2) the prediction of SEP events attaining the Earth. xml:lang="fr"

  17. 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.

  18. A distributed big data storage and data mining framework for solar-generated electricity quantity forecasting

    Science.gov (United States)

    Wang, Jianzong; Chen, Yanjun; Hua, Rui; Wang, Peng; Fu, Jia

    2012-02-01

    Photovoltaic is a method of generating electrical power by converting solar radiation into direct current electricity using semiconductors that exhibit the photovoltaic effect. Photovoltaic power generation employs solar panels composed of a number of solar cells containing a photovoltaic material. Due to the growing demand for renewable energy sources, the manufacturing of solar cells and photovoltaic arrays has advanced considerably in recent years. Solar photovoltaics are growing rapidly, albeit from a small base, to a total global capacity of 40,000 MW at the end of 2010. More than 100 countries use solar photovoltaics. Driven by advances in technology and increases in manufacturing scale and sophistication, the cost of photovoltaic has declined steadily since the first solar cells were manufactured. Net metering and financial incentives, such as preferential feed-in tariffs for solar-generated electricity; have supported solar photovoltaics installations in many countries. However, the power that generated by solar photovoltaics is affected by the weather and other natural factors dramatically. To predict the photovoltaic energy accurately is of importance for the entire power intelligent dispatch in order to reduce the energy dissipation and maintain the security of power grid. In this paper, we have proposed a big data system--the Solar Photovoltaic Power Forecasting System, called SPPFS to calculate and predict the power according the real-time conditions. In this system, we utilized the distributed mixed database to speed up the rate of collecting, storing and analysis the meteorological data. In order to improve the accuracy of power prediction, the given neural network algorithm has been imported into SPPFS.By adopting abundant experiments, we shows that the framework can provide higher forecast accuracy-error rate less than 15% and obtain low latency of computing by deploying the mixed distributed database architecture for solar-generated electricity.

  19. Intelligent optimization models based on hard-ridge penalty and RBF for forecasting global solar radiation

    International Nuclear Information System (INIS)

    Jiang, He; Dong, Yao; Wang, Jianzhou; Li, Yuqin

    2015-01-01

    Highlights: • CS-hard-ridge-RBF and DE-hard-ridge-RBF are proposed to forecast solar radiation. • Pearson and Apriori algorithm are used to analyze correlations between the data. • Hard-ridge penalty is added to reduce the number of nodes in the hidden layer. • CS algorithm and DE algorithm are used to determine the optimal parameters. • Proposed two models have higher forecasting accuracy than RBF and hard-ridge-RBF. - Abstract: Due to the scarcity of equipment and the high costs of maintenance, far fewer observations of solar radiation are made than observations of temperature, precipitation and other weather factors. Therefore, it is increasingly important to study several relevant meteorological factors to accurately forecast solar radiation. For this research, monthly average global solar radiation and 12 meteorological parameters from 1998 to 2010 at four sites in the United States were collected. Pearson correlation coefficients and Apriori association rules were successfully used to analyze correlations between the data, which provided a basis for these relative parameters as input variables. Two effective and innovative methods were developed to forecast monthly average global solar radiation by converting a RBF neural network into a multiple linear regression problem, adding a hard-ridge penalty to reduce the number of nodes in the hidden layer, and applying intelligent optimization algorithms, such as the cuckoo search algorithm (CS) and differential evolution (DE), to determine the optimal center and scale parameters. The experimental results show that the proposed models produce much more accurate forecasts than other models

  20. 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.

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

    Energy Technology Data Exchange (ETDEWEB)

    Woolsey, Lauren N.; Cranmer, Steven R. [Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138 (United States)

    2014-06-01

    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.

  2. Advanced Cloud Forecasting for Solar Energy's Impact on Grid Modernization

    International Nuclear Information System (INIS)

    Werth, D.; Nichols, R.

    2017-01-01

    Solar energy production is subject to variability in the solar resource - clouds and aerosols will reduce the available solar irradiance and inhibit power production. The fact that solar irradiance can vary by large amounts at small timescales and in an unpredictable way means that power utilities are reluctant to assign to their solar plants a large portion of future energy demand - the needed power might be unavailable, forcing the utility to make costly adjustments to its daily portfolio. The availability and predictability of solar radiation therefore represent important research topics for increasing the power produced by renewable sources.

  3. Advanced Cloud Forecasting for Solar Energy’s Impact on Grid Modernization

    Energy Technology Data Exchange (ETDEWEB)

    Werth, D. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL); Nichols, R. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL)

    2017-09-29

    Solar energy production is subject to variability in the solar resource – clouds and aerosols will reduce the available solar irradiance and inhibit power production. The fact that solar irradiance can vary by large amounts at small timescales and in an unpredictable way means that power utilities are reluctant to assign to their solar plants a large portion of future energy demand – the needed power might be unavailable, forcing the utility to make costly adjustments to its daily portfolio. The availability and predictability of solar radiation therefore represent important research topics for increasing the power produced by renewable sources.

  4. DATA ASSIMILATION APPROACH FOR FORECAST OF SOLAR ACTIVITY CYCLES

    Energy Technology Data Exchange (ETDEWEB)

    Kitiashvili, Irina N., E-mail: irina.n.kitiashvili@nasa.gov [NASA Ames Research Center, Moffett Field, Mountain View, CA 94035 (United States)

    2016-11-01

    Numerous attempts to predict future solar cycles are mostly based on empirical relations derived from observations of previous cycles, and they yield a wide range of predicted strengths and durations of the cycles. Results obtained with current dynamo models also deviate strongly from each other, thus raising questions about criteria to quantify the reliability of such predictions. The primary difficulties in modeling future solar activity are shortcomings of both the dynamo models and observations that do not allow us to determine the current and past states of the global solar magnetic structure and its dynamics. Data assimilation is a relatively new approach to develop physics-based predictions and estimate their uncertainties in situations where the physical properties of a system are not well-known. This paper presents an application of the ensemble Kalman filter method for modeling and prediction of solar cycles through use of a low-order nonlinear dynamo model that includes the essential physics and can describe general properties of the sunspot cycles. Despite the simplicity of this model, the data assimilation approach provides reasonable estimates for the strengths of future solar cycles. In particular, the prediction of Cycle 24 calculated and published in 2008 is so far holding up quite well. In this paper, I will present my first attempt to predict Cycle 25 using the data assimilation approach, and discuss the uncertainties of that prediction.

  5. Short-Term Solar Forecasting Performance of Popular Machine Learning Algorithms: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Florita, Anthony R [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Elgindy, Tarek [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Hodge, Brian S [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Dobbs, Alex [National Renewable Energy Laboratory (NREL), Golden, CO (United States)

    2017-10-03

    A framework for assessing the performance of short-term solar forecasting is presented in conjunction with a range of numerical results using global horizontal irradiation (GHI) from the open-source Surface Radiation Budget (SURFRAD) data network. A suite of popular machine learning algorithms is compared according to a set of statistically distinct metrics and benchmarked against the persistence-of-cloudiness forecast and a cloud motion forecast. Results show significant improvement compared to the benchmarks with trade-offs among the machine learning algorithms depending on the desired error metric. Training inputs include time series observations of GHI for a history of years, historical weather and atmospheric measurements, and corresponding date and time stamps such that training sensitivities might be inferred. Prediction outputs are GHI forecasts for 1, 2, 3, and 4 hours ahead of the issue time, and they are made for every month of the year for 7 locations. Photovoltaic power and energy outputs can then be made using the solar forecasts to better understand power system impacts.

  6. The Next Level in Automated Solar Flare Forecasting: the EU FLARECAST Project

    Science.gov (United States)

    Georgoulis, M. K.; Bloomfield, D.; Piana, M.; Massone, A. M.; Gallagher, P.; Vilmer, N.; Pariat, E.; Buchlin, E.; Baudin, F.; Csillaghy, A.; Soldati, M.; Sathiapal, H.; Jackson, D.; Alingery, P.; Argoudelis, V.; Benvenuto, F.; Campi, C.; Florios, K.; Gontikakis, C.; Guennou, C.; Guerra, J. A.; Kontogiannis, I.; Latorre, V.; Murray, S.; Park, S. H.; Perasso, A.; Sciacchitano, F.; von Stachelski, S.; Torbica, A.; Vischi, D.

    2017-12-01

    We attempt an informative description of the Flare Likelihood And Region Eruption Forecasting (FLARECAST) project, European Commission's first large-scale investment to explore the limits of reliability and accuracy achieved for the forecasting of major solar flares. We outline the consortium, top-level objectives and first results of the project, highlighting the diversity and fusion of expertise needed to deliver what was promised. The project's final product, featuring an openly accessible, fully modular and free to download flare forecasting facility will be delivered in early 2018. The project's three objectives, namely, science, research-to-operations and dissemination / communication, are also discussed: in terms of science, we encapsulate our close-to-final assessment on how close (or far) are we from a practically exploitable solar flare forecasting. In terms of R2O, we briefly describe the architecture of the FLARECAST infrastructure that includes rigorous validation for each forecasting step. From the three different communication levers of the project we finally focus on lessons learned from the two-way interaction with the community of stakeholders and governmental organizations. The FLARECAST project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 640216.

  7. A hybrid method based on a new clustering technique and multilayer perceptron neural networks for hourly solar radiation forecasting

    International Nuclear Information System (INIS)

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

    2016-01-01

    Highlights: • A novel clustering approach is proposed based on the data transformation approach. • A novel cluster selection method based on correlation analysis is presented. • The proposed hybrid clustering approach leads to deep learning for MLPNN. • A hybrid forecasting method is developed to predict solar radiations. • The evaluation results show superior performance of the proposed forecasting model. - Abstract: Accurate forecasting of renewable energy sources plays a key role in their integration into the grid. This paper proposes a hybrid solar irradiance forecasting framework using a Transformation based K-means algorithm, named TB K-means, to increase the forecast accuracy. The proposed clustering method is a combination of a new initialization technique, K-means algorithm and a new gradual data transformation approach. Unlike the other K-means based clustering methods which are not capable of providing a fixed and definitive answer due to the selection of different cluster centroids for each run, the proposed clustering provides constant results for different runs of the algorithm. The proposed clustering is combined with a time-series analysis, a novel cluster selection algorithm and a multilayer perceptron neural network (MLPNN) to develop the hybrid solar radiation forecasting method for different time horizons (1 h ahead, 2 h ahead, …, 48 h ahead). The performance of the proposed TB K-means clustering is evaluated using several different datasets and compared with different variants of K-means algorithm. Solar datasets with different solar radiation characteristics are also used to determine the accuracy and processing speed of the developed forecasting method with the proposed TB K-means and other clustering techniques. The results of direct comparison with other well-established forecasting models demonstrate the superior performance of the proposed hybrid forecasting method. Furthermore, a comparative analysis with the benchmark solar

  8. Forecast daily indices of solar activity, F10.7, using support vector regression method

    International Nuclear Information System (INIS)

    Huang Cong; Liu Dandan; Wang Jingsong

    2009-01-01

    The 10.7 cm solar radio flux (F10.7), the value of the solar radio emission flux density at a wavelength of 10.7 cm, is a useful index of solar activity as a proxy for solar extreme ultraviolet radiation. It is meaningful and important to predict F10.7 values accurately for both long-term (months-years) and short-term (days) forecasting, which are often used as inputs in space weather models. This study applies a novel neural network technique, support vector regression (SVR), to forecasting daily values of F10.7. The aim of this study is to examine the feasibility of SVR in short-term F10.7 forecasting. The approach, based on SVR, reduces the dimension of feature space in the training process by using a kernel-based learning algorithm. Thus, the complexity of the calculation becomes lower and a small amount of training data will be sufficient. The time series of F10.7 from 2002 to 2006 are employed as the data sets. The performance of the approach is estimated by calculating the norm mean square error and mean absolute percentage error. It is shown that our approach can perform well by using fewer training data points than the traditional neural network. (research paper)

  9. Forecast of the key parameters of the 24-th solar cycle

    International Nuclear Information System (INIS)

    Chumak, Oleg Vasilievich; Matveychuk, Tatiana Viktorovna

    2010-01-01

    To predict the key parameters of the solar cycle, a new method is proposed based on the empirical law describing the correlation between the maximum height of the preceding solar cycle and the entropy of the forthcoming one. The entropy of the forthcoming cycle may be estimated using this empirical law, if the maximum height of the current cycle is known. The cycle entropy is shown to correlate well with the cycle's maximum height and, as a consequence, the height of the forthcoming maximum can be estimated. In turn, the correlation found between the height of the maximum and the duration of the ascending branch (the Waldmeier rule) allows the epoch of the maximum, Tmax, to be estimated, if the date of the minimum is known. Moreover, using the law discovered, one can find out the analogous cycles which are similar to the cycle being forecasted, and hence, obtain the synoptic forecast of all main features of the forthcoming cycle. The estimates have shown the accuracy level of this technique to be 86%. The new regularities discovered are also interesting because they are fundamental in the theory of solar cycles and may provide new empirical data. The main parameters of the future solar cycle 24 are as follows: the height of the maximum is Wmax = 95 ± 20, the duration of the ascending branch is Ta = 4.5 ± 0.5yr, the total cycle duration according to the synoptic forecast is 11.3 yr. (research papers)

  10. ARTIFICIAL NEURAL NETWORK AND WAVELET DECOMPOSITION IN THE FORECAST OF GLOBAL HORIZONTAL SOLAR RADIATION

    Directory of Open Access Journals (Sweden)

    Luiz Albino Teixeira Júnior

    2015-04-01

    Full Text Available This paper proposes a method (denoted by WD-ANN that combines the Artificial Neural Networks (ANN and the Wavelet Decomposition (WD to generate short-term global horizontal solar radiation forecasting, which is an essential information for evaluating the electrical power generated from the conversion of solar energy into electrical energy. The WD-ANN method consists of two basic steps: firstly, it is performed the decomposition of level p of the time series of interest, generating p + 1 wavelet orthonormal components; secondly, the p + 1 wavelet orthonormal components (generated in the step 1 are inserted simultaneously into an ANN in order to generate short-term forecasting. The results showed that the proposed method (WD-ANN improved substantially the performance over the (traditional ANN method.

  11. Solar particle radiation storms forecasting and analysis the HESPERIA HORIZON 2020 project and beyond

    CERN Document Server

    Crosby, Norma

    2018-01-01

    Solar energetic particles (SEPs) emitted from the Sun are a major space weather hazard motivating the development of predictive capabilities. This book presents the results and findings of the HESPERIA (High Energy Solar Particle Events forecasting and Analysis) project of the EU HORIZON 2020 programme. It discusses the forecasting operational tools developed within the project, and presents progress to SEP research contributed by HESPERIA both from the observational as well as the SEP modelling perspective. Using multi-frequency observational data and simulations HESPERIA investigated the chain of processes from particle acceleration in the corona, particle transport in the magnetically complex corona and interplanetary space, to the detection near 1 AU. The book also elaborates on the unique software that has been constructed for inverting observations of relativistic SEPs to physical parameters that can be compared with spac e-borne measurements at lower energies. Introductory and pedagogical material incl...

  12. Future mission studies: Forecasting solar flux directly from its chaotic time series

    Science.gov (United States)

    Ashrafi, S.

    1991-01-01

    The mathematical structure of the programs written to construct a nonlinear predictive model to forecast solar flux directly from its time series without reference to any underlying solar physics is presented. This method and the programs are written so that one could apply the same technique to forecast other chaotic time series, such as geomagnetic data, attitude and orbit data, and even financial indexes and stock market data. Perhaps the most important application of this technique to flight dynamics is to model Goddard Trajectory Determination System (GTDS) output of residues between observed position of spacecraft and calculated position with no drag (drag flag = off). This would result in a new model of drag working directly from observed data.

  13. Univariate and multivariate forecasting of hourly solar radiation with artificial intelligence techniques

    Energy Technology Data Exchange (ETDEWEB)

    Sfetsos, A. [7 Pirsou Str., Athens (Greece); Coonick, A.H. [Imperial Coll. of Science Technology and Medicine, Dept. of Electrical and Electronic Engineering, London (United Kingdom)

    2000-07-01

    This paper introduces a new approach for the forecasting of mean hourly global solar radiation received by a horizontal surface. In addition to the traditional linear methods, several artificial-intelligence-based techniques are studied. These include linear, feed-forward, recurrent Elman and Radial Basis neural networks alongside the adaptive neuro-fuzzy inference scheme. The problem is examined initially for the univariate case, and is extended to include additional meteorological parameters in the process of estimating the optimum model. The results indicate that the developed artificial intelligence models predict the solar radiation time series more effectively compared to the conventional procedures based on the clearness index. The forecasting ability of some models can be further enhanced with the use of additional meteorological parameters. (Author)

  14. A Hierarchical Approach Using Machine Learning Methods in Solar Photovoltaic Energy Production Forecasting

    Directory of Open Access Journals (Sweden)

    Zhaoxuan Li

    2016-01-01

    Full Text Available We evaluate and compare two common methods, artificial neural networks (ANN and support vector regression (SVR, for predicting energy productions from a solar photovoltaic (PV system in Florida 15 min, 1 h and 24 h ahead of time. A hierarchical approach is proposed based on the machine learning algorithms tested. The production data used in this work corresponds to 15 min averaged power measurements collected from 2014. The accuracy of the model is determined using computing error statistics such as mean bias error (MBE, mean absolute error (MAE, root mean square error (RMSE, relative MBE (rMBE, mean percentage error (MPE and relative RMSE (rRMSE. This work provides findings on how forecasts from individual inverters will improve the total solar power generation forecast of the PV system.

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

    Directory of Open Access Journals (Sweden)

    Manoja Kumar Behera

    2018-06-01

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

  16. Long Term Solar Radiation Forecast Using Computational Intelligence Methods

    Directory of Open Access Journals (Sweden)

    João Paulo Coelho

    2014-01-01

    Full Text Available The point prediction quality is closely related to the model that explains the dynamic of the observed process. Sometimes the model can be obtained by simple algebraic equations but, in the majority of the physical systems, the relevant reality is too hard to model with simple ordinary differential or difference equations. This is the case of systems with nonlinear or nonstationary behaviour which require more complex models. The discrete time-series problem, obtained by sampling the solar radiation, can be framed in this type of situation. By observing the collected data it is possible to distinguish multiple regimes. Additionally, due to atmospheric disturbances such as clouds, the temporal structure between samples is complex and is best described by nonlinear models. This paper reports the solar radiation prediction by using hybrid model that combines support vector regression paradigm and Markov chains. The hybrid model performance is compared with the one obtained by using other methods like autoregressive (AR filters, Markov AR models, and artificial neural networks. The results obtained suggests an increasing prediction performance of the hybrid model regarding both the prediction error and dynamic behaviour.

  17. Forecasting solar proton event with artificial neural network

    Science.gov (United States)

    Gong, J.; Wang, J.; Xue, B.; Liu, S.; Zou, Z.

    Solar proton event (SPE), relatively rare but popular in solar maximum, can bring hazard situation to spacecraft. As a special event, SPE always accompanies flare, which is also called proton flare. To produce such an eruptive event, large amount energy must be accumulated within the active region. So we can investigate the character of the active region and its evolving trend, together with other such as cm radio emission and soft X-ray background to evaluate the potential of SEP in chosen area. In order to summarize the omen of SPEs in the active regions behind the observed parameters, we employed AI technology. Full connecting neural network was chosen to fulfil this job. After constructing the network, we train it with 13 parameters that was able to exhibit the character of active regions and their evolution trend. More than 80 sets of event parameter were defined to teach the neural network to identify whether an active region was potential of SPE. Then we test this model with a data base consisting SPE and non-SPE cases that was not used to train the neural network. The result showed that 75% of the choice by the model was right.

  18. The use of satellite data assimilation methods in regional NWP for solar irradiance forecasting

    Science.gov (United States)

    Kurzrock, Frederik; Cros, Sylvain; Chane-Ming, Fabrice; Potthast, Roland; Linguet, Laurent; Sébastien, Nicolas

    2016-04-01

    As an intermittent energy source, the injection of solar power into electricity grids requires irradiance forecasting in order to ensure grid stability. On time scales of more than six hours ahead, numerical weather prediction (NWP) is recognized as the most appropriate solution. However, the current representation of clouds in NWP models is not sufficiently precise for an accurate forecast of solar irradiance at ground level. Dynamical downscaling does not necessarily increase the quality of irradiance forecasts. Furthermore, incorrectly simulated cloud evolution is often the cause of inaccurate atmospheric analyses. In non-interconnected tropical areas, the large amplitudes of solar irradiance variability provide abundant solar yield but present significant problems for grid safety. Irradiance forecasting is particularly important for solar power stakeholders in these regions where PV electricity penetration is increasing. At the same time, NWP is markedly more challenging in tropic areas than in mid-latitudes due to the special characteristics of tropical homogeneous convective air masses. Numerous data assimilation methods and strategies have evolved and been applied to a large variety of global and regional NWP models in the recent decades. Assimilating data from geostationary meteorological satellites is an appropriate approach. Indeed, models converting radiances measured by satellites into cloud properties already exist. Moreover, data are available at high temporal frequencies, which enable a pertinent cloud cover evolution modelling for solar energy forecasts. In this work, we present a survey of different approaches which aim at improving cloud cover forecasts using the assimilation of geostationary meteorological satellite data into regional NWP models. Various approaches have been applied to a variety of models and satellites and in different regions of the world. Current methods focus on the assimilation of cloud-top information, derived from infrared

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

    KAUST Repository

    Dehwah, Ahmad H.

    2017-04-11

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

  20. Satellite image analysis and a hybrid ESSS/ANN model to forecast solar irradiance in the tropics

    International Nuclear Information System (INIS)

    Dong, Zibo; Yang, Dazhi; Reindl, Thomas; Walsh, Wilfred M.

    2014-01-01

    Highlights: • Satellite image analysis is performed and cloud cover index is classified using self-organizing maps (SOM). • The ESSS model is used to forecast cloud cover index. • Solar irradiance is estimated using multi-layer perceptron (MLP). • The proposed model shows better accuracy than other investigated models. - Abstract: We forecast hourly solar irradiance time series using satellite image analysis and a hybrid exponential smoothing state space (ESSS) model together with artificial neural networks (ANN). Since cloud cover is the major factor affecting solar irradiance, cloud detection and classification are crucial to forecast solar irradiance. Geostationary satellite images provide cloud information, allowing a cloud cover index to be derived and analysed using self-organizing maps (SOM). Owing to the stochastic nature of cloud generation in tropical regions, the ESSS model is used to forecast cloud cover index. Among different models applied in ANN, we favour the multi-layer perceptron (MLP) to derive solar irradiance based on the cloud cover index. This hybrid model has been used to forecast hourly solar irradiance in Singapore and the technique is found to outperform traditional forecasting models

  1. Potential for Development of Solar and Wind Resource in Bhutan

    Energy Technology Data Exchange (ETDEWEB)

    Gilman, P.; Cowlin, S.; Heimiller, D.

    2009-09-01

    With support from the U.S. Agency for International Development (USAID), the U.S. Department of Energy's National Renewable Energy Laboratory (NREL) produced maps and data of the wind and solar resources in Bhutan. The solar resource data show that Bhutan has an adequate resource for flat-plate collectors, with annual average values of global horizontal solar radiation ranging from 4.0 to 5.5 kWh/m2-day (4.0 to 5.5 peak sun hours per day). The information provided in this report may be of use to energy planners in Bhutan involved in developing energy policy or planning wind and solar projects, and to energy analysts around the world interested in gaining an understanding of Bhutan's wind and solar energy potential.

  2. LHCb Computing Resources: 2011 re-assessment, 2012 request and 2013 forecast

    CERN Document Server

    Graciani, R

    2011-01-01

    This note covers the following aspects: re-assessment of computing resource usage estimates for 2011 data taking period, request of computing resource needs for 2012 data taking period and a first forecast of the 2013 needs, when no data taking is foreseen. Estimates are based on 2010 experienced and last updates from LHC schedule, as well as on a new implementation of the computing model simulation tool. Differences in the model and deviations in the estimates from previous presented results are stressed.

  3. LHCb Computing Resources: 2012 re-assessment, 2013 request and 2014 forecast

    CERN Document Server

    Graciani Diaz, Ricardo

    2012-01-01

    This note covers the following aspects: re-assessment of computing resource usage estimates for 2012 data-taking period, request of computing resource needs for 2013, and a first forecast of the 2014 needs, when restart of data-taking is foreseen. Estimates are based on 2011 experience, as well as on the results of a simulation of the computing model described in the document. Differences in the model and deviations in the estimates from previous presented results are stressed.

  4. A Green Energy Application in Energy Management Systems by an Artificial Intelligence-Based Solar Radiation Forecasting Model

    Directory of Open Access Journals (Sweden)

    Ping-Huan Kuo

    2018-04-01

    Full Text Available The photovoltaic (PV systems generate green energy from the sunlight without any pollution or noise. The PV systems are simple, convenient to install, and seldom malfunction. Unfortunately, the energy generated by PV systems depends on climatic conditions, location, and system design. The solar radiation forecasting is important to the smooth operation of PV systems. However, solar radiation detected by a pyranometer sensor is strongly nonlinear and highly unstable. The PV energy generation makes a considerable contribution to the smart grids via a large number of relatively small PV systems. In this paper, a high-precision deep convolutional neural network model (SolarNet is proposed to facilitate the solar radiation forecasting. The proposed model is verified by experiments. The experimental results demonstrate that SolarNet outperforms other benchmark models in forecasting accuracy as well as in predicting complex time series with a high degree of volatility and irregularity.

  5. [Medical human resources planning in Europe: A literature review of the forecasting models].

    Science.gov (United States)

    Benahmed, N; Deliège, D; De Wever, A; Pirson, M

    2018-02-01

    Healthcare is a labor-intensive sector in which half of the expenses are dedicated to human resources. Therefore, policy makers, at national and internal levels, attend to the number of practicing professionals and the skill mix. This paper aims to analyze the European forecasting model for supply and demand of physicians. To describe the forecasting tools used for physician planning in Europe, a grey literature search was done in the OECD, WHO, and European Union libraries. Electronic databases such as Pubmed, Medine, Embase and Econlit were also searched. Quantitative methods for forecasting medical supply rely mainly on stock-and-flow simulations and less often on systemic dynamics. Parameters included in forecasting models exhibit wide variability for data availability and quality. The forecasting of physician needs is limited to healthcare consumption and rarely considers overall needs and service targets. Besides quantitative methods, horizon scanning enables an evaluation of the changes in supply and demand in an uncertain future based on qualitative techniques such as semi-structured interviews, Delphi Panels, or focus groups. Finally, supply and demand forecasting models should be regularly updated. Moreover, post-hoc analyze is also needed but too rarely implemented. Medical human resource planning in Europe is inconsistent. Political implementation of the results of forecasting projections is essential to insure efficient planning. However, crucial elements such as mobility data between Member States are poorly understood, impairing medical supply regulation policies. These policies are commonly limited to training regulations, while horizontal and vertical substitution is less frequently taken into consideration. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

  6. Conditional Monthly Weather Resampling Procedure for Operational Seasonal Water Resources Forecasting

    Science.gov (United States)

    Beckers, J.; Weerts, A.; Tijdeman, E.; Welles, E.; McManamon, A.

    2013-12-01

    To provide reliable and accurate seasonal streamflow forecasts for water resources management several operational hydrologic agencies and hydropower companies around the world use the Extended Streamflow Prediction (ESP) procedure. The ESP in its original implementation does not accommodate for any additional information that the forecaster may have about expected deviations from climatology in the near future. Several attempts have been conducted to improve the skill of the ESP forecast, especially for areas which are affected by teleconnetions (e,g. ENSO, PDO) via selection (Hamlet and Lettenmaier, 1999) or weighting schemes (Werner et al., 2004; Wood and Lettenmaier, 2006; Najafi et al., 2012). A disadvantage of such schemes is that they lead to a reduction of the signal to noise ratio of the probabilistic forecast. To overcome this, we propose a resampling method conditional on climate indices to generate meteorological time series to be used in the ESP. The method can be used to generate a large number of meteorological ensemble members in order to improve the statistical properties of the ensemble. The effectiveness of the method was demonstrated in a real-time operational hydrologic seasonal forecasts system for the Columbia River basin operated by the Bonneville Power Administration. The forecast skill of the k-nn resampler was tested against the original ESP for three basins at the long-range seasonal time scale. The BSS and CRPSS were used to compare the results to those of the original ESP method. Positive forecast skill scores were found for the resampler method conditioned on different indices for the prediction of spring peak flows in the Dworshak and Hungry Horse basin. For the Libby Dam basin however, no improvement of skill was found. The proposed resampling method is a promising practical approach that can add skill to ESP forecasts at the seasonal time scale. Further improvement is possible by fine tuning the method and selecting the most

  7. Treatment of Solar Generation in Electric Utility Resource Planning

    Energy Technology Data Exchange (ETDEWEB)

    Sterling, J.; McLaren, J.; Taylor, M.; Cory, K.

    2013-10-01

    Today's utility planners have a different market and economic context than their predecessors, including planning for the growth of renewable energy. State and federal support policies, solar photovoltaic (PV) price declines, and the introduction of new business models for solar PV 'ownership' are leading to increasing interest in solar technologies (especially PV); however, solar introduces myriad new variables into the utility resource planning decision. Most, but not all, utility planners have less experience analyzing solar than conventional generation as part of capacity planning, portfolio evaluation, and resource procurement decisions. To begin to build this knowledge, utility staff expressed interest in one effort: utility exchanges regarding data, methods, challenges, and solutions for incorporating solar in the planning process. Through interviews and a questionnaire, this report aims to begin this exchange of information and capture utility-provided information about: 1) how various utilities approach long-range resource planning; 2) methods and tools utilities use to conduct resource planning; and, 3) how solar technologies are considered in the resource planning process.

  8. Forecasting Solar Flares Using Magnetogram-based Predictors and Machine Learning

    Science.gov (United States)

    Florios, Kostas; Kontogiannis, Ioannis; Park, Sung-Hong; Guerra, Jordan A.; Benvenuto, Federico; Bloomfield, D. Shaun; Georgoulis, Manolis K.

    2018-02-01

    We propose a forecasting approach for solar flares based on data from Solar Cycle 24, taken by the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO) mission. In particular, we use the Space-weather HMI Active Region Patches (SHARP) product that facilitates cut-out magnetograms of solar active regions (AR) in the Sun in near-realtime (NRT), taken over a five-year interval (2012 - 2016). Our approach utilizes a set of thirteen predictors, which are not included in the SHARP metadata, extracted from line-of-sight and vector photospheric magnetograms. We exploit several machine learning (ML) and conventional statistics techniques to predict flares of peak magnitude {>} M1 and {>} C1 within a 24 h forecast window. The ML methods used are multi-layer perceptrons (MLP), support vector machines (SVM), and random forests (RF). We conclude that random forests could be the prediction technique of choice for our sample, with the second-best method being multi-layer perceptrons, subject to an entropy objective function. A Monte Carlo simulation showed that the best-performing method gives accuracy ACC=0.93(0.00), true skill statistic TSS=0.74(0.02), and Heidke skill score HSS=0.49(0.01) for {>} M1 flare prediction with probability threshold 15% and ACC=0.84(0.00), TSS=0.60(0.01), and HSS=0.59(0.01) for {>} C1 flare prediction with probability threshold 35%.

  9. THE ECONOMETRICS OF THE FORECASTING OF FINANCIAL RESOURCES, A MAIN COMPONENT OF THE FINANCIAL MANAGEMENT

    Directory of Open Access Journals (Sweden)

    2009-05-01

    Full Text Available The paper intends to emphasise the importance of budget resources forecasting for long periods of time, within thefinancial management. An as accurate as possible forecasting of the volume of financial resources will represent the basis forthe future projections of the expenditure of local communities, as they are regulated by law, knowing that one of the principlesrepresenting the basis of budget making is that of the balanced budget. To the same extent, the volume of the budget liquiditieswill depend on the rigorousness of the design of the volume of financial resources.. Beyond the abstract character of themathematic calculus made by specialists in econometrics, the financial manager is also interested to know the forecastingtechniques so that he/she can draw up the income and expenditure budget, the basis for the implementation of the economicsocialdevelopment strategies of the local communities. The financial management remains a fundamental component of thepublic management through the theoretical-methodological arsenal made available for the loan officer.

  10. Remote Sensing of Clouds for Solar Forecasting Applications

    Science.gov (United States)

    Mejia, Felipe

    A method for retrieving cloud optical depth (tauc) using a UCSD developed ground- based Sky Imager (USI) is presented. The Radiance Red-Blue Ratio (RRBR) method is motivated from the analysis of simulated images of various tauc produced by a Radiative Transfer Model (RTM). From these images the basic parameters affecting the radiance and RBR of a pixel are identified as the solar zenith angle (SZA), tau c , solar pixel an- gle/scattering angle (SPA), and pixel zenith angle/view angle (PZA). The effects of these parameters are described and the functions for radiance, Ilambda (tau c ,SZA,SPA,PZA) , and the red-blue ratio, RBR(tauc ,SZA,SPA,PZA) , are retrieved from the RTM results. RBR, which is commonly used for cloud detection in sky images, provides non-unique solutions for tau c , where RBR increases with tauc up to about tauc = 1 (depending on other parameters) and then decreases. Therefore, the RRBR algorithm uses the measured Imeaslambda (SPA,PZA) , in addition to RBRmeas (SPA,PZA ) to obtain a unique solution for tauc . The RRBR method is applied to images of liquid water clouds taken by a USI at the Oklahoma Atmospheric Radiation Measurement program (ARM) site over the course of 220 days and compared against measurements from a microwave radiometer (MWR) and output from the Min [ MH96a ] method for overcast skies. tau c values ranged from 0-80 with values over 80 being capped and registered as 80. A tauc RMSE of 2.5 between the Min method [ MH96b ] and the USI are observed. The MWR and USI have an RMSE of 2.2 which is well within the uncertainty of the MWR. The procedure developed here provides a foundation to test and develop other cloud detection algorithms. Using the RRBR tauc estimate as an input we then explore the potential of using tomographic techniques for 3-D cloud reconstruction. The Algebraic Reconstruction Technique (ART) is applied to optical depth maps from sky images to reconstruct 3-D cloud extinction coefficients. Reconstruction accuracy

  11. Model predictive control for a smart solar tank based on weather and consumption forecasts

    DEFF Research Database (Denmark)

    Halvgaard, Rasmus; Bacher, Peder; Perers, Bengt

    2012-01-01

    In this work the heat dynamics of a storage tank were modelled on the basis of data and maximum likelihood methods. The resulting grey-box model was used for Economic Model Predictive Control (MPC) of the energy in the tank. The control objective was to balance the energy from a solar collector...... and the heat consumption in a residential house. The storage tank provides heat in periods where there is low solar radiation and stores heat when there is surplus solar heat. The forecasts of consumption patterns were based on data obtained from meters in a group of single-family houses in Denmark. The tank...... can also be heated by electric heating elements if necessary, but the electricity costs of operating these heating elements should be minimized. Consequently, the heating elements should be used in periods with cheap electricity. It is proposed to integrate a price-sensitive control to enable...

  12. Solar Particle Radiation Storms Forecasting and Analysis within the Framework of the `HESPERIA' HORIZON 2020 Project

    Science.gov (United States)

    Posner, A.; Malandraki, O.; Nunez, M.; Heber, B.; Labrenz, J.; Kühl, P.; Milas, N.; Tsiropoula, G.; Pavlos, E.

    2017-12-01

    Two prediction tools that have been developed in the framework of HESPERIA based upon the proven concepts UMASEP and REleASE. Near-relativistic (NR) electrons traveling faster than ions (30 MeV protons have 0.25c) are used to forecast the arrival of protons of Solar Energetic Particle (SEP) events with real-time measurements of NR electrons. The faster electrons arrive at L1 30 to 90 minutes before the slower protons. REleASE (Relativistic Electron Alert System for Exploration, Posner, 2007) uses this effect to predict the proton flux by utilizing actual electron fluxes and their most recent increases. Through HESPERIA, a clone of REleASE was built in open source programming language. The same forecasting principle was adapted to real-time data from ACE/EPAM. It is shown that HESPERIA REleASE forecasting works with any NR electron flux measurements. >500 MeV solar protons are so energetic that they usually have effects on the ground, producing Ground Level Enhancement (GLE) events. Within HESPERIA, a predictor of >500 SEP proton events near earth (geostationary orbit) has been developed. In order to predict these events, UMASEP (Núñez, 2011, 2015) has been used. UMASEP makes a lag-correlation of solar electromagnetic (EM) flux with the particle flux near earth. If the correlation is high, the model infers that there is a magnetic connection through which particles are arriving. If, additionally, the intensity of the flux of the associated solar event is also high, then UMASEP issues a SEP prediction. In the case of the prediction of >500 MeV SEP events, the implemented system, called HESPERIA UMASEP-500, correlates X-ray flux with differential proton fluxes by GOES, and with fluxes collected by neutron monitor stations around the world. When the correlation estimation and flare surpasses thresholds, a >500 MeV SEP forecast is issued. These findings suggest that a synthesis of the various approaches may improve over the status quo. Both forecasting tools are

  13. How Solar Resource Data supports Research and Development

    OpenAIRE

    Kern, Jürgen

    2013-01-01

    The presentation describes the methods of renewable resource data, how the research and development will benefits from Renewable Resource Atlas and how institutions will leverage the solar monitoring station data to support renewable energy project deployment in other locations throughout the Kingdom.

  14. A Two-Dimensional Gridded Solar Forecasting System using Situation-Dependent Blending of Multiple Weather Models

    Science.gov (United States)

    Lu, S.; Hwang, Y.; Shao, X.; Hamann, H.

    2015-12-01

    Previously, we reported the application of a "weather situation" dependent multi-model blending approach to improve the forecast accuracy of solar irradiance and other atmospheric parameters. The approach uses machine-learning techniques to classify "weather situations" by a set of atmospheric parameters. The "weather situation" classification is location-dependent and each "weather situation" has characteristic forecast errors from a set of individual input numerical weather prediction (NWP) models. The input models are thus corrected or combined differently for different "weather situations" to minimize the overall forecast error. While the original implementation of the model-blending is applicable to only point-like locations having historical data of both measurements and forecasts, here we extend the approach to provide two-dimensional (2D) gridded forecasts. An experimental 2D forecasting system has been set up to provide gridded forecasts of solar irradiance (global horizontal irradiance), temperature, wind speed, and humidity for the contiguous United States (CONUS). Validation results show around 30% enhancement of 0 to 48 hour ahead solar irradiance forecast accuracy compared to the best input NWP model. The forecasting system may be leveraged by other site- or region-specific solar energy forecast products. To enable the 2D forecasting system, historical solar irradiance measurements from around 1,600 selected sites of the remote automated weather stations (RAWS) network have been employed. The CONUS was divided into smaller sub-regions, each containing a group of 10 to 20 RAWS sites. A group of sites, as classified by statistical analysis, have similar "weather patterns", i.e. the NWPs have similar "weather situation" dependent forecast errors for all sites in a group. The model-blending trained by the historical data from a group of sites is then applied for all locations in the corresponding sub-region. We discuss some key techniques developed for

  15. Forecasting the Impact of an 1859-calibre Superstorm on Satellite Resources

    Science.gov (United States)

    Odenwald, Sten; Green, James; Taylor, William

    2005-01-01

    We have assembled a database of operational satellites in orbit as of 2004, and have developed a series of simple models to assess the economic impacts to this resource caused by various scenarios of superstorm events possible during the next sunspot cycle between 2010 and 2014. Despite the apparent robustness of our satellite assets against the kinds of storms we have encountered during the satellite era, our models suggest a potential economic loss exceeding $10(exp 11) for satellite replacement and lost profitability caused by a once a century single storm similar to the 1859 superstorm. From a combination of power system and attitude control system (the most vulnerable) failures, we estimate that 80 satellites (LEO, MEO, GEO) may be disabled as a consequence of a superstorm event. Additional consequences may include the failure of many of the GPS, GLONASS and Galileo satellite systems in MEO. Approximately 98 LEO satellites that normally would not have re-entered for many decades, may prematurely de-orbit in ca 2021 as a result of the temporarily increased atmospheric drag caused by the superstorm event occurring in 2012. The $10(exp 11) International Space Station may lose at least 15 kilometers of altitude, placing it in critical need for re-boosting by an amount that is potentially outside the range of typical Space Shuttle operations during the previous solar maximum in ca 2000, and at a time when NASA plans to decommission the Space Shuttle. Several LEO satellites will unexpectedly be placed on orbits that enter the ISS zone of avoidance, requiring some action by ground personnel and ISS astronauts to avoid close encounters. Radiation effects on astronauts have also been considered and could include a range of possibilities from acute radiation sickness for astronauts inside spacecraft, to near-lethal doses during EVAs. The specifics depends very sensitively on the spectral hardness of the accompanying SPE event. Currently, the ability to forecast extreme

  16. NREL Solar Radiation Resource Assessment Project: Status and outlook

    Science.gov (United States)

    Renne, D.; Riordan, C.; Maxwell, E.; Stoffel, T.; Marion, B.; Rymes, M.; Wilcox, S.; Myers, D.

    1992-05-01

    This report summarizes the activities and accomplishments of NREL's Solar Radiation Resource Assessment Project during fiscal year 1991. Currently, the primary focus of the SRRAP is to produce a 1961 - 1990 National Solar Radiation Data Base, providing hourly values of global horizontal, diffuse, and direct normal solar radiation at approximately 250 sites around the United States. Because these solar radiation quantities were measured intermittently at only about 50 of these sites, models were developed and applied to the majority of the stations to provide estimates of these parameters. Although approximately 93 percent of the data base consists of modeled data this represents a significant improvement over the SOLMET/ERSATZ 1952 - 1975 data base. The magnitude and importance of this activity are such that the majority of SRRAP human and financial resources were devoted to the data base development. However, in FY 1991 the SRRAP was involved in many other activities, which are reported here. These include the continued maintenance of a solar radiation monitoring network in the southeast United States at six Historically Black Colleges and Universities (HBCU's), the transfer of solar radiation resource assessment technology through a variety of activities, participation in international programs, and the maintenance and operation of NREL's Solar Radiation Research Laboratory.

  17. Solar Tutorial and Annotation Resource (STAR)

    Science.gov (United States)

    Showalter, C.; Rex, R.; Hurlburt, N. E.; Zita, E. J.

    2009-12-01

    We have written a software suite designed to facilitate solar data analysis by scientists, students, and the public, anticipating enormous datasets from future instruments. Our “STAR" suite includes an interactive learning section explaining 15 classes of solar events. Users learn software tools that exploit humans’ superior ability (over computers) to identify many events. Annotation tools include time slice generation to quantify loop oscillations, the interpolation of event shapes using natural cubic splines (for loops, sigmoids, and filaments) and closed cubic splines (for coronal holes). Learning these tools in an environment where examples are provided prepares new users to comfortably utilize annotation software with new data. Upon completion of our tutorial, users are presented with media of various solar events and asked to identify and annotate the images, to test their mastery of the system. Goals of the project include public input into the data analysis of very large datasets from future solar satellites, and increased public interest and knowledge about the Sun. In 2010, the Solar Dynamics Observatory (SDO) will be launched into orbit. SDO’s advancements in solar telescope technology will generate a terabyte per day of high-quality data, requiring innovation in data management. While major projects develop automated feature recognition software, so that computers can complete much of the initial event tagging and analysis, still, that software cannot annotate features such as sigmoids, coronal magnetic loops, coronal dimming, etc., due to large amounts of data concentrated in relatively small areas. Previously, solar physicists manually annotated these features, but with the imminent influx of data it is unrealistic to expect specialized researchers to examine every image that computers cannot fully process. A new approach is needed to efficiently process these data. Providing analysis tools and data access to students and the public have proven

  18. Forecasting solar radiation using an optimized hybrid model by Cuckoo Search algorithm

    International Nuclear Information System (INIS)

    Wang, Jianzhou; Jiang, He; Wu, Yujie; Dong, Yao

    2015-01-01

    Due to energy crisis and environmental problems, it is very urgent to find alternative energy sources nowadays. Solar energy, as one of the great potential clean energies, has widely attracted the attention of researchers. In this paper, an optimized hybrid method by CS (Cuckoo Search) on the basis of the OP-ELM (Optimally Pruned Extreme Learning Machine), called CS-OP-ELM, is developed to forecast clear sky and real sky global horizontal radiation. First, MRSR (Multiresponse Sparse Regression) and LOO-CV (leave-one-out cross-validation) can be applied to rank neurons and prune the possibly meaningless neurons of the FFNN (Feed Forward Neural Network), respectively. Then, Direct strategy and Direct-Recursive strategy based on OP-ELM are introduced to build a hybrid model. Furthermore, CS (Cuckoo Search) optimized algorithm is employed to determine the proper weight coefficients. In order to verify the effectiveness of the developed method, hourly solar radiation data from six sites of the United States has been collected, and methods like ARMA (Autoregression moving average), BP (Back Propagation) neural network and OP-ELM can be compared with CS-OP-ELM. Experimental results show the optimized hybrid method CS-OP-ELM has the best forecasting performance. - Highlights: • An optimized hybrid method called CS-OP-ELM is proposed to forecast solar radiation. • CS-OP-ELM adopts multiple variables dataset as input variables. • Direct and Direct-Recursive strategy are introduced to build a hybrid model. • CS (Cuckoo Search) algorithm is used to determine the optimal weight coefficients. • The proposed method has the best performance compared with other methods

  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. Solar combisystems with forecast control to increase the solar fraction and lower the auxiliary energy cost

    DEFF Research Database (Denmark)

    Perers, Bengt; Furbo, Simon; Fan, Jianhua

    2011-01-01

    Solar Combi systems still need quite a lot of auxiliary energy especially in small systems without seasonal storage possibilities. The control of the auxiliary energy input both in time and power is important to utilize as much as possible of the solar energy available from the collectors and also...... energy sources. It can be either direct electric heating elements or a heat pump upgrading ambient energy in the air, ground, solar collector or waste heat from the house. The paper describes system modeling and simulation results. Advanced laboratory experiments are also starting now with three...

  2. Analysis of the balancing of the wind and solar energy resources in Andalusia (Southern Spain)

    Science.gov (United States)

    Santos-Alamillos, F. J.; Pozo-Vazquez, D.; Lara-Fanego, V.; Ruiz-Arias, J. A.; Hernandez-Alvaro, J.; Tova-Pescador, J.

    2010-09-01

    A higher penetration of the renewable energy in the electric system in the future will be conditioned to a reduction of the uncertainty of the yield. A way to obtain this goal is to analyze the balancing between the productions of different sources of renewable energy, trying to combine these productions. In this work we analyze, from a meteorological point of view, the balancing between wind and solar energy resources in Andalusia (southern Iberian Peninsula). To this end, wind speed and global radiation data corresponding to an one year integration of the Weather Research and Forecasting (WRF) Numerical Weather Prediction (NWP) model were analyzed. Two method of analysis were used: a point correlation analysis and a Canonical Correlation Analysis (CCA). Results from these analyses allow obtaining, eventually, areas of local and distributed balancing between the wind and solar energy resources. The analysis was carried out separately for the different seasons of the year. Results showed, overall, a considerable balancing effect between the wind and solar resources in the mountain areas of the interior of the region, along the coast of the central part of the region and, specially, in the coastal area near the Gibraltar strait. Nevertheless, considerable differences were found between the seasons of the year, which may lead to compensating effects. Autumn proved to be the season with the most significant results.

  3. A dynamic system to forecast ionospheric storm disturbances based on solar wind conditions

    Directory of Open Access Journals (Sweden)

    L. R. Cander

    2005-06-01

    Full Text Available For the reliable performance of technologically advanced radio communications systems under geomagnetically disturbed conditions, the forecast and modelling of the ionospheric response during storms is a high priority. The ionospheric storm forecasting models that are currently in operation have shown a high degree of reliability during quiet conditions, but they have proved inadequate during storm events. To improve their prediction accuracy, we have to take advantage of the deeper understanding in ionospheric storm dynamics that is currently available, indicating a correlation between the Interplanetary Magnetic Field (IMF disturbances and the qualitative signature of ionospheric storm disturbances at middle latitude stations. In this paper we analyse observations of the foF2 critical frequency parameter from one mid-latitude European ionospheric station (Chilton in conjunction with observations of IMF parameters (total magnitude, Bt and Bz-IMF component from the ACE spacecraft mission for eight storm events. The determination of the time delay in the ionospheric response to the interplanetary medium disturbances leads to significant results concerning the forecast of the ionospheric storms onset and their development during the first 24 h. In this way the real-time ACE observations of the solar wind parameters may be used in the development of a real-time dynamic ionospheric storm model with adequate accuracy.

  4. Forecast of solar proton flux profiles for well-connected events

    Science.gov (United States)

    Ji, Eun-Young; Moon, Yong-Jae; Park, Jinhye

    2014-12-01

    We have developed a forecast model of solar proton flux profiles (> 10 MeV channel) for well-connected events. Among 136 solar proton events (SPEs) from 1986 to 2006, we select 49 well-connected ones that are all associated with single X-ray flares stronger than M1 class and start to increase within 4 h after their X-ray peak times. These events show rapid increments in proton flux. By comparing several empirical functions, we select a modified Weibull curve function to approximate a SPE flux profile. The parameters (peak flux, rise time, and decay time) of this function are determined by the relationship between X-ray flare parameters (peak flux, impulsive time, and emission measure) and SPE parameters. For 49 well-connected SPEs, the linear correlation coefficient between the predicted and the observed proton peak fluxes is 0.65 with the RMS error of 0.55 log10(pfu). In addition, we determine another forecast model based on flare and coronal mass ejection (CME) parameters using 22 SPEs. The used CME parameters are linear speed and angular width. As a result, we find that the linear correlation coefficient between the predicted and the observed proton peak fluxes is 0.83 with the RMS error of 0.35 log10(pfu). From the relationship between error of model and CME acceleration, we find that CME acceleration is an important factor for predicting proton flux profiles.

  5. Short-Term Solar Irradiance Forecasting Model Based on Artificial Neural Network Using Statistical Feature Parameters

    Directory of Open Access Journals (Sweden)

    Hongshan Zhao

    2012-05-01

    Full Text Available Short-term solar irradiance forecasting (STSIF is of great significance for the optimal operation and power predication of grid-connected photovoltaic (PV plants. However, STSIF is very complex to handle due to the random and nonlinear characteristics of solar irradiance under changeable weather conditions. Artificial Neural Network (ANN is suitable for STSIF modeling and many research works on this topic are presented, but the conciseness and robustness of the existing models still need to be improved. After discussing the relation between weather variations and irradiance, the characteristics of the statistical feature parameters of irradiance under different weather conditions are figured out. A novel ANN model using statistical feature parameters (ANN-SFP for STSIF is proposed in this paper. The input vector is reconstructed with several statistical feature parameters of irradiance and ambient temperature. Thus sufficient information can be effectively extracted from relatively few inputs and the model complexity is reduced. The model structure is determined by cross-validation (CV, and the Levenberg-Marquardt algorithm (LMA is used for the network training. Simulations are carried out to validate and compare the proposed model with the conventional ANN model using historical data series (ANN-HDS, and the results indicated that the forecast accuracy is obviously improved under variable weather conditions.

  6. Estimating the Value of Improved Distributed Photovoltaic Adoption Forecasts for Utility Resource Planning

    Energy Technology Data Exchange (ETDEWEB)

    Gagnon, Pieter [National Renewable Energy Lab. (NREL), Golden, CO (United States); Barbose, Galen L. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Stoll, Brady [National Renewable Energy Lab. (NREL), Golden, CO (United States); Ehlen, Ali [National Renewable Energy Lab. (NREL), Golden, CO (United States); Zuboy, Jarret [National Renewable Energy Lab. (NREL), Golden, CO (United States); Mai, Trieu [National Renewable Energy Lab. (NREL), Golden, CO (United States); Mills, Andrew D. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

    2018-05-15

    Misforecasting the adoption of customer-owned distributed photovoltaics (DPV) can have operational and financial implications for utilities; forecasting capabilities can be improved, but generally at a cost. This paper informs this decision-space by using a suite of models to explore the capacity expansion and operation of the Western Interconnection over a 15-year period across a wide range of DPV growth rates and misforecast severities. The system costs under a misforecast are compared against the costs under a perfect forecast, to quantify the costs of misforecasting. Using a simplified probabilistic method applied to these modeling results, an analyst can make a first-order estimate of the financial benefit of improving a utility’s forecasting capabilities, and thus be better informed about whether to make such an investment. For example, under our base assumptions, a utility with 10 TWh per year of retail electric sales who initially estimates that DPV growth could range from 2% to 7.5% of total generation over the next 15 years could expect total present-value savings of approximately $4 million if they could reduce the severity of misforecasting to within ±25%. Utility resource planners can compare those savings against the costs needed to achieve that level of precision, to guide their decision on whether to make an investment in tools or resources.

  7. An operational integrated short-term warning solution for solar radiation storms: introducing the Forecasting Solar Particle Events and Flares (FORSPEF) system

    Science.gov (United States)

    Anastasiadis, Anastasios; Sandberg, Ingmar; Papaioannou, Athanasios; Georgoulis, Manolis; Tziotziou, Kostas; Jiggens, Piers; Hilgers, Alain

    2015-04-01

    We present a novel integrated prediction system, of both solar flares and solar energetic particle (SEP) events, which is in place to provide short-term warnings for hazardous solar radiation storms. FORSPEF system provides forecasting of solar eruptive events, such as solar flares with a projection to coronal mass ejections (CMEs) (occurrence and velocity) and the likelihood of occurrence of a SEP event. It also provides nowcasting of SEP events based on actual solar flare and CME near real-time alerts, as well as SEP characteristics (peak flux, fluence, rise time, duration) per parent solar event. The prediction of solar flares relies on a morphological method which is based on the sophisticated derivation of the effective connected magnetic field strength (Beff) of potentially flaring active-region (AR) magnetic configurations and it utilizes analysis of a large number of AR magnetograms. For the prediction of SEP events a new reductive statistical method has been implemented based on a newly constructed database of solar flares, CMEs and SEP events that covers a large time span from 1984-2013. The method is based on flare location (longitude), flare size (maximum soft X-ray intensity), and the occurrence (or not) of a CME. Warnings are issued for all > C1.0 soft X-ray flares. The warning time in the forecasting scheme extends to 24 hours with a refresh rate of 3 hours while the respective warning time for the nowcasting scheme depends on the availability of the near real-time data and falls between 15-20 minutes. We discuss the modules of the FORSPEF system, their interconnection and the operational set up. The dual approach in the development of FORPSEF (i.e. forecasting and nowcasting scheme) permits the refinement of predictions upon the availability of new data that characterize changes on the Sun and the interplanetary space, while the combined usage of solar flare and SEP forecasting methods upgrades FORSPEF to an integrated forecasting solution. This

  8. Evaluation of Sources of Uncertainties in Solar Resource Measurement

    Energy Technology Data Exchange (ETDEWEB)

    Habte, Aron M [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Sengupta, Manajit [National Renewable Energy Laboratory (NREL), Golden, CO (United States)

    2017-09-25

    This poster presents a high-level overview of sources of uncertainties in solar resource measurement, demonstrating the impact of various sources of uncertainties -- such as cosine response, thermal offset, spectral response, and others -- on the accuracy of data from several radiometers. The study provides insight on how to reduce the impact of some of the sources of uncertainties.

  9. Wind and Solar Energy Resource Assessment for Navy Installations in the Midwestern US

    Science.gov (United States)

    Darmenova, K.; Apling, D.; Higgins, G. J.; Carnes, J.; Smith, C.

    2012-12-01

    A stable supply of energy is critical for sustainable economic development and the ever-increasing demand for energy resources drives the need for alternative weather-driven renewable energy solutions such as solar and wind-generated power. Recognizing the importance of energy as a strategic resource, the Department of the Navy has focused on energy efficient solutions aiming to increase tactical and shore energy security and reduce greenhouse gas emissions. Implementing alternative energy solutions will alleviate the Navy installations demands on the National power grid, however transitioning to renewable energy sources is a complex multi-stage process that involves initial investment in resource assessment and feasibility of building solar and wind power systems in Navy's facilities. This study focuses on the wind and solar energy resource assessment for Navy installations in the Midwestern US. We use the dynamically downscaled datasets at 12 km resolution over the Continental US generated with the Weather Research and Forecasting (WRF) model to derive the wind climatology in terms of wind speed, direction, and wind power at 20 m above the surface for 65 Navy facilities. In addition, we derived the transmissivity of the atmosphere, diffuse radiation fraction, cloud cover and seasonal energy potential for a zenith facing surface with unobstructed horizon for each installation location based on the results of a broadband radiative transfer model and our cloud database based on 17-years of GOES data. Our analysis was incorporated in a GIS framework in combination with additional infrastructure data that enabled a synergistic resource assessment based on the combination of climatological and engineering factors.

  10. An adaptive wavelet-network model for forecasting daily total solar-radiation

    International Nuclear Information System (INIS)

    Mellit, A.; Benghanem, M.; Kalogirou, S.A.

    2006-01-01

    The combination of wavelet theory and neural networks has lead to the development of wavelet networks. Wavelet-networks are feed-forward networks using wavelets as activation functions. Wavelet-networks have been used successfully in various engineering applications such as classification, identification and control problems. In this paper, the use of adaptive wavelet-network architecture in finding a suitable forecasting model for predicting the daily total solar-radiation is investigated. Total solar-radiation is considered as the most important parameter in the performance prediction of renewable energy systems, particularly in sizing photovoltaic (PV) power systems. For this purpose, daily total solar-radiation data have been recorded during the period extending from 1981 to 2001, by a meteorological station in Algeria. The wavelet-network model has been trained by using either the 19 years of data or one year of the data. In both cases the total solar radiation data corresponding to year 2001 was used for testing the model. The network was trained to accept and handle a number of unusual cases. Results indicate that the model predicts daily total solar-radiation values with a good accuracy of approximately 97% and the mean absolute percentage error is not more than 6%. In addition, the performance of the model was compared with different neural network structures and classical models. Training algorithms for wavelet-networks require smaller numbers of iterations when compared with other neural networks. The model can be used to fill missing data in weather databases. Additionally, the proposed model can be generalized and used in different locations and for other weather data, such as sunshine duration and ambient temperature. Finally, an application using the model for sizing a PV-power system is presented in order to confirm the validity of this model

  11. Modeling and forecasting monthly movement of annual average solar insolation based on the least-squares Fourier-model

    International Nuclear Information System (INIS)

    Yang, Zong-Chang

    2014-01-01

    Highlights: • Introduce a finite Fourier-series model for evaluating monthly movement of annual average solar insolation. • Present a forecast method for predicting its movement based on the extended Fourier-series model in the least-squares. • Shown its movement is well described by a low numbers of harmonics with approximately 6-term Fourier series. • Predict its movement most fitting with less than 6-term Fourier series. - Abstract: Solar insolation is one of the most important measurement parameters in many fields. Modeling and forecasting monthly movement of annual average solar insolation is of increasingly importance in areas of engineering, science and economics. In this study, Fourier-analysis employing finite Fourier-series is proposed for evaluating monthly movement of annual average solar insolation and extended in the least-squares for forecasting. The conventional Fourier analysis, which is the most common analysis method in the frequency domain, cannot be directly applied for prediction. Incorporated with the least-square method, the introduced Fourier-series model is extended to predict its movement. The extended Fourier-series forecasting model obtains its optimums Fourier coefficients in the least-square sense based on its previous monthly movements. The proposed method is applied to experiments and yields satisfying results in the different cities (states). It is indicated that monthly movement of annual average solar insolation is well described by a low numbers of harmonics with approximately 6-term Fourier series. The extended Fourier forecasting model predicts the monthly movement of annual average solar insolation most fitting with less than 6-term Fourier series

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2012-07-01

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

  13. A Groundwater Resource Index (GRI) for drought monitoring and forecasting in a mediterranean climate

    Science.gov (United States)

    Mendicino, Giuseppe; Senatore, Alfonso; Versace, Pasquale

    2008-08-01

    SummaryDrought indices are essential elements of an efficient drought watching system, aimed at providing a concise overall picture of drought conditions. Owing to its simplicity, time-flexibility and standardization, the Standardized Precipitation Index (SPI) has become a very widely used meteorological index, even if it is not able to account for effects of aquifers, soil, land use characteristics, canopy growth and temperature anomalies. Many other drought indices have been developed over the years, with monitoring and forecasting purposes, also with the purpose of taking advantage of the opportunities offered by remote sensing and improved general circulation models (GCMs). Moreover, some aggregated indices aimed at capturing the different features of drought have been proposed, but very few drought indices are focused on the groundwater resource status. In this paper a novel Groundwater Resource Index (GRI) is presented as a reliable tool useful in a multi-analysis approach for monitoring and forecasting drought conditions. The GRI is derived from a simple distributed water balance model, and has been tested in a Mediterranean region, characterized by different geo-lithological conditions mainly affecting the summer hydrologic response of the catchments to winter precipitation. The analysis of the GRI characteristics shows a high spatial variability and, compared to the SPI through spectral analysis, a significant sensitivity to the lithological characterization of the analyzed region. Furthermore, the GRI shows a very high auto-correlation during summer months, useful for forecasting purposes. The capability of the proposed index in forecasting summer droughts was tested analyzing the correlation of the GRI April values with the mean summer runoff values of some river basins (obtaining a mean correlation value of 0.60) and with the summer NDVI values of several forested areas, where correlation values greater than 0.77 were achieved. Moreover, its performance

  14. Statistical parameters as a means to a priori assess the accuracy of solar forecasting models

    International Nuclear Information System (INIS)

    Voyant, Cyril; Soubdhan, Ted; Lauret, Philippe; David, Mathieu; Muselli, Marc

    2015-01-01

    In this paper we propose to determinate and to test a set of 20 statistical parameters in order to estimate the short term predictability of the global horizontal irradiation time series and thereby to propose a new prospective tool indicating the expected error regardless the forecasting methods used. The mean absolute log return, which is a tool usually used in econometrics but never in global radiation prediction, proves to be a very good estimator. Some examples of the use of this tool are exposed, showing the interest of this statistical parameter in concrete cases of predictions or optimizations. This study gives a judgment for engineers and researchers on the installation or management of solar plants and could help in minimizing the energy crisis allowing to improve the renewable energy part of the energy mix. - Highlights: • Use of statistical parameter never used for the global radiation forecasting. • A priori index allowing to optimize easily and quickly a clear sky model. • New methodology allowing to quantify the prediction error regardless the predictor used. • The prediction error depends more on the location and the time series type than the machine Learning method used.

  15. Concentrating Solar Power: Best Practices Handbook for the Collection and Use of Solar Resource Data (CSP)

    Energy Technology Data Exchange (ETDEWEB)

    Stoffel, T.; Renne, D.; Myers, D.; Wilcox, S.; Sengupta, M.; George, R.; Turchi, C.

    2010-09-01

    As the world looks for low-carbon sources of energy, solar power stands out as the most abundant energy resource. Harnessing this energy is the challenge for this century. Photovoltaics and concentrating solar power (CSP) are two primary forms of electricity generation using sunlight. These use different technologies, collect different fractions of the solar resource, and have different siting and production capabilities. Although PV systems are most often deployed as distributed generation sources, CSP systems favor large, centrally located systems. Accordingly, large CSP systems require a substantial investment, sometimes exceeding $1 billion in construction costs. Before such a project is undertaken, the best possible information about the quality and reliability of the fuel source must be made available. That is, project developers need to have reliable data about the solar resource available at specific locations to predict the daily and annual performance of a proposed CSP plant. Without these data, no financial analysis is possible. This handbook presents detailed information about solar resource data and the resulting data products needed for each stage of the project.

  16. Net load forecasting for high renewable energy penetration grids

    International Nuclear Information System (INIS)

    Kaur, Amanpreet; Nonnenmacher, Lukas; Coimbra, Carlos F.M.

    2016-01-01

    We discuss methods for net load forecasting and their significance for operation and management of power grids with high renewable energy penetration. Net load forecasting is an enabling technology for the integration of microgrid fleets with the macrogrid. Net load represents the load that is traded between the grids (microgrid and utility grid). It is important for resource allocation and electricity market participation at the point of common coupling between the interconnected grids. We compare two inherently different approaches: additive and integrated net load forecast models. The proposed methodologies are validated on a microgrid with 33% annual renewable energy (solar) penetration. A heuristics based solar forecasting technique is proposed, achieving skill of 24.20%. The integrated solar and load forecasting model outperforms the additive model by 10.69% and the uncertainty range for the additive model is larger than the integrated model by 2.2%. Thus, for grid applications an integrated forecast model is recommended. We find that the net load forecast errors and the solar forecasting errors are cointegrated with a common stochastic drift. This is useful for future planning and modeling because the solar energy time-series allows to infer important features of the net load time-series, such as expected variability and uncertainty. - Highlights: • Net load forecasting methods for grids with renewable energy generation are discussed. • Integrated solar and load forecasting outperforms the additive model by 10.69%. • Net load forecasting reduces the uncertainty between the interconnected grids.

  17. Developing a forecast model of solar proton flux profiles for well-connected events

    Science.gov (United States)

    Ji, E. Y.; Moon, Y. J.; Park, J.

    2014-12-01

    We have developed a forecast model of solar proton flux profile (> 10 MeV channel) for well-connected events. Among 136 solar proton events (SPEs) from 1986 to 2006, we select 49 well-connected ones that are all associated with single X-ray flares stronger than M1 class and start to increase within four hours after their X-ray peak times. These events show rapid increments in proton flux. By comparing several empirical functions, we select a modified Weibull curve function to approximate a SPE flux profile, which is similar to the particle injection rate. The parameters (peak value, rise time and decay time) of this function are determined by the relationship between X-ray flare parameters (peak flux, impulsive time, and emission measure) and SPE parameters. For 49 well-connected SPEs, the linear correlation between the predicted proton peak flux and the observed proton peak fluxes is 0.65 with the RMS error of 0.55 pfu in the log10. In addition, we have developed another forecast model based on flare and CME parameters using 22 SPEs. The used CME parameters are linear speed and angular width. As a result, we find that the linear correlation between the predicted proton peak flux and the observed proton peak fluxes is 0.83 with the RMS error of 0.35 pfu in the log10. From the relationship between the model error and CME acceleration, we find that CME acceleration is also an important factor for predicting proton flux profiles.

  18. A Public-Private-Academic Partnership to Advance Solar Power Forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Marquis, Melinda [National Oceanic and Atmospheric Administration (NOAA), Boulder, CO (United States). Earth System Research Lab; Benjamin, Stan [National Oceanic and Atmospheric Administration (NOAA), Boulder, CO (United States). Earth System Research Lab; James, Eric [National Oceanic and Atmospheric Administration (NOAA), Boulder, CO (United States). Earth System Research Lab; Univ. of Colorado, Boulder, CO (United States); Lantz, kathy [National Oceanic and Atmospheric Administration (NOAA), Boulder, CO (United States). Earth System Research Lab; Univ. of Colorado, Boulder, CO (United States); Molling, Christine [National Oceanic and Atmospheric Administration (NOAA), Boulder, CO (United States). Earth System Research Lab; Univ. of Wisconsin, Madison, WI (United States)

    2015-04-30

    Executive Summary NOAA is making major contributions to the solar forecasting project in three areas. First, it is improving its forecasts of solar irradiance, clouds, and aerosols in its numerical weather prediction models. Second, it is providing advanced satellite products for DOE's FOA awardees to use in their forecast systems. Third, it is using high-quality ground-based measurements from SURFRAD and ISIS stations to verify and validate forecast model output. This reports covers results from all three areas for the period May 1, 2014 - April 30, 2015. Modeling In its modeling effort, NOAA continues work to improve the skill of solar forecasts from the Earth System Research Lab (ESRL) research versions of the 13-km Rapid Refresh (RAP) and the 3-km High-Resolution Rapid Refresh (HRRR) models, which are in turn transitioned into operations at the National Centers for Environmental Prediction (NCEP). A major milestone was achieved in September 2014 with the initial operational implementation of the HRRR at NCEP. In the ESRL research versions of the models, testing and development, in both real-time runs and retrospective experiments, is guided by an extensive in-house verification system. Early in the SFIP project, we developed the capability to verify our model forecasts against the high-quality surface radiation measurements from the SURFRAD and ISIS networks. This highlighted some shortcomings with the RAP and HRRR forecasts of incoming shortwave radiation. Most of our effort during Phase 1 of SFIP was focused on addressing these problems with a variety of model system improvements. The RAP and HRRR models during the warm season of 2014 had a noticeable warm and dry bias in near-surface conditions over most of the central and eastern United States, and our new SURFRAD/ISIS verification revealed that there was also a large excess of incoming global horizontal irradiance in the models. We hypothesized that a lack of cloud cover (particularly low-level cloud

  19. Physics-based Space Weather Forecasting in the Project for Solar-Terrestrial Environment Prediction (PSTEP) in Japan

    Science.gov (United States)

    Kusano, K.

    2016-12-01

    Project for Solar-Terrestrial Environment Prediction (PSTEP) is a Japanese nation-wide research collaboration, which was recently launched. PSTEP aims to develop a synergistic interaction between predictive and scientific studies of the solar-terrestrial environment and to establish the basis for next-generation space weather forecasting using the state-of-the-art observation systems and the physics-based models. For this project, we coordinate the four research groups, which develop (1) the integration of space weather forecast system, (2) the physics-based solar storm prediction, (3) the predictive models of magnetosphere and ionosphere dynamics, and (4) the model of solar cycle activity and its impact on climate, respectively. In this project, we will build the coordinated physics-based model to answer the fundamental questions concerning the onset of solar eruptions and the mechanism for radiation belt dynamics in the Earth's magnetosphere. In this paper, we will show the strategy of PSTEP, and discuss about the role and prospect of the physics-based space weather forecasting system being developed by PSTEP.

  20. Managing living marine resources in a dynamic environment: the role of seasonal to decadal climate forecasts

    DEFF Research Database (Denmark)

    Tommasi, Desiree; Stock, Charles A.; Hobday, Alistair J.

    2017-01-01

    and industry operations, as well as new research avenues in fisheries science. LMRs respond to climate variability via changes in physiology and behavior. For species and systems where climate-fisheries links are well established, forecasted LMR responses can lead to anticipatory and more effective decisions......Recent developments in global dynamical climate prediction systems have allowed for skillful predictions of climate variables relevant to living marine resources (LMRs) at a scale useful to understanding and managing LMRs. Such predictions present opportunities for improved LMR management......, benefitting both managers and stakeholders. Here, we provide an overview of climate prediction systems and advances in seasonal to decadal prediction of marine-resource relevant environmental variables. We then describe a range of climate-sensitive LMR decisions that can be taken at lead-times of months...

  1. Methodology for Clustering High-Resolution Spatiotemporal Solar Resource Data

    Energy Technology Data Exchange (ETDEWEB)

    Getman, Dan [National Renewable Energy Lab. (NREL), Golden, CO (United States); Lopez, Anthony [National Renewable Energy Lab. (NREL), Golden, CO (United States); Mai, Trieu [National Renewable Energy Lab. (NREL), Golden, CO (United States); Dyson, Mark [National Renewable Energy Lab. (NREL), Golden, CO (United States)

    2015-09-01

    In this report, we introduce a methodology to achieve multiple levels of spatial resolution reduction of solar resource data, with minimal impact on data variability, for use in energy systems modeling. The selection of an appropriate clustering algorithm, parameter selection including cluster size, methods of temporal data segmentation, and methods of cluster evaluation are explored in the context of a repeatable process. In describing this process, we illustrate the steps in creating a reduced resolution, but still viable, dataset to support energy systems modeling, e.g. capacity expansion or production cost modeling. This process is demonstrated through the use of a solar resource dataset; however, the methods are applicable to other resource data represented through spatiotemporal grids, including wind data. In addition to energy modeling, the techniques demonstrated in this paper can be used in a novel top-down approach to assess renewable resources within many other contexts that leverage variability in resource data but require reduction in spatial resolution to accommodate modeling or computing constraints.

  2. The state of solar energy resource assessment in Chile

    Energy Technology Data Exchange (ETDEWEB)

    Ortega, Alberto; Escobar, Rodrigo [Mechanical and Metallurgical Engineering Department, Pontificia Universidad Catolica de Chile, Vicuna Mackenna 4860, Macul, Santiago (Chile); Colle, Sergio [Laboratorios de Engenharia de Processos de Conversao e Tecnologia de Energia - LEPTEN, Mechanical Engineering Department, Universidade Federal de Santa Catarina, Florianopolis (Brazil); de Abreu, Samuel Luna [IFSC - Instituto Federal de Santa Catarina, Campus Sao Jose, Sao Jose - SC (Brazil)

    2010-11-15

    The Chilean government has determined that a renewable energy quota of up to 10% of the electrical energy generated must be met by 2024. This plan has already sparked interest in wind, geothermal, hydro and biomass power plants in order to introduce renewable energy systems to the country. Solar energy is being considered only for demonstration, small-scale CSP plants and for domestic water heating applications. This apparent lack of interest in solar energy is partly due to the absence of a valid solar energy database, adequate for energy system simulation and planning activities. One of the available solar radiation databases is 20-40 years old, with measurements taken by pyranographs and Campbell-Stokes devices. A second database from the Chilean Meteorological Service is composed by pyranometer readings, sparsely distributed along the country and available from 1988, with a number of these stations operating intermittently. The Chilean government through its National Energy Commission (CNE) has contracted the formulation of a simulation model and also the deployment of network of measurement stations in northern Chile. Recent efforts by the authors have resulted in a preliminary assessment by satellite image processing. Here, we compare the existing databases of solar radiation in Chile. Monthly mean solar energy maps are created from ground measurements and satellite estimations and compared. It is found that significant deviation exists between sources, and that all ground-station measurements display unknown uncertainty levels, thus highlighting the need for a proper, country-wide long-term resource assessment initiative. However, the solar energy levels throughout the country can be considered as high, and it is thought that they are adequate for energy planning activities - although not yet for proper power plant design and dimensioning. (author)

  3. How seasonal forecast could help a decision maker: an example of climate service for water resource management

    Science.gov (United States)

    Viel, Christian; Beaulant, Anne-Lise; Soubeyroux, Jean-Michel; Céron, Jean-Pierre

    2016-04-01

    The FP7 project EUPORIAS was a great opportunity for the climate community to co-design with stakeholders some original and innovative climate services at seasonal time scales. In this framework, Météo-France proposed a prototype that aimed to provide to water resource managers some tailored information to better anticipate the coming season. It is based on a forecasting system, built on a refined hydrological suite, forced by a coupled seasonal forecast model. It particularly delivers probabilistic river flow prediction on river basins all over the French territory. This paper presents the work we have done with "EPTB Seine Grands Lacs" (EPTB SGL), an institutional stakeholder in charge of the management of 4 great reservoirs on the upper Seine Basin. First, we present the co-design phase, which means the translation of classical climate outputs into several indices, relevant to influence the stakeholder's decision making process (DMP). And second, we detail the evaluation of the impact of the forecast on the DMP. This evaluation is based on an experiment realised in collaboration with the stakeholder. Concretely EPTB SGL has replayed some past decisions, in three different contexts: without any forecast, with a forecast A and with a forecast B. One of forecast A and B really contained seasonal forecast, the other only contained random forecasts taken from past climate. This placebo experiment, realised in a blind test, allowed us to calculate promising skill scores of the DMP based on seasonal forecast in comparison to a classical approach based on climatology, and to EPTG SGL current practice.

  4. Development of distributed topographical forecasting model for wind resource assessment using artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Narayana, P.B. [Green Life Energy Solutions LLP, Secunderabad (India); Rao, S.S. [National Institute of Technology. Dept. of Mechanical Engineering, Warangal (India); Reddy, K.H. [JNT Univ.. Dept. of Mechanical Engineering, Anantapur (India)

    2012-07-01

    Economics of wind power projects largely depend on the availability of wind power density. Wind resource assessment is a study estimating wind speeds and wind power densities in the region under consideration. The accuracy and reliability of data sets comprising of wind speeds and wind power densities at different heights per topographic region characterized by elevation or mean sea level, is important for wind power projects. Indian Wind Resource Assessment program conducted in 80's consisted of wind data measured by monitoring stations at different topographies in order to measure wind power density values at 25 and 50 meters above the ground level. In this paper, an attempt has been made to assess wind resource at a given location using artificial neural networks. Existing wind resource data has been used to train the neural networks. Location topography (characterized by longitude, latitude and mean sea level), air density, mean annual wind speed (MAWS) are used as inputs to the neural network. Mean annual wind power density (MAWPD) in watt/m{sup 2} is predicted for a new topographic location. Simple back propagation based neural network has been found to be sufficient for predicting these values with suitable accuracy. This model is closely linked to the problem of wind energy forecasting considering the variations of specific atmospheric variables with time horizons. This model will help the wind farm developers to have an initial estimation of the wind energy potential at a particular topography. (Author)

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

    Directory of Open Access Journals (Sweden)

    Fei Wang

    2017-12-01

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

  6. Managing living marine resources in a dynamic environment: The role of seasonal to decadal climate forecasts

    Science.gov (United States)

    Tommasi, Desiree; Stock, Charles A.; Hobday, Alistair J.; Methot, Rick; Kaplan, Isaac C.; Eveson, J. Paige; Holsman, Kirstin; Miller, Timothy J.; Gaichas, Sarah; Gehlen, Marion; Pershing, Andrew; Vecchi, Gabriel A.; Msadek, Rym; Delworth, Tom; Eakin, C. Mark; Haltuch, Melissa A.; Séférian, Roland; Spillman, Claire M.; Hartog, Jason R.; Siedlecki, Samantha; Samhouri, Jameal F.; Muhling, Barbara; Asch, Rebecca G.; Pinsky, Malin L.; Saba, Vincent S.; Kapnick, Sarah B.; Gaitan, Carlos F.; Rykaczewski, Ryan R.; Alexander, Michael A.; Xue, Yan; Pegion, Kathleen V.; Lynch, Patrick; Payne, Mark R.; Kristiansen, Trond; Lehodey, Patrick; Werner, Francisco E.

    2017-03-01

    Recent developments in global dynamical climate prediction systems have allowed for skillful predictions of climate variables relevant to living marine resources (LMRs) at a scale useful to understanding and managing LMRs. Such predictions present opportunities for improved LMR management and industry operations, as well as new research avenues in fisheries science. LMRs respond to climate variability via changes in physiology and behavior. For species and systems where climate-fisheries links are well established, forecasted LMR responses can lead to anticipatory and more effective decisions, benefitting both managers and stakeholders. Here, we provide an overview of climate prediction systems and advances in seasonal to decadal prediction of marine-resource relevant environmental variables. We then describe a range of climate-sensitive LMR decisions that can be taken at lead-times of months to decades, before highlighting a range of pioneering case studies using climate predictions to inform LMR decisions. The success of these case studies suggests that many additional applications are possible. Progress, however, is limited by observational and modeling challenges. Priority developments include strengthening of the mechanistic linkages between climate and marine resource responses, development of LMR models able to explicitly represent such responses, integration of climate driven LMR dynamics in the multi-driver context within which marine resources exist, and improved prediction of ecosystem-relevant variables at the fine regional scales at which most marine resource decisions are made. While there are fundamental limits to predictability, continued advances in these areas have considerable potential to make LMR managers and industry decision more resilient to climate variability and help sustain valuable resources. Concerted dialog between scientists, LMR managers and industry is essential to realizing this potential.

  7. State of the Science for Sub-Seasonal to Seasonal Precipitation Forecasting in Support of Water Resource Managers

    Science.gov (United States)

    DeWitt, D. G.

    2017-12-01

    Water resource managers are one of the communities that would strongly benefit from highly-skilled sub-seasonal to seasonal precipitation forecasts. Unfortunately, the current state of the art prediction tools frequently fail to provide a level of skill sufficient to meet the stakeholders needs, especially on the monthly and seasonal timescale. On the other hand, the skill of precipitation forecasts on the week-2 timescale are relatively high and arguably useful in many decision-making contexts. This talk will present a comparison of forecast skill for the week-2 through the first season timescale and describe current efforts within NOAA and elsewhere to try to improve forecast skill beyond week-2, including research gaps that need to be addressed in order to make progress.

  8. Forecasting the Depletion of Transboundary Groundwater Resources in Hyper-Arid Environments

    Science.gov (United States)

    Mazzoni, A.; Heggy, E.

    2014-12-01

    The increase in awareness about the overexploitation of transboundary groundwater resources in hyper-arid environments that occurred in the last decades has highlighted the need to better map, monitor and manage these resources. Climate change, economic and population growth are driving forces that put more pressure on these fragile but fundamental resources. The aim of our approach is to address the question of whether or not groundwater resources, especially non-renewable, could serve as "backstop" water resource during water shortage periods that would probably affect the drylands in the upcoming 100 years. The high dependence of arid regions on these resources requires prudent management to be able to preserve their fossil aquifers and exploit them in a more sustainable way. We use the NetLogo environment with the FAO Aquastat Database to evaluate if the actual trends of extraction, consumption and use of non-renewable groundwater resources would remain feasible with the future climate change impacts and the population growth scenarios. The case studies selected are three: the Nubian Sandstone Aquifer System, shared between Egypt, Libya, Sudan and Chad; the North Western Sahara Aquifer System, with Algeria, Tunisia and Libya and the Umm Radhuma Dammam Aquifer, in its central part, shared between Saudi Arabia, Qatar and Bahrain. The reason these three fossil aquifers were selected are manifold. First, they represent properly transboundary non-renewable groundwater resources, with all the implications that derive from this, i.e. the necessity of scientific and socio-political cooperation among riparians, the importance of monitoring the status of shared resources and the need to elaborate a shared management policy. Furthermore, each country is characterized by hyper-arid climatic conditions, which will be exacerbated in the next century by climate change and lead to probable severe water shortage periods. Together with climate change, the rate of population

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

    DEFF Research Database (Denmark)

    Sperati, Simone; Alessandrini, Stefano; Pinson, Pierre

    2015-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Gianfranco Chicco

    2015-12-01

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

  11. Estimating solar resources in Mexico using cloud cover data

    Energy Technology Data Exchange (ETDEWEB)

    Renne, David; George, Ray; Brady, Liz; Marion, Bill [National Renewable Energy Laboratory, Colorado (United States); Estrada Cajigal, Vicente [Cuernavaca, Morelos (Mexico)

    2000-07-01

    This paper presents the results of applying the National Renewable Energy Laboratory's (NREL) Climatological Solar Radiation (CSR) model to Mexico to develop solar resource data. A major input to the CSR model is a worldwide surface and satellite-derived cloud cover database, called the Real Time Nephanalysis (RTNEPH). The RTNEPH is developed by the U.S. Air Force and distributed by the U.S. National Climatic Data Center. The RTNEPH combines routine ground-based cloud cover observations made every three hours at national weather centers throughout the world with satellite-derived cloud cover information developed from polar orbiting weather satellites. The data are geospatially digitized so that multilayerd cloud cover information is available on a grid of approximately 40-km to a side. The development of this database is an ongoing project that now covers more than twenty years of observations. For the North America analysis (including Mexico) we used an 8-year summarized histogram of the RTNEPH that provides monthly average cloud cover information for the period 1985-1992. The CSR model also accounts for attenuation of the solar beam due to aerosols, atmospheric trace gases, and water vapor. The CSR model outputs monthly average direct normal, global horizontal and diffuse solar information for each of the 40-km grid cells. From this information it is also possible to produce solar resource estimates for various solar collector types and orientations, such as flat plate collectors oriented at latitude tilt, or concentrating solar power collectors. Model results are displayed using Geographic Information System software. CSR model results for Mexico are presented here, along with a discussion of earlier solar resource assessment studies for Mexico, where both modeling approaches and measurement analyses have been used. [Spanish] Este articulo presenta los resultados de aplicar el modelo Radiacion Solar Climatologica CSR del NREL (National Renewable Energy

  12. Evaluation of Statistical Methods for Modeling Historical Resource Production and Forecasting

    Science.gov (United States)

    Nanzad, Bolorchimeg

    This master's thesis project consists of two parts. Part I of the project compares modeling of historical resource production and forecasting of future production trends using the logit/probit transform advocated by Rutledge (2011) with conventional Hubbert curve fitting, using global coal production as a case study. The conventional Hubbert/Gaussian method fits a curve to historical production data whereas a logit/probit transform uses a linear fit to a subset of transformed production data. Within the errors and limitations inherent in this type of statistical modeling, these methods provide comparable results. That is, despite that apparent goodness-of-fit achievable using the Logit/Probit methodology, neither approach provides a significant advantage over the other in either explaining the observed data or in making future projections. For mature production regions, those that have already substantially passed peak production, results obtained by either method are closely comparable and reasonable, and estimates of ultimately recoverable resources obtained by either method are consistent with geologically estimated reserves. In contrast, for immature regions, estimates of ultimately recoverable resources generated by either of these alternative methods are unstable and thus, need to be used with caution. Although the logit/probit transform generates high quality-of-fit correspondence with historical production data, this approach provides no new information compared to conventional Gaussian or Hubbert-type models and may have the effect of masking the noise and/or instability in the data and the derived fits. In particular, production forecasts for immature or marginally mature production systems based on either method need to be regarded with considerable caution. Part II of the project investigates the utility of a novel alternative method for multicyclic Hubbert modeling tentatively termed "cycle-jumping" wherein overlap of multiple cycles is limited. The

  13. Forecasting the Earth’s radiation belts and modelling solar energetic particle events: Recent results from SPACECAST

    Directory of Open Access Journals (Sweden)

    Poedts Stefaan

    2013-05-01

    Full Text Available High-energy charged particles in the van Allen radiation belts and in solar energetic particle events can damage satellites on orbit leading to malfunctions and loss of satellite service. Here we describe some recent results from the SPACECAST project on modelling and forecasting the radiation belts, and modelling solar energetic particle events. We describe the SPACECAST forecasting system that uses physical models that include wave-particle interactions to forecast the electron radiation belts up to 3 h ahead. We show that the forecasts were able to reproduce the >2 MeV electron flux at GOES 13 during the moderate storm of 7–8 October 2012, and the period following a fast solar wind stream on 25–26 October 2012 to within a factor of 5 or so. At lower energies of 10 – a few 100 keV we show that the electron flux at geostationary orbit depends sensitively on the high-energy tail of the source distribution near 10 RE on the nightside of the Earth, and that the source is best represented by a kappa distribution. We present a new model of whistler mode chorus determined from multiple satellite measurements which shows that the effects of wave-particle interactions beyond geostationary orbit are likely to be very significant. We also present radial diffusion coefficients calculated from satellite data at geostationary orbit which vary with Kp by over four orders of magnitude. We describe a new automated method to determine the position at the shock that is magnetically connected to the Earth for modelling solar energetic particle events and which takes into account entropy, and predict the form of the mean free path in the foreshock, and particle injection efficiency at the shock from analytical theory which can be tested in simulations.

  14. Forecasting resource-allocation decisions under climate uncertainty: fire suppression with assessment of net benefits of research

    Science.gov (United States)

    Jeffrey P. Prestemon; Geoffrey H. Donovan

    2008-01-01

    Making input decisions under climate uncertainty often involves two-stage methods that use expensive and opaque transfer functions. This article describes an alternative, single-stage approach to such decisions using forecasting methods. The example shown is for preseason fire suppression resource contracting decisions faced by the United States Forest Service. Two-...

  15. Solar Activity from 2006 to 2014 and Short-term Forecasts of Solar Proton Events Using the ESPERTA Model

    Energy Technology Data Exchange (ETDEWEB)

    Alberti, T.; Lepreti, F. [Dipartimento di Fisica, Università della Calabria, Ponte P. Bucci, Cubo 31C, 87036, Rende (CS) (Italy); Laurenza, M.; Storini, M.; Consolini, G. [INAF-IAPS, Via del Fosso del Cavaliere 100, I-00133, Roma (Italy); Cliver, E. W., E-mail: tommaso.alberti@unical.it, E-mail: monica.laurenza@iaps.inaf.it [National Solar Observatory, Boulder, CO (United States)

    2017-03-20

    To evaluate the solar energetic proton (SEP) forecast model of Laurenza et al., here termed ESPERTA, we computed the input parameters (soft X-ray (SXR) fluence and ∼1 MHz radio fluence) for all ≥M2 SXR flares from 2006 to 2014. This database is outside the 1995–2005 interval on which ESPERTA was developed. To assess the difference in the general level of activity between these two intervals, we compared the occurrence frequencies of SXR flares and SEP events for the first six years of cycles 23 (1996 September–2002 September) and 24 (2008 December–2014 December). We found a reduction of SXR flares and SEP events of 40% and 46%, respectively, in the latter period. Moreover, the numbers of ≥M2 flares with high values of SXR and ∼1 MHz fluences (>0.1 J m{sup −2} and >6 × 10{sup 5} sfu × minute, respectively) are both reduced by ∼30%. A somewhat larger percentage decrease of these two parameters (∼40% versus ∼30%) is obtained for the 2006–2014 interval in comparison with 1995–2005. Despite these differences, ESPERTA performance was comparable for the two intervals. For the 2006–2014 interval, ESPERTA had a probability of detection (POD) of 59% (19/32) and a false alarm rate (FAR) of 30% (8/27), versus a POD = 63% (47/75) and an FAR = 42% (34/81) for the original 1995–2005 data set. In addition, for the 2006–2014 interval the median (average) warning time was estimated to be ∼2 hr (∼7 hr), versus ∼6 hr (∼9 hr), for the 1995–2005 data set.

  16. High-Resolution Hydrological Sub-Seasonal Forecasting for Water Resources Management Over Europe

    Science.gov (United States)

    Wood, E. F.; Wanders, N.; Pan, M.; Sheffield, J.; Samaniego, L. E.; Thober, S.; Kumar, R.; Prudhomme, C.; Houghton-Carr, H.

    2017-12-01

    For decision-making at the sub-seasonal and seasonal time scale, hydrological forecasts with a high temporal and spatial resolution are required by water managers. So far such forecasts have been unavailable due to 1) lack of availability of meteorological seasonal forecasts, 2) coarse temporal resolution of meteorological seasonal forecasts, requiring temporal downscaling, 3) lack of consistency between observations and seasonal forecasts, requiring bias-correction. The EDgE (End-to-end Demonstrator for improved decision making in the water sector in Europe) project commissioned by the ECMWF (C3S) created a unique dataset of hydrological seasonal forecasts derived from four global climate models (CanCM4, FLOR-B01, ECMF, LFPW) in combination with four global hydrological models (PCR-GLOBWB, VIC, mHM, Noah-MP), resulting in 208 forecasts for any given day. The forecasts provide a daily temporal and 5-km spatial resolution, and are bias corrected against E-OBS meteorological observations. The forecasts are communicated to stakeholders via Sectoral Climate Impact Indicators (SCIIs), created in collaboration with the end-user community of the EDgE project (e.g. the percentage of ensemble realizations above the 10th percentile of monthly river flow, or below the 90th). Results show skillful forecasts for discharge from 3 months to 6 months (latter for N Europe due to snow); for soil moisture up to three months due precipitation forecast skill and short initial condition memory; and for groundwater greater than 6 months (lowest skill in western Europe.) The SCIIs are effective in communicating both forecast skill and uncertainty. Overall the new system provides an unprecedented ensemble for seasonal forecasts with significant skill over Europe to support water management. The consistency in both the GCM forecasts and the LSM parameterization ensures a stable and reliable forecast framework and methodology, even if additional GCMs or LSMs are added in the future.

  17. Using size distribution analysis to forecast natural gas resources in Asia Pacific

    International Nuclear Information System (INIS)

    Aguilera, Roberto F.; Ripple, Ronald D.

    2011-01-01

    Highlights: → We estimate the total endowment of conventional natural gas in Asia Pacific. → Includes volumes in previously unassessed provinces. → Endowment distributed across countries to show where volumes are most likely to be found. → A breakdown between offshore versus onshore resources is also estimated. → We find there is a significant natural gas endowment in the region. -- Abstract: Increasing energy consumption in Asia Pacific will largely be met by fossil fuels. Natural gas production in the region presently ranks behind that of oil and coal. However, the abundance of gas could lead to a significant gas market share increase in the energy mix. The purpose of this paper is to estimate the total endowment of conventional gas in Asia Pacific. This is carried out with a Variable Shape Distribution (VSD) model that forecasts volumes in provinces that have not been previously evaluated. The endowment is then distributed across countries to show where volumes are most likely to be found. A breakdown between offshore versus onshore resources is also estimated. The results of the analysis show there is a significant gas endowment. The estimated distribution across countries and onshore/offshore areas provides insight into the relative economics of gas production, as well as a basis for potential investment decisions. With appropriate energy policies, it may be possible to tap the vast gas potential in Asia Pacific. Considering gas may be the most abundant, inexpensive, and clean fossil fuel, the outcome would be increased energy security and a low carbon economy.

  18. Efficient multi-scenario Model Predictive Control for water resources management with ensemble streamflow forecasts

    Science.gov (United States)

    Tian, Xin; Negenborn, Rudy R.; van Overloop, Peter-Jules; María Maestre, José; Sadowska, Anna; van de Giesen, Nick

    2017-11-01

    Model Predictive Control (MPC) is one of the most advanced real-time control techniques that has been widely applied to Water Resources Management (WRM). MPC can manage the water system in a holistic manner and has a flexible structure to incorporate specific elements, such as setpoints and constraints. Therefore, MPC has shown its versatile performance in many branches of WRM. Nonetheless, with the in-depth understanding of stochastic hydrology in recent studies, MPC also faces the challenge of how to cope with hydrological uncertainty in its decision-making process. A possible way to embed the uncertainty is to generate an Ensemble Forecast (EF) of hydrological variables, rather than a deterministic one. The combination of MPC and EF results in a more comprehensive approach: Multi-scenario MPC (MS-MPC). In this study, we will first assess the model performance of MS-MPC, considering an ensemble streamflow forecast. Noticeably, the computational inefficiency may be a critical obstacle that hinders applicability of MS-MPC. In fact, with more scenarios taken into account, the computational burden of solving an optimization problem in MS-MPC accordingly increases. To deal with this challenge, we propose the Adaptive Control Resolution (ACR) approach as a computationally efficient scheme to practically reduce the number of control variables in MS-MPC. In brief, the ACR approach uses a mixed-resolution control time step from the near future to the distant future. The ACR-MPC approach is tested on a real-world case study: an integrated flood control and navigation problem in the North Sea Canal of the Netherlands. Such an approach reduces the computation time by 18% and up in our case study. At the same time, the model performance of ACR-MPC remains close to that of conventional MPC.

  19. Acceleration, Transport, Forecasting and Impact of solar energetic particles in the framework of the 'HESPERIA' HORIZON 2020 project

    Science.gov (United States)

    Malandraki, Olga; Klein, Karl-Ludwig; Vainio, Rami; Agueda, Neus; Nunez, Marlon; Heber, Bernd; Buetikofer, Rolf; Sarlanis, Christos; Crosby, Norma

    2017-04-01

    High-energy solar energetic particles (SEPs) emitted from the Sun are a major space weather hazard motivating the development of predictive capabilities. In this work, the current state of knowledge on the origin and forecasting of SEP events will be reviewed. Subsequently, we will present the EU HORIZON2020 HESPERIA (High Energy Solar Particle Events foRecastIng and Analysis) project, its structure, its main scientific objectives and forecasting operational tools, as well as the added value to SEP research both from the observational as well as the SEP modelling perspective. The project addresses through multi-frequency observations and simulations the chain of processes from particle acceleration in the corona, particle transport in the magnetically complex corona and interplanetary space to the detection near 1 AU. Furthermore, publicly available software to invert neutron monitor observations of relativistic SEPs to physical parameters that can be compared with space-borne measurements at lower energies is provided for the first time by HESPERIA. In order to achieve these goals, HESPERIA is exploiting already available large datasets stored in databases such as the neutron monitor database (NMDB) and SEPServer that were developed under EU FP7 projects from 2008 to 2013. Forecasting results of the two novel SEP operational forecasting tools published via the consortium server of 'HESPERIA' will be presented, as well as some scientific key results on the acceleration, transport and impact on Earth of high-energy particles. Acknowledgement: This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 637324.

  20. Very short-term reactive forecasting of the solar ultraviolet index using an extreme learning machine integrated with the solar zenith angle.

    Science.gov (United States)

    Deo, Ravinesh C; Downs, Nathan; Parisi, Alfio V; Adamowski, Jan F; Quilty, John M

    2017-05-01

    Exposure to erythemally-effective solar ultraviolet radiation (UVR) that contributes to malignant keratinocyte cancers and associated health-risk is best mitigated through innovative decision-support systems, with global solar UV index (UVI) forecast necessary to inform real-time sun-protection behaviour recommendations. It follows that the UVI forecasting models are useful tools for such decision-making. In this study, a model for computationally-efficient data-driven forecasting of diffuse and global very short-term reactive (VSTR) (10-min lead-time) UVI, enhanced by drawing on the solar zenith angle (θ s ) data, was developed using an extreme learning machine (ELM) algorithm. An ELM algorithm typically serves to address complex and ill-defined forecasting problems. UV spectroradiometer situated in Toowoomba, Australia measured daily cycles (0500-1700h) of UVI over the austral summer period. After trialling activations functions based on sine, hard limit, logarithmic and tangent sigmoid and triangular and radial basis networks for best results, an optimal ELM architecture utilising logarithmic sigmoid equation in hidden layer, with lagged combinations of θ s as the predictor data was developed. ELM's performance was evaluated using statistical metrics: correlation coefficient (r), Willmott's Index (WI), Nash-Sutcliffe efficiency coefficient (E NS ), root mean square error (RMSE), and mean absolute error (MAE) between observed and forecasted UVI. Using these metrics, the ELM model's performance was compared to that of existing methods: multivariate adaptive regression spline (MARS), M5 Model Tree, and a semi-empirical (Pro6UV) clear sky model. Based on RMSE and MAE values, the ELM model (0.255, 0.346, respectively) outperformed the MARS (0.310, 0.438) and M5 Model Tree (0.346, 0.466) models. Concurring with these metrics, the Willmott's Index for the ELM, MARS and M5 Model Tree models were 0.966, 0.942 and 0.934, respectively. About 57% of the ELM model

  1. 太阳能预报方法及其应用和问题%A Review on Methods of Solar Energy Forecasting and Its Application

    Institute of Scientific and Technical Information of China (English)

    马金玉; 罗勇; 申彦波; 李世奎

    2011-01-01

    太阳能预报包括预测太阳辐射量和光伏发电功率,对光伏发电系统并网运行有重要意义,是当前太阳能开发利用的一个关键问题.本文对国内外太阳能预报方法进行了扼要的评述,归纳了太阳能预报的机理及其方法在光伏发电中的应用.太阳辐射的预报方法主要有传统统计、神经网络、卫星遥感和数值模拟等方法.文中基于光伏发电应用的需求,分析了不同预报方法的优点和不足,并探讨了若干有待进一步改善的问题,展望了国内太阳能预报技术方法的发展和应用前景.%Solar forecasting, consisting of solar radiation forecasting and photovoltaic solar power forecasting, is important for photovoltaic power generation systems in network operation. In recent years, with the development of the solar industry, the demand for solar energy forecasting is increasing. Solar energy prediction methods have been developed in developed country. Our solar photovoltaic technology research is, however, at a primary stage, with only a few universities and institutes conducting simulation-based research, little of which accounts for meteorological factors.According to predicted solar physical factors, the prediction can be generally divided into two categories. One is to predict solar radiation which requires the calculation of photovoltaic power according to the output photoelectric conversion efficiency. The other is direct prediction of output power of PV systems. As the domestic forecast on solar energy technologies and applications are rarely reported, mechanisms of solar forecasting, methods and applications in photovoltaic power generation were reviewed based on the demand for photovoltaic applications. This review would provide an important basis for domestic solar photovoltaic power generation development. This paper focuses on the situations of solar energy prediction at home and abroad, and summarizes the principles of solar energy

  2. A wavelet-coupled support vector machine model for forecasting global incident solar radiation using limited meteorological dataset

    International Nuclear Information System (INIS)

    Deo, Ravinesh C.; Wen, Xiaohu; Qi, Feng

    2016-01-01

    Highlights: • A forecasting model for short- and long-term global incident solar radiation (R_n) has been developed. • The support vector machine and discrete wavelet transformation algorithm has been integrated. • The precision of the wavelet-coupled hybrid model is assessed using several prediction score metrics. • The proposed model is an appealing tool for forecasting R_n in the present study region. - Abstract: A solar radiation forecasting model can be utilized is a scientific contrivance for investigating future viability of solar energy potentials. In this paper, a wavelet-coupled support vector machine (W-SVM) model was adopted to forecast global incident solar radiation based on the sunshine hours (S_t), minimum temperature (T_m_a_x), maximum temperature (T_m_a_x), windspeed (U), evaporation (E) and precipitation (P) as the predictor variables. To ascertain conclusive results, the merit of the W-SVM was benchmarked with the classical SVM model. For daily forecasting, sixteen months of data (01-March-2014 to 30-June-2015) partitioned into the train (65%) and test (35%) set for the three metropolitan stations (Brisbane City, Cairns Aero and Townsville Aero) were utilized. Data were decomposed into their wavelet sub-series by discrete wavelet transformation algorithm and summed up to create new series with one approximation and four levels of detail using Daubechies-2 mother wavelet. For daily forecasting, six model scenarios were formulated where the number of input was increased and the forecast was assessed by statistical metrics (correlation coefficient r; Willmott’s index d; Nash-Sutcliffe coefficient E_N_S; peak deviation P_d_v), distribution statistics and prediction errors (mean absolute error MAE; root mean square error RMSE; mean absolute percentage error MAPE; relative root mean square error RMSE). Results for daily forecasts showed that the W-SVM model outperformed the classical SVM model for optimum input combinations. A sensitivity

  3. 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.

  4. Performance Analysis for One-Step-Ahead Forecasting of Hybrid Solar and Wind Energy on Short Time Scales

    Directory of Open Access Journals (Sweden)

    Jing Huang

    2018-05-01

    Full Text Available With ever increasing demand for electricity and the huge potential of renewable energy, an increasing number of renewable-energy sources are being used to generate electricity. However, due to the intermittency of renewable-energy generation, many researchers try to overcome the variable nature of renewable energy. A hybrid renewable-energy system is one possible way to introduce smoothing of the supply. Many hybrid renewable-energy studies focus on system optimization and management. This paper mainly researches the performance prediction accuracy of a hybrid solar and wind system. Through a mixed autoregressive and dynamical system model, we test the predictability of the hybrid system and compare it with individual solar and wind series forecasting. After error analysis, the predictability of the hybrid system shows a better performance than solar or wind for Adelaide global solar radiation and Starfish Hill wind farm data. The prediction errors were reduced by 13% to more than 30% according to various error analyses. This result indicates an advantage of the hybrid solar and wind system compared to solar and wind systems taken individually.

  5. Forecasting Financial Resources for Future Traumatic Spinal Cord Injury Care Using Simulation Modeling.

    Science.gov (United States)

    Ahn, Henry; Lewis, Rachel; Santos, Argelio; Cheng, Christiana L; Noonan, Vanessa K; Dvorak, Marcel F; Singh, Anoushka; Linassi, A Gary; Christie, Sean; Goytan, Michael; Atkins, Derek

    2017-10-15

    Survivors of traumatic spinal cord injury (tSCI) have intense healthcare needs during acute and rehabilitation care and often through the rest of life. To prepare for a growing and aging population, simulation modeling was used to forecast the change in healthcare financial resources and long-term patient outcomes between 2012 and 2032. The model was developed with data from acute and rehabilitation care facilities across Canada participating in the Access to Care and Timing project. Future population and tSCI incidence for 2012 and 2032 were predicted with data from Statistics Canada and the Canadian Institute for Health Information. The projected tSCI incidence for 2012 was validated with actual data from the Rick Hansen SCI Registry of the participating facilities. Using a medium growth scenario, in 2032, the projected median age of persons with tSCI is 57 and persons 61 and older will account for 46% of injuries. Admissions to acute and rehabilitation facilities in 2032 were projected to increase by 31% and 25%, respectively. Because of the demographic shift to an older population, an increase in total population life expectancy with tSCI of 13% was observed despite a 22% increase in total life years lost to tSCI between 2012 and 2032. Care cost increased 54%, and rest of life cost increased 37% in 2032, translating to an additional CAD $16.4 million. With the demographics and management of tSCI changing with an aging population, accurate projections for the increased demand on resources will be critical for decision makers when planning the delivery of healthcare after tSCI.

  6. Technology assessment of solar-energy systems. Materials resource and hazardous materials impacts of solar deployment

    Science.gov (United States)

    Schiffman, Y. M.; Tahami, J. E.

    1982-04-01

    The materials-resource and hazardous-materials impacts were determined by examining the type and quantity of materials used in the manufacture, construction, installation, operation and maintenance of solar systems. The materials requirements were compared with US materials supply and demand data to determine if potential problems exist in terms of future availability of domestic supply and increased dependence on foreign sources of supply. Hazardous materials were evaluated in terms of public and occupational health hazards and explosive and fire hazards. It is concluded that: although large amounts of materials would be required, the US had sufficient industrial capacity to produce those materials; (2) postulated growth in solar technology deployment during the period 1995-2000 could cause some production shortfalls in the steel and copper industry; the U.S. could increase its import reliance for certain materials such as silver, iron ore, and copper; however, shifts to other materials such as aluminum and polyvinylchloride could alleviate some of these problems.

  7. A seamless global hydrological monitoring and forecasting system for water resources assessment and hydrological hazard early warning

    Science.gov (United States)

    Sheffield, Justin; He, Xiaogang; Wood, Eric; Pan, Ming; Wanders, Niko; Zhan, Wang; Peng, Liqing

    2017-04-01

    Sustainable management of water resources and mitigation of the impacts of hydrological hazards are becoming ever more important at large scales because of inter-basin, inter-country and inter-continental connections in water dependent sectors. These include water resources management, food production, and energy production, whose needs must be weighed against the water needs of ecosystems and preservation of water resources for future generations. The strains on these connections are likely to increase with climate change and increasing demand from burgeoning populations and rapid development, with potential for conflict over water. At the same time, network connections may provide opportunities to alleviate pressures on water availability through more efficient use of resources such as trade in water dependent goods. A key constraint on understanding, monitoring and identifying solutions to increasing competition for water resources and hazard risk is the availability of hydrological data for monitoring and forecasting water resources and hazards. We present a global online system that provides continuous and consistent water products across time scales, from the historic instrumental period, to real-time monitoring, short-term and seasonal forecasts, and climate change projections. The system is intended to provide data and tools for analysis of historic hydrological variability and trends, water resources assessment, monitoring of evolving hazards and forecasts for early warning, and climate change scale projections of changes in water availability and extreme events. The system is particular useful for scientists and stakeholders interested in regions with less available in-situ data, and where forecasts have the potential to help decision making. The system is built on a database of high-resolution climate data from 1950 to present that merges available observational records with bias-corrected reanalysis and satellite data, which then drives a coupled land

  8. Study of the capability for rapid warnings of solar flare radiation hazards to aircraft. Part I. Forecasts and warnings of solar flare radiation hazards. Part II. An FAA polar flight solar cosmic radiation forecast/warning communication system study. Technical memo

    International Nuclear Information System (INIS)

    Sauer, H.H.; Stonehocker, G.H.

    1977-04-01

    The first part of the report provides background information on the occurrence of solar activity and the consequent sporadic production of electromagnetic and particle emissions from the sun. A summary is given of the current procedures for the forecasting of solar activity together with procedures used to verify these forecasts as currently available. A summary of current forecasting of radiation hazards as provided in support of the Concorde SST program is also given. The second part of the report describes a forecast message distribution system developed in conjunction with solar cosmic radiation forecasts and warnings of the Space Environment Laboratory of NOAA for the Federal Aviation Administration's (FAA) Office of Aviation Medicine. The study analyzes the currently available and future aeronautical telecommunication system facilities to determine an optimum system to distribute forecasts to the preflight planning centers in the international flight service stations for polar-flying subsonic and supersonic transport (SST) type aircraft. Also recommended for the system are timely and reliable distribution of warnings to individual in-flight aircraft in polar areas by the responsible air traffic control authority

  9. Alternatives for the assessment of the solar resource in Argentina

    International Nuclear Information System (INIS)

    Grossi Gallegos, H; Righini, R; Raichijk, C

    2005-01-01

    In Argentina, from 1972 on, several maps were presented which reported the distribution of global solar irradiation received on a horizontal plane placed at ground level and which used different time bases and information quality, whether estimates obtained from correlations established with other meteorological variables or direct irradiation measurements.n a paper by Grossi Gallegos (1998) 12 charts were presented with the monthly distribution of the mean value of daily global irradiation and one with their annual distribution, using all available information up to that moment in Argentina, whether from daily irradiation data obtained with Argentina s Solarimetric Network pyrano meters or sunshine hours measured by the National Meteorological Service (SMN) Network; the error due to the inclusion of estimates and interpolations was evaluated as lower than 10%.Argentina's Solarimetric Network underwent a continuous decrease in the number of operational stations due to the lack of resources for supporting them.In view of this situation, different alternatives were gradually evaluated which would make it possible to improve the already mentioned available global solar irradiation charts.In this sense, a statistical survey of the adjustment of satellite irradiation data available in Internet (in the base known as Surface Solar Energy (SSE) Data Set, Version 1.00) to the ground values.The objective was evaluating the possibility of using them as a complement to the data that had already been used and their application in order to obtain contour maps in homogeneous zones such as the Pampa Humeda, using geostatistical methods for drawing the irradiation isolines.Root-mean-square errors (RMSE) range from 3.7% to 24.8% depending on the inhomogeneity of the area. Nevertheless, the available temporal series are limited in time and thus their climatic representativity can be questioned.Given the shortage of solar irradiation measured data accurate enough to fulfill

  10. Smoothing out the volatility of South Africa’s wind and solar energy resources

    CSIR Research Space (South Africa)

    Mushwana, Crescent

    2015-10-01

    Full Text Available In the past, renewables were mainly driven by the US, Europe and China, but South Africa is slowly picking up. This presentation discusses South Africa's wind and solar resources as alternative energy resources....

  11. Estimation of wind and solar resources in Mali

    Energy Technology Data Exchange (ETDEWEB)

    Badger, J.; Kamissoko, F.; Olander Rasmussen, M.; Larsen, Soeren; Guidon, N.; Boye Hansen, L.; Dewilde, L.; Alhousseini, M.; Noergaard, P.; Nygaard, I.

    2012-11-15

    The wind resource has been estimated for all of Mali at 7.5 km resolution using the KAMM/WAsP numerical wind atlas methodology. Three domains were used to cover entire country and three sets of wind classes used to capture change in large scale forcing over country. The final output includes generalized climate statistics for any location in Mali, giving wind direction and wind speed distribution. The modelled generalized climate statistics can be used directly in the WAsP software. The preliminary results show a wind resource, which is relatively low, but which under certain conditions may be economically feasible, i.e. at favourably exposed sites, giving enhanced winds, and where practical utilization is possible, given consideration to grid connection or replacement or augmentation of diesel-based electricity systems. The solar energy resource for Mali was assessed for the period between July 2008 and June 2011 using a remote sensing based estimate of the down-welling surface shortwave flux. The remote sensing estimates were adjusted on a month-by-month basis to account for seasonal differences between the remote sensing estimates and in situ data. Calibration was found to improve the coefficient of determination as well as decreasing the mean error both for the calibration and validation data. Compared to the results presented in the ''Renewable energy resources in Mali - preliminary mapping''-report that showed a tendency for underestimation compared to data from the NASA PPOWER/SSE database, the presented results show a very good agreement with the in situ data (after calibration) with no significant bias. Unfortunately, the NASA-database only contains data up until 2005, so a similar comparison could not be done for the time period analyzed in this study, although the agreement with the historic NASA data is still useful as reference. (LN)

  12. Long-term infrastructure forecasting in the Gulf of Mexico: a decision- and resource-based approach

    International Nuclear Information System (INIS)

    Kaiser, M.J.; Mesyanzhinov, D.V.; Pulsipher, A.G.

    2004-01-01

    A long-term infrastructure forecast in the Gulf of Mexico is developed in a disaggregated decision- and resource-based environment. Models for the installation and removal rates of structures are performed across five water depth categories for the Western and Central Gulf of Mexico planning areas for structures grouped according to a major and nonmajor classification. Master hydrocarbon production schedules are constructed per water depth and planning area using a two-parameter decision model, where 'bundled' resources are recoverable at a given time and at a specific rate. The infrastructure requirements to support the expected production is determined by extrapolating historical data. The analytic forecasting framework allows for subjective judgement, technological change, analogy, and historical trends to be employed in a user-defined manner. Special attention to the aggregation procedures employed and the general methodological framework are highlighted, including a candid discussion of the limitations of analysis and suggestions for further research

  13. The Solar Reflectance Index as a Tool to Forecast the Heat Released to the Urban Environment: Potentiality and Assessment Issues

    Directory of Open Access Journals (Sweden)

    Alberto Muscio

    2018-02-01

    Full Text Available Overheating of buildings and urban areas is a more and more severe issue in view of global warming combined with increasing urbanization. The thermal behavior of urban surfaces in the hot seasons is the result of a complex balance of construction and environmental parameters such as insulation level, thermal mass, shielding, and solar reflective capability on one side, and ambient conditions on the other side. Regulations makers and the construction industry have favored the use of parameters that allow the forecasting of the interaction between different material properties without the need for complex analyses. Among these, the solar reflectance index (SRI takes into account solar reflectance and thermal emittance to predict the thermal behavior of a surface subjected to solar radiation through a physically rigorous mathematical procedure that considers assigned air and sky temperatures, peak solar irradiance, and wind velocity. The correlation of SRI with the heat released to the urban environment is analyzed in this paper, as well as the sensitivity of its calculation procedure to variation of the input parameters, as possibly induced by the measurement methods used or by the material ageing.

  14. 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.

  15. 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

  16. Integrating water data, models and forecasts - the Australian Water Resources Information System (Invited)

    Science.gov (United States)

    Argent, R.; Sheahan, P.; Plummer, N.

    2010-12-01

    Under the Commonwealth Water Act 2007 the Bureau of Meteorology was given a new national role in water information, encompassing standards, water accounts and assessments, hydrological forecasting, and collecting, enhancing and making freely available Australia’s water information. The Australian Water Resources Information System (AWRIS) is being developed to fulfil part of this role, by providing foundational data, information and model structures and services. Over 250 organisations across Australia are required to provide water data and metadata to the Bureau, including federal, state and local governments, water storage management and hydroelectricity companies, rural and urban water utilities, and catchment management bodies. The data coverage includes the categories needed to assess and account for water resources at a range of scales. These categories are surface, groundwater and meteorological observations, water in storages, water restrictions, urban and irrigation water use and flows, information on rights, allocations and trades, and a limited suite of water quality parameters. These data are currently supplied to the Bureau via a file-based delivery system at various frequencies from annual to daily or finer, and contain observations taken at periods from minutes to monthly or coarser. One of the primary keys to better data access and utilisation is better data organisation, including content and markup standards. As a significant step on the path to standards for water data description, the Bureau has developed a Water Data Transfer Format (WDTF) for transmission of a variety of water data categories, including site metadata. WDTF is adapted from the OGC’s observation and sampling-features standard. The WDTF XML schema is compatible with the OGC's Web Feature Service (WFS) interchange standard, and conforms to GML Simple Features profile (GML-SF) level 1, emphasising the importance of standards in data exchange. In the longer term we are also

  17. Cost Optimization of Water Resources in Pernambuco, Brazil: Valuing Future Infrastructure and Climate Forecasts

    Science.gov (United States)

    Kumar, Ipsita; Josset, Laureline; Lall, Upmanu; Cavalcanti e Silva, Erik; Cordeiro Possas, José Marcelo; Cauás Asfora, Marcelo

    2017-04-01

    Optimal management of water resources is paramount in semi-arid regions to limit strains on the society and economy due to limited water availability. This problem is likely to become even more recurrent as droughts are projected to intensify in the coming years, causing increasing stresses to the water supply in the concerned areas. The state of Pernambuco, in the Northeast Brazil is one such case, where one of the largest reservoir, Jucazinho, has been at approximately 1% capacity throughout 2016, making infrastructural challenges in the region very real. To ease some of the infrastructural stresses and reduce vulnerabilities of the water system, a new source of water from Rio São Francisco is currently under development. Till its development, water trucks have been regularly mandated to cover water deficits, but at a much higher cost, thus endangering the financial sustainability of the region. In this paper, we propose to evaluate the sustainability of the considered water system by formulating an optimization problem and determine the optimal operations to be conducted. We start with a comparative study of the current and future infrastructures capabilities to face various climate. We show that while the Rio Sao Francisco project mitigates the problems, both implementations do not prevent failure and require the reliance on water trucks during prolonged droughts. We also study the cost associated with the provision of water to the municipalities for several streamflow forecasts. In particular, we investigate the value of climate predictions to adapt operational decisions by comparing the results with a fixed policy derived from historical data. We show that the use of climate information permits the reduction of the water deficit and reduces overall operational costs. We conclude with a discussion on the potential of the approach to evaluate future infrastructure developments. This study is funded by the Inter-American Development Bank (IADB), and in

  18. Forecasting the demand on solar water heating systems and their energy savings potential during the period 2001-2005 in Jordan

    International Nuclear Information System (INIS)

    Kablan, M.M.

    2003-01-01

    Jordan is an example of a developing country that depends almost exclusively on imported oil. Luckily, Jordan is blessed with good solar energy resources. However, only 24% of Jordanian families are installing solar water heating systems (SWHS). The objective of this research is to forecast the yearly demand on SWHS by the household sector during the period 2001-2005 and to compute the potential energy savings throughout the investigated period due to the use of SWHS. It is found that the net energy collected over the entire investigated period is about 1454.4 million kW h. In addition, the capital savings over the entire investigated period is estimated to be 46.28 million US$ if SWHS are used to heat water instead of the commonly used LPG gas cookers. The results of the research may assist decision makers in the energy sector to implement more comprehensive plans that encourage more families to install SWHS and save on imported oil

  19. Three-dimensional data assimilation and reanalysis of radiation belt electrons: Observations over two solar cycles, and operational forecasting.

    Science.gov (United States)

    Kellerman, A. C.; Shprits, Y.; Kondrashov, D. A.; Podladchikova, T.; Drozdov, A.; Subbotin, D.; Makarevich, R. A.; Donovan, E.; Nagai, T.

    2015-12-01

    Understanding of the dynamics in Earth's radiation belts is critical to accurate modeling and forecasting of space weather conditions, both which are important for design, and protection of our space-borne assets. In the current study, we utilize the Versatile Electron Radiation Belt (VERB) code, multi-spacecraft measurements, and a split-operator Kalman filter to recontructe the global state of the radiation belt system in the CRRES era and the current era. The reanalysis has revealed a never before seen 4-belt structure in the radiation belts during the March 1991 superstorm, and highlights several important aspects in regards to the the competition between the source, acceleration, loss, and transport of particles. In addition to the above, performing reanalysis in adiabatic coordinates relies on specification of the Earth's magnetic field, and associated observational, and model errors. We determine the observational errors for the Kalman filter directly from cross-spacecraft phase-space density (PSD) conjunctions, and obtain the error in VERB by comparison with reanalysis over a long time period. Specification of errors associated with several magnetic field models provides an important insight into the applicability of such models for radiation belt research. The comparison of CRRES area reanalysis with Van Allen Probe era reanalysis allows us to perform a global comparison of the dynamics of the radiation belts during different parts of the solar cycle and during different solar cycles. The data assimilative model is presently used to perform operational forecasts of the radiation belts (http://rbm.epss.ucla.edu/realtime-forecast/).

  20. Evaluation of the performance of a meso-scale NWP model to forecast solar irradiance on Reunion Island for photovoltaic power applications

    Science.gov (United States)

    Kalecinski, Natacha; Haeffelin, Martial; Badosa, Jordi; Periard, Christophe

    2013-04-01

    Solar photovoltaic power is a predominant source of electrical power on Reunion Island, regularly providing near 30% of electrical power demand for a few hours per day. However solar power on Reunion Island is strongly modulated by clouds in small temporal and spatial scales. Today regional regulations require that new solar photovoltaic plants be combined with storage systems to reduce electrical power fluctuations on the grid. Hence cloud and solar irradiance forecasting becomes an important tool to help optimize the operation of new solar photovoltaic plants on Reunion Island. Reunion Island, located in the South West of the Indian Ocean, is exposed to persistent trade winds, most of all in winter. In summer, the southward motion of the ITCZ brings atmospheric instabilities on the island and weakens trade winds. This context together with the complex topography of Reunion Island, which is about 60 km wide, with two high summits (3070 and 2512 m) connected by a 1500 m plateau, makes cloudiness very heterogeneous. High cloudiness variability is found between mountain and coastal areas and between the windward, leeward and lateral regions defined with respect to the synoptic wind direction. A detailed study of local dynamics variability is necessary to better understand cloud life cycles around the island. In the presented work, our approach to explore the short-term solar irradiance forecast at local scales is to use the deterministic output from a meso-scale numerical weather prediction (NWP) model, AROME, developed by Meteo France. To start we evaluate the performance of the deterministic forecast from AROME by using meteorological measurements from 21 meteorological ground stations widely spread around the island (and with altitudes from 8 to 2245 m). Ground measurements include solar irradiation, wind speed and direction, relative humidity, air temperature, precipitation and pressure. Secondly we study in the model the local dynamics and thermodynamics that

  1. SERI Solar Radiation Resource Assessment Project: Fiscal Year 1990 Annual Progress Report

    Energy Technology Data Exchange (ETDEWEB)

    Riordan, C; Maxwell, E; Stoffel, T; Rymes, M; Wilcox, S

    1991-07-01

    The purpose of the Solar Radiation Resource Project is to help meet the needs of the public, government, industry, and utilities for solar radiation data, models, and assessments as required to develop, design, deploy, and operate solar energy conversion systems. The project scientists produce information on the spatial (geographic), temporal (hourly, daily, and seasonal), and spectral (wavelength distribution) variability of solar radiation at different locations in the United States. Resources committed to the project in FY 1990 supported about four staff members, including part-time administrative support. With these resources, the staff must concentrate on solar radiation resource assessment in the United States; funds do not allow for significant efforts to respond to a common need for improved worldwide data. 34 refs., 21 figs., 6 tabs.

  2. Numerical Forecasting Experiment of the Wave Energy Resource in the China Sea

    Directory of Open Access Journals (Sweden)

    Chong Wei Zheng

    2016-01-01

    Full Text Available The short-term forecasting of wave energy is important to provide guidance for the electric power operation and power transmission system and to enhance the efficiency of energy capture and conversion. This study produced a numerical forecasting experiment of the China Sea wave energy using WAVEWATCH-III (WW3, the latest version 4.18 wave model driven by T213 (WW3-T213 and T639 (WW3-T639 wind data separately. Then the WW3-T213 and WW3-T639 were verified and compared to build a short-term wave energy forecasting structure suited for the China Sea. Considering the value of wave power density (WPD, “wave energy rose,” daily and weekly total storage and effective storage of wave energy, this study also designed a series of short-term wave energy forecasting productions. Results show that both the WW3-T213 and WW3-T639 exhibit a good skill on the numerical forecasting of the China Sea WPD, while the result of WW3-T639 is much better. Judging from WPD and daily and weekly total storage and effective storage of wave energy, great wave energy caused by cold airs was found. As there are relatively frequent cold airs in winter, early spring, and later autumn in the China Sea and the surrounding waters, abundant wave energy ensues.

  3. Analysis of resource potential for China’s unconventional gas and forecast for its long-term production growth

    International Nuclear Information System (INIS)

    Wang, Jianliang; Mohr, Steve; Feng, Lianyong; Liu, Huihui; Tverberg, Gail E.

    2016-01-01

    China is vigorously promoting the development of its unconventional gas resources because natural gas is viewed as a lower-carbon energy source and because China has relatively little conventional natural gas supply. In this paper, we first evaluate how much unconventional gas might be available based on an analysis of technically recoverable resources for three types of unconventional gas resources: shale gas, coalbed methane and tight gas. We then develop three alternative scenarios of how this extraction might proceed, using the Geologic Resources Supply Demand Model. Based on our analysis, the medium scenario, which we would consider to be our best estimate, shows a resource peak of 176.1 billion cubic meters (bcm) in 2068. Depending on economic conditions and advance in extraction techniques, production could vary greatly from this. If economic conditions are adverse, unconventional natural gas production could perhaps be as low as 70.1 bcm, peaking in 2021. Under the extremely optimistic assumption that all of the resources that appear to be technologically available can actually be recovered, unconventional production could amount to as much as 469.7 bcm, with peak production in 2069. Even if this high scenario is achieved, China’s total gas production will only be sufficient to meet China’s lowest demand forecast. If production instead matches our best estimate, significant amounts of natural gas imports are likely to be needed. - Highlights: • A comprehensive investigation on China’s unconventional gas resources is presented. • China’s unconventional gas production is forecast under different scenarios. • Unconventional gas production will increase rapidly in high scenario. • Achieving the projected production in high scenario faces many challenges. • The increase of China’s unconventional gas production cannot solve its gas shortage.

  4. A GLOBAL ASSESSMENT OF SOLAR ENERGY RESOURCES: NASA's Prediction of Worldwide Energy Resources (POWER) Project

    Science.gov (United States)

    Zhang, T.; Stackhouse, P. W., Jr.; Chandler, W.; Hoell, J. M.; Westberg, D.; Whitlock, C. H.

    2010-12-01

    NASA's POWER project, or the Prediction of the Worldwide Energy Resources project, synthesizes and analyzes data on a global scale. The products of the project find valuable applications in the solar and wind energy sectors of the renewable energy industries. The primary source data for the POWER project are NASA's World Climate Research Project (WCRP)/Global Energy and Water cycle Experiment (GEWEX) Surface Radiation Budget (SRB) project (Release 3.0) and the Global Modeling and Assimilation Office (GMAO) Goddard Earth Observing System (GEOS) assimilation model (V 4.0.3). Users of the POWER products access the data through NASA's Surface meteorology and Solar Energy (SSE, Version 6.0) website (http://power.larc.nasa.gov). Over 200 parameters are available to the users. The spatial resolution is 1 degree by 1 degree now and will be finer later. The data covers from July 1983 to December 2007, a time-span of 24.5 years, and are provided as 3-hourly, daily and monthly means. As of now, there have been over 18 million web hits and over 4 million data file downloads. The POWER products have been systematically validated against ground-based measurements, and in particular, data from the Baseline Surface Radiation Network (BSRN) archive, and also against the National Solar Radiation Data Base (NSRDB). Parameters such as minimum, maximum, daily mean temperature and dew points, relative humidity and surface pressure are validated against the National Climate Data Center (NCDC) data. SSE feeds data directly into Decision Support Systems including RETScreen International clean energy project analysis software that is written in 36 languages and has greater than 260,000 users worldwide.

  5. National Forecast Charts

    Science.gov (United States)

    code. Press enter or select the go button to submit request Local forecast by "City, St" or Prediction Center on Twitter NCEP Quarterly Newsletter WPC Home Analyses and Forecasts National Forecast to all federal, state, and local government web resources and services. National Forecast Charts

  6. Research on strategy and optimization method of PRT empty vehicles resource allocation based on traffic demand forecast

    Science.gov (United States)

    Xiang, Yu; Tao, Cheng

    2018-05-01

    During the operation of the personal rapid transit system(PRT), the empty vehicle resources is distributed unevenly because of different passenger demand. In order to maintain the balance between supply and demand, and to meet the passenger needs of the ride, PRT empty vehicle resource allocation model is constructed based on the future demand forecasted by historical demand in this paper. The improved genetic algorithm is implied in distribution of the empty vehicle which can reduce the customers waiting time and improve the operation efficiency of the PRT system so that all passengers can take the PRT vehicles in the shortest time. The experimental result shows that the improved genetic algorithm can allocate the empty vehicle from the system level optimally, and realize the distribution of the empty vehicle resources reasonably in the system.

  7. Comparative evaluation of solar, fission, fusion, and fossil energy resources. Part 1: Solar energy

    Science.gov (United States)

    Williams, J. R.

    1974-01-01

    The utilization of solar energy to meet the energy needs of the U.S. is discussed. Topics discussed include: availability of solar energy, solar energy collectors, heating for houses and buildings, solar water heater, electric power generation, and ocean thermal power.

  8. A 24-h forecast of solar irradiance using artificial neural network: Application for performance prediction of a grid-connected PV plant at Trieste, Italy

    Energy Technology Data Exchange (ETDEWEB)

    Mellit, Adel [Department of Electronics, Faculty of Sciences and Technology, LAMEL, Jijel University, Ouled-aissa, P.O. Box 98, Jijel 18000 (Algeria); Pavan, Alessandro Massi [Department of Materials and Natural Resources, University of Trieste Via A. Valerio, 2 - 34127 Trieste (Italy)

    2010-05-15

    Forecasting of solar irradiance is in general significant for planning the operations of power plants which convert renewable energies into electricity. In particular, the possibility to predict the solar irradiance (up to 24 h or even more) can became - with reference to the Grid Connected Photovoltaic Plants (GCPV) - fundamental in making power dispatching plans and - with reference to stand alone and hybrid systems - also a useful reference for improving the control algorithms of charge controllers. In this paper, a practical method for solar irradiance forecast using artificial neural network (ANN) is presented. The proposed Multilayer Perceptron MLP-model makes it possible to forecast the solar irradiance on a base of 24 h using the present values of the mean daily solar irradiance and air temperature. An experimental database of solar irradiance and air temperature data (from July 1st 2008 to May 23rd 2009 and from November 23rd 2009 to January 24th 2010) has been used. The database has been collected in Trieste (latitude 45 40'N, longitude 13 46'E), Italy. In order to check the generalization capability of the MLP-forecaster, a K-fold cross-validation was carried out. The results indicate that the proposed model performs well, while the correlation coefficient is in the range 98-99% for sunny days and 94-96% for cloudy days. As an application, the comparison between the forecasted one and the energy produced by the GCPV plant installed on the rooftop of the municipality of Trieste shows the goodness of the proposed model. (author)

  9. Forecasting the Optimal Factors of Formation of the Population Savings as the Basis for Investment Resources of the Regional Economy

    Directory of Open Access Journals (Sweden)

    Odintsova Tetiana M.

    2017-04-01

    Full Text Available The article is aimed at studying the optimal factors of formation of the population savings as the basis for investment resources of the regional economy. A factorial (nonlinear correlative-regression analysis of the formation of savings of the population of Ukraine was completed. On its basis a forecast of the optimal structure and volumes of formation of the population incomes was carried out taking into consideration impact of fundamental factors on these incomes. Such approach provides to identify the marginal volumes of tax burden, population savings, and capital investments, directed to economic growth.

  10. Real-time energy resources scheduling considering short-term and very short-term wind forecast

    Energy Technology Data Exchange (ETDEWEB)

    Silva, Marco; Sousa, Tiago; Morais, Hugo; Vale, Zita [Polytechnic of Porto (Portugal). GECAD - Knowledge Engineering and Decision Support Research Center

    2012-07-01

    This paper proposes an energy resources management methodology based on three distinct time horizons: day-ahead scheduling, hour-ahead scheduling, and real-time scheduling. In each scheduling process the update of generation and consumption operation and of the storage and electric vehicles storage status are used. Besides the new operation conditions, the most accurate forecast values of wind generation and of consumption using results of short-term and very short-term methods are used. A case study considering a distribution network with intensive use of distributed generation and electric vehicles is presented. (orig.)

  11. A nonlinear support vector machine model with hard penalty function based on glowworm swarm optimization for forecasting daily global solar radiation

    International Nuclear Information System (INIS)

    Jiang, He; Dong, Yao

    2016-01-01

    Highlights: • Eclat data mining algorithm is used to determine the possible predictors. • Support vector machine is converted into a ridge regularization problem. • Hard penalty selects the number of radial basis functions to simply the structure. • Glowworm swarm optimization is utilized to determine the optimal parameters. - Abstract: For a portion of the power which is generated by grid connected photovoltaic installations, an effective solar irradiation forecasting approach must be crucial to ensure the quality and the security of power grid. This paper develops and investigates a novel model to forecast 30 daily global solar radiation at four given locations of the United States. Eclat data mining algorithm is first presented to discover association rules between solar radiation and several meteorological factors laying a theoretical foundation for these correlative factors as input vectors. An effective and innovative intelligent optimization model based on nonlinear support vector machine and hard penalty function is proposed to forecast solar radiation by converting support vector machine into a regularization problem with ridge penalty, adding a hard penalty function to select the number of radial basis functions, and using glowworm swarm optimization algorithm to determine the optimal parameters of the model. In order to illustrate our validity of the proposed method, the datasets at four sites of the United States are split to into training data and test data, separately. The experiment results reveal that the proposed model delivers the best forecasting performances comparing with other competitors.

  12. Solar Resources for Universities | State, Local, and Tribal Governments |

    Science.gov (United States)

    stakeholders to develop deployment solutions, and empower decision makers. Text version To assist organizations Federal Tax Incentives for Battery Storage Systems Non-Power Purchase Agreement (PPA) Options to Financing Power Purchase Agreements for Solar Deployment at Universities Writing Solar Requests for Proposals

  13. Solar energy resources not accounted in Brazilian National Energy Balance

    Energy Technology Data Exchange (ETDEWEB)

    Pinheiro, Paulo Cesar da Costa [Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, MG (Brazil). Dept. de Engenharia Mecanica], Emails: pinheiro@netuno.Lcc.ufmg.br, pinheiro@demec.ufmg.br

    2009-07-01

    The main development vector of a society is the energy. The solar energy is the main energy source on the planet earth. Brazil is a tropical country, and the incident solar energy on its soil (15 trillion MWh/year) is 20,000 times its annual oil production. Several uses of solar energy are part of our lives in a so natural way that it despised in the consumption and use energy balance. In Brazil, solar energy is used directly in many activities and not accounted for in Energy Balance (BEN 2007), consisting of a virtual power generation. This work aims to make a preliminary assessment of solar energy used in different segments of the Brazilian economy. (author)

  14. Renewable energy and resource curse on the possible consequences of solar energy in North Africa

    NARCIS (Netherlands)

    Bae, Yuh Jin

    2013-01-01

    The main aim of this thesis is to project whether the five North African countries (Algeria, Egypt, Libya, Morocco, and Tunisa) have the potentials to suffer from a solar energy curse. Under the assumption that a solar energy curse will be similar to the current resource curse, the combination of

  15. COMPLEX MAPPING OF ENERGY RESOURCES FOR ALLOCATION OF SOLAR AND WIND ENERGY OBJECTS

    Directory of Open Access Journals (Sweden)

    B. A. Novakovskiy

    2016-01-01

    Full Text Available The paper presents developed methodology of solar and wind energy resources complex mapping at the regional level, taking into account the environmental and socio-economic factors affecting the placement of renewable energy facilities. Methodology provides a reasonable search and allocation of areas, the most promising for the placement of wind and solar power plants.

  16. Towards a Solid Foundation of Using Remotely Sensed Solar-Induced Chlorophyll Fluorescence for Crop Monitoring and Yield Forecast

    Science.gov (United States)

    Chen, Y.; Sun, Y.; You, L.; Liu, Y.

    2017-12-01

    The growing demand for food production due to population increase coupled with high vulnerability to volatile environmental changes poses a paramount challenge for mankind in the coming century. Real-time crop monitoring and yield forecasting must be a key part of any solution to this challenge as these activities provide vital information needed for effective and efficient crop management and for decision making. However, traditional methods of crop growth monitoring (e.g., remotely sensed vegetation indices) do not directly relate to the most important function of plants - photosynthesis and therefore crop yield. The recent advance in the satellite remote sensing of Solar-Induced chlorophyll Fluorescence (SIF), an integrative photosynthetic signal from molecular origin and a direct measure of plant functions holds great promise for real-time monitoring of crop growth conditions and forecasting yields. In this study, we use satellite measurements of SIF from both the Global Ozone Monitoring Experiment-2 (GOME-2) onboard MetOp-A and the Orbiting Carbon Observatory-2 (OCO-2) satellites to estimate crop yield using both process-based and statistical models. We find that SIF-based crop yield well correlates with the global yield product Spatial Production Allocation Model (SPAM) derived from ground surveys for all major crops including maize, soybean, wheat, sorghum, and rice. The potential and challenges of using upcoming SIF satellite missions for crop monitoring and prediction will also be discussed.

  17. Space plasma observations - observations of solar-terrestrial environment. Space Weather Forecast

    International Nuclear Information System (INIS)

    Sagawa, Eiichi; Akioka, Maki

    1996-01-01

    The space environment becomes more important than ever before because of the expansion in the utilization of near-earth space and the increase in the vulnerability of large scale systems on the ground such as electrical power grids. The concept of the Space Weather Forecast program emerged from the accumulation of understanding on basic physical processes and from our activities as one of the regional warning centers of the international network of space environment services. (author)

  18. New technique for global solar radiation forecasting by simulated annealing and genetic algorithms using

    International Nuclear Information System (INIS)

    Tolabi, H.B.; Ayob, S.M.

    2014-01-01

    In this paper, a novel approach based on simulated annealing algorithm as a meta-heuristic method is implemented in MATLAB software to estimate the monthly average daily global solar radiation on a horizontal surface for six different climate cities of Iran. A search method based on genetic algorithm is applied to accelerate problem solving. Results show that simulated annealing based on genetic algorithm search is a suitable method to find the global solar radiation. (author)

  19. Solar Resources for Local Governments | State, Local, and Tribal

    Science.gov (United States)

    Validation, and Permitting April: Project Financing, Policy, and Incentives May: Solar Procurement (Requests Module 3 Presentation Module 4: Project Financing, Policy, and Incentives Introduction Text version Clarification February: Screening and Identifying PV Projects March: Detailed Site Evaluation, Project

  20. A new solar power output prediction based on hybrid forecast engine and decomposition model.

    Science.gov (United States)

    Zhang, Weijiang; Dang, Hongshe; Simoes, Rolando

    2018-06-12

    Regarding to the growing trend of photovoltaic (PV) energy as a clean energy source in electrical networks and its uncertain nature, PV energy prediction has been proposed by researchers in recent decades. This problem is directly effects on operation in power network while, due to high volatility of this signal, an accurate prediction model is demanded. A new prediction model based on Hilbert Huang transform (HHT) and integration of improved empirical mode decomposition (IEMD) with feature selection and forecast engine is presented in this paper. The proposed approach is divided into three main sections. In the first section, the signal is decomposed by the proposed IEMD as an accurate decomposition tool. To increase the accuracy of the proposed method, a new interpolation method has been used instead of cubic spline curve (CSC) fitting in EMD. Then the obtained output is entered into the new feature selection procedure to choose the best candidate inputs. Finally, the signal is predicted by a hybrid forecast engine composed of support vector regression (SVR) based on an intelligent algorithm. The effectiveness of the proposed approach has been verified over a number of real-world engineering test cases in comparison with other well-known models. The obtained results prove the validity of the proposed method. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

  1. A Short-term ESPERTA-based Forecast Tool for Moderate-to-extreme Solar Proton Events

    Science.gov (United States)

    Laurenza, M.; Alberti, T.; Cliver, E. W.

    2018-04-01

    The ESPERTA (Empirical model for Solar Proton Event Real Time Alert) forecast tool has a Probability of Detection (POD) of 63% for all >10 MeV events with proton peak intensity ≥10 pfu (i.e., ≥S1 events, S1 referring to minor storms on the NOAA Solar Radiation Storms scale), from 1995 to 2014 with a false alarm rate (FAR) of 38% and a median (minimum) warning time (WT) of ∼4.8 (0.4) hr. The NOAA space weather scale includes four additional categories: moderate (S2), strong (S3), severe (S4), and extreme (S5). As S1 events have only minor impacts on HF radio propagation in the polar regions, the effective threshold for significant space radiation effects appears to be the S2 level (100 pfu), above which both biological and space operation impacts are observed along with increased effects on HF propagation in the polar regions. We modified the ESPERTA model to predict ≥S2 events and obtained a POD of 75% (41/55) and an FAR of 24% (13/54) for the 1995–2014 interval with a median (minimum) WT of ∼1.7 (0.2) hr based on predictions made at the time of the S1 threshold crossing. The improved performance of ESPERTA for ≥S2 events is a reflection of the big flare syndrome, which postulates that the measures of the various manifestations of eruptive solar flares increase as one considers increasingly larger events.

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

    KAUST Repository

    Dehwah, Ahmad H.; Elmetennani, Shahrazed; Claudel, Christian

    2017-01-01

    -WCMA) to estimate and predict the variations of the solar power in a wireless sensor network. The presented approach combines the information from the real-time measurement data and a set of stored profiles representing the energy patterns in the WSNs location

  3. A Comparative Verification of Forecasts from Two Operational Solar Wind Models (Postprint)

    Science.gov (United States)

    2012-02-08

    much confidence to place on predicted parameters. Cost /benefit information is provided to administrators who decide to sustain or replace existing...magnetic field magnitude and three components of the magnetic field vector in the geocentric solar magnetospheric (GSM) coordinate system at each hour of

  4. Using Forecasting to Predict Long-Term Resource Utilization for Web Services

    Science.gov (United States)

    Yoas, Daniel W.

    2013-01-01

    Researchers have spent years understanding resource utilization to improve scheduling, load balancing, and system management through short-term prediction of resource utilization. Early research focused primarily on single operating systems; later, interest shifted to distributed systems and, finally, into web services. In each case researchers…

  5. A Hierarchical Approach Using Machine Learning Methods in Solar Photovoltaic Energy Production Forecasting

    OpenAIRE

    Zhaoxuan Li; SM Mahbobur Rahman; Rolando Vega; Bing Dong

    2016-01-01

    We evaluate and compare two common methods, artificial neural networks (ANN) and support vector regression (SVR), for predicting energy productions from a solar photovoltaic (PV) system in Florida 15 min, 1 h and 24 h ahead of time. A hierarchical approach is proposed based on the machine learning algorithms tested. The production data used in this work corresponds to 15 min averaged power measurements collected from 2014. The accuracy of the model is determined using computing error statisti...

  6. Solar resources and power potential mapping in Vietnam using satellite-derived and GIS-based information

    International Nuclear Information System (INIS)

    Polo, J.; Bernardos, A.; Navarro, A.A.; Fernandez-Peruchena, C.M.; Ramírez, L.; Guisado, María V.; Martínez, S.

    2015-01-01

    Highlights: • Satellite-based, reanalysis data and measurements are combined for solar mapping. • Plant output modeling for PV and CSP results in simple expressions of solar potential. • Solar resource, solar potential are used in a GIS for determine technical solar potential. • Solar resource and potential maps of Vietnam are presented. - Abstract: The present paper presents maps of the solar resources in Vietnam and of the solar potential for concentrating solar power (CSP) and for grid-connected photovoltaic (PV) technology. The mapping of solar radiation components has been calculated from satellite-derived data combined with solar radiation derived from sunshine duration and other additional sources of information based on reanalysis for several atmospheric and meteorological parameters involved. Two scenarios have been selected for the study of the solar potential: CSP Parabolic Trough of 50 MWe and grid-connected Flat Plate PV plant of around 1 MWe. For each selected scenario plant performance simulations have been computed for developing simple expressions that allow the estimation of the solar potential from the annual solar irradiation and the latitude of every site in Vietnam. Finally, Geographic Information Systems (GIS) have been used for combining the solar potential with the land availability according each scenario to deliver the technical solar potential maps of Vietnam

  7. Integrated Flood Forecast and Virtual Dam Operation System for Water Resources and Flood Risk Management

    Science.gov (United States)

    Shibuo, Yoshihiro; Ikoma, Eiji; Lawford, Peter; Oyanagi, Misa; Kanauchi, Shizu; Koudelova, Petra; Kitsuregawa, Masaru; Koike, Toshio

    2014-05-01

    While availability of hydrological- and hydrometeorological data shows growing tendency and advanced modeling techniques are emerging, such newly available data and advanced models may not always be applied in the field of decision-making. In this study we present an integrated system of ensemble streamflow forecast (ESP) and virtual dam simulator, which is designed to support river and dam manager's decision making. The system consists of three main functions: real time hydrological model, ESP model, and dam simulator model. In the real time model, the system simulates current condition of river basins, such as soil moisture and river discharges, using LSM coupled distributed hydrological model. The ESP model takes initial condition from the real time model's output and generates ESP, based on numerical weather prediction. The dam simulator model provides virtual dam operation and users can experience impact of dam control on remaining reservoir volume and downstream flood under the anticipated flood forecast. Thus the river and dam managers shall be able to evaluate benefit of priori dam release and flood risk reduction at the same time, on real time basis. Furthermore the system has been developed under the concept of data and models integration, and it is coupled with Data Integration and Analysis System (DIAS) - a Japanese national project for integrating and analyzing massive amount of observational and model data. Therefore it has advantage in direct use of miscellaneous data from point/radar-derived observation, numerical weather prediction output, to satellite imagery stored in data archive. Output of the system is accessible over the web interface, making information available with relative ease, e.g. from ordinary PC to mobile devices. We have been applying the system to the Upper Tone region, located northwest from Tokyo metropolitan area, and we show application example of the system in recent flood events caused by typhoons.

  8. Ultra-Portable Solar-Powered 3D Printers for Onsite Manufacturing of Medical Resources.

    Science.gov (United States)

    Wong, Julielynn Y

    2015-09-01

    The first space-based fused deposition modeling (FDM) 3D printer is powered by solar photovoltaics. This study seeks to demonstrate the feasibility of using solar energy to power a FDM 3D printer to manufacture medical resources at the Mars Desert Research Station and to design an ultra-portable solar-powered 3D printer for off-grid environments. Six solar panels in a 3×2 configuration, a voltage regulator/capacitor improvised from a power adapter, and two 12V batteries in series were connected to power a FDM 3D printer. Three designs were printed onsite and evaluated by experts post analogue mission. A solar-powered 3D printer composed of off-the-shelf components was designed to be transported in airline carry-on luggage. During the analogue mission, the solar-powered printer could only be operated for solar-powered 3D printer was designed that could print an estimated 16 dental tools or 8 mallet finger splints or 7 scalpel handles on one fully charged 12V 150Wh battery with a 110V AC converter. It is feasible to use solar energy to power a 3D printer to manufacture functional and personalized medical resources at a Mars analogue research station. Based on these findings, a solar-powered suitcase 3D printing system containing solar panels, 12V battery with charge controller and AC inverter, and back-up solar charge controller and inverter was designed for transport to and use in off-grid communities.

  9. Forecasting Resource as a Method of Increasing the Security of Technical Devices

    Science.gov (United States)

    Cherepanov, Anatoly P.; Lyapustin, Pavel K.

    2017-10-01

    The article shows a method of increasing the safe operation of technical devices at various stages of the life cycle according to the proposed classification parameters of the resource by applying the model of resource prediction. The model takes into account the presence of defects, the rate of corrosion and corrosion resistance of the material, the volume of technical diagnosis, the degree of risk in case of failure or damage of technical devices. The article shows the application of the model resource of the technical device from the manufacture to the end of its service life.

  10. Forecasting the impact of an 1859-caliber superstorm on geosynchronous Earth-orbiting satellites: Transponder resources

    Science.gov (United States)

    Odenwald, Sten F.; Green, James L.

    2007-06-01

    We calculate the economic impact on the existing geosynchronous Earth-orbiting satellite population of an 1859-caliber superstorm event were it to occur between 2008 and 2018 during the next solar activity cycle. From a detailed model for transponder capacity and leasing, we have investigated the total revenue loss over the entire solar cycle, as a function of superstorm onset year and intensity. Our Monte Carlo simulations of 1000 possible superstorms, of varying intensity and onset year, suggest that the minimum revenue loss could be of the order of 30 billion. The losses would be larger than this if more that 20 satellites are disabled, if future launch rates do not keep up with the expected rate of retirements, or if the number of spare transponders falls below ˜30%. Consequently, revenue losses can be significantly reduced below 30 billion if the current satellite population undergoes net growth beyond 300 units during Solar Cycle 24 and a larger margin of unused transponders is maintained.

  11. Prior Flaring as a Complement to Free Magnetic Energy for Forecasting Solar Eruptions

    Science.gov (United States)

    Falconer, David A.; Moore, Ronald L.; Barghouty, Abdulnasser F.; Khazanov, Igor

    2012-01-01

    From a large database of (1) 40,000 SOHO/MDI line-of-sight magnetograms covering the passage of 1,300 sunspot active regions across the 30 deg radius central disk of the Sun, (2) a proxy of each active region's free magnetic energy measured from each of the active region's central-disk-passage magnetograms, and (3) each active region's full-disk-passage history of production of major flares and fast coronal mass ejections (CMEs), we find new statistical evidence that (1) there are aspects of an active region's magnetic field other than the free energy that are strong determinants of the active region's productivity of major flares and fast CMEs in the coming few days, (2) an active region's recent productivity of major flares, in addition to reflecting the amount of free energy in the active region, also reflects these other determinants of coming productivity of major eruptions, and (3) consequently, the knowledge of whether an active region has recently had a major flare, used in combination with the active region's free-energy proxy measured from a magnetogram, can greatly alter the forecast chance that the active region will have a major eruption in the next few days after the time of the magnetogram. The active-region magnetic conditions that, in addition to the free energy, are reflected by recent major flaring are presumably the complexity and evolution of the field.

  12. PRIOR FLARING AS A COMPLEMENT TO FREE MAGNETIC ENERGY FOR FORECASTING SOLAR ERUPTIONS

    International Nuclear Information System (INIS)

    Falconer, David A.; Moore, Ronald L.; Barghouty, Abdulnasser F.; Khazanov, Igor

    2012-01-01

    From a large database of (1) 40,000 SOHO/MDI line-of-sight magnetograms covering the passage of 1300 sunspot active regions across the 30° radius central disk of the Sun, (2) a proxy of each active region's free magnetic energy measured from each of the active region's central-disk-passage magnetograms, and (3) each active region's full-disk-passage history of production of major flares and fast coronal mass ejections (CMEs), we find new statistical evidence that (1) there are aspects of an active region's magnetic field other than the free energy that are strong determinants of the active region's productivity of major flares and fast CMEs in the coming few days; (2) an active region's recent productivity of major flares, in addition to reflecting the amount of free energy in the active region, also reflects these other determinants of coming productivity of major eruptions; and (3) consequently, the knowledge of whether an active region has recently had a major flare, used in combination with the active region's free-energy proxy measured from a magnetogram, can greatly alter the forecast chance that the active region will have a major eruption in the next few days after the time of the magnetogram. The active-region magnetic conditions that, in addition to the free energy, are reflected by recent major flaring are presumably the complexity and evolution of the field.

  13. Residential heating costs: A comparison of geothermal solar and conventional resources

    Science.gov (United States)

    Bloomster, C. H.; Garrett-Price, B. A.; Fassbender, L. L.

    1980-08-01

    The costs of residential heating throughout the United States using conventional, solar, and geothermal energy were determined under current and projected conditions. These costs are very sensitive to location, being dependent on the local prices of conventional energy supplies, local solar insolation, climate, and the proximity and temperature of potential geothermal resources. The sharp price increases in imported fuels during 1979 and the planned decontrol of domestic oil and natural gas prices have set the stage for geothermal and solar market penetration in the 1980's.

  14. Space Weather Forecasting at IZMIRAN

    Science.gov (United States)

    Gaidash, S. P.; Belov, A. V.; Abunina, M. A.; Abunin, A. A.

    2017-12-01

    Since 1998, the Institute of Terrestrial Magnetism, Ionosphere, and Radio Wave Propagation (IZMIRAN) has had an operating heliogeophysical service—the Center for Space Weather Forecasts. This center transfers the results of basic research in solar-terrestrial physics into daily forecasting of various space weather parameters for various lead times. The forecasts are promptly available to interested consumers. This article describes the center and the main types of forecasts it provides: solar and geomagnetic activity, magnetospheric electron fluxes, and probabilities of proton increases. The challenges associated with the forecasting of effects of coronal mass ejections and coronal holes are discussed. Verification data are provided for the center's forecasts.

  15. Principles of solar engineering

    CERN Document Server

    Goswami, D Yogi

    2015-01-01

    Introduction to Solar Energy ConversionGlobal Energy Needs and ResourcesSolar EnergyEnergy StorageEconomics of Solar SystemsSummary of RE ResourcesForecast of Future Energy MixReferencesFundamentals of Solar RadiationThe Physics of the Sun and Its Energy TransportThermal Radiation FundamentalsSun-Earth Geometric RelationshipSolar RadiationEstimation of Terrestrial Solar RadiationModels Based on Long-Term Measured Horizontal Solar RadiationMeasurement of Solar RadiationSolar Radiation Mapping Using Satellite DataReferencesSuggested ReadingsSolar Thermal CollectorsRadiative Properties and Characteristics of MaterialsFlat-Plate CollectorsTubular Solar Energy CollectorsExperimental Testing of CollectorsConcentrating Solar CollectorsParabolic Trough ConcentratorCompound-Curvature Solar ConcentratorsCentral Receiver CollectorFresnel Reflectors and LensesSolar Concentrator SummaryReferencesSuggested ReadingThermal Energy Storage and TransportThermal Energy StorageTypes of TESDesign of Storage SystemEnergy Transport ...

  16. Short-term solar irradiance forecasting and photovoltaic systems performance in a tropical climate in Singapore

    OpenAIRE

    Nobre, André Maia

    2015-01-01

    Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia Civil, Florianópolis, 2015. A humanidade usou e continua consumindo em grande quantidade os recursos não-renováveis do planeta como petróleo, gás natural e carvão mineral para suprir suas necessidades energéticas. Somente nas últimas duas décadas que outras fontes de energia renováveis, como a solar fotovoltaica e a eólica, passaram a se tornar relevantes na...

  17. Use of a Geothermal-Solar Hybrid Power Plant to Mitigate Declines in Geothermal Resource Productivity

    Energy Technology Data Exchange (ETDEWEB)

    Dan Wendt; Greg Mines

    2014-09-01

    Many, if not all, geothermal resources are subject to decreasing productivity manifested in the form of decreasing brine temperature, flow rate, or both during the life span of the associated power generation project. The impacts of resource productivity decline on power plant performance can be significant; a reduction in heat input to a power plant not only decreases the thermal energy available for conversion to electrical power, but also adversely impacts the power plant conversion efficiency. The reduction in power generation is directly correlated to a reduction in revenues from power sales. Further, projects with Power Purchase Agreement (PPA) contracts in place may be subject to significant economic penalties if power generation falls below the default level specified. A potential solution to restoring the performance of a power plant operating from a declining productivity geothermal resource involves the use of solar thermal energy to restore the thermal input to the geothermal power plant. There are numerous technical merits associated with a renewable geothermal-solar hybrid plant in which the two heat sources share a common power block. The geo-solar hybrid plant could provide a better match to typical electrical power demand profiles than a stand-alone geothermal plant. The hybrid plant could also eliminate the stand-alone concentrated solar power plant thermal storage requirement for operation during times of low or no solar insolation. This paper identifies hybrid plant configurations and economic conditions for which solar thermal retrofit of a geothermal power plant could improve project economics. The net present value of the concentrated solar thermal retrofit of an air-cooled binary geothermal plant is presented as functions of both solar collector array cost and electricity sales price.

  18. Passive Solar Driven Water Treatment of Contaminated Water Resources

    OpenAIRE

    Ahmed, Mubasher

    2016-01-01

    Master's thesis in Environmental technology Freshwater, being vital for mankind survival, has become a very serious concern for the public especially living in countries with limited water, energy and economic resources. Freshwater generation is an energy-intensive task particularly when fossil based fuels are required as energy source. However, environmental concerns and high energy costs have called for the alternative and renewable sources of energy like wind, hy...

  19. Inner solar system prospective energy and material resources

    CERN Document Server

    Zacny, Kris

    2015-01-01

    This book investigates Venus and Mercury prospective energy and material resources. It is a collection of topics related to exploration and utilization of these bodies. It presents past and future technologies and solutions to old problems that could become reality in our life time. The book therefore is a great source of condensed information for specialists interested in current and impending Venus and Mercury related activities and a good starting point for space researchers, inventors, technologists and potential investors.   Written for researchers, engineers, and businessmen interested in Venus and Mercury exploration and exploitation.

  20. About the National Forecast Chart

    Science.gov (United States)

    code. Press enter or select the go button to submit request Local forecast by "City, St" or Prediction Center on Twitter NCEP Quarterly Newsletter WPC Home Analyses and Forecasts National Forecast to all federal, state, and local government web resources and services. The National Forecast Charts

  1. Statistical forecast of seasonal discharge in Central Asia using observational records: development of a generic linear modelling tool for operational water resource management

    Directory of Open Access Journals (Sweden)

    H. Apel

    2018-04-01

    Full Text Available The semi-arid regions of Central Asia crucially depend on the water resources supplied by the mountainous areas of the Tien Shan and Pamir and Altai mountains. During the summer months the snow-melt- and glacier-melt-dominated river discharge originating in the mountains provides the main water resource available for agricultural production, but also for storage in reservoirs for energy generation during the winter months. Thus a reliable seasonal forecast of the water resources is crucial for sustainable management and planning of water resources. In fact, seasonal forecasts are mandatory tasks of all national hydro-meteorological services in the region. In order to support the operational seasonal forecast procedures of hydro-meteorological services, this study aims to develop a generic tool for deriving statistical forecast models of seasonal river discharge based solely on observational records. The generic model structure is kept as simple as possible in order to be driven by meteorological and hydrological data readily available at the hydro-meteorological services, and to be applicable for all catchments in the region. As snow melt dominates summer runoff, the main meteorological predictors for the forecast models are monthly values of winter precipitation and temperature, satellite-based snow cover data, and antecedent discharge. This basic predictor set was further extended by multi-monthly means of the individual predictors, as well as composites of the predictors. Forecast models are derived based on these predictors as linear combinations of up to four predictors. A user-selectable number of the best models is extracted automatically by the developed model fitting algorithm, which includes a test for robustness by a leave-one-out cross-validation. Based on the cross-validation the predictive uncertainty was quantified for every prediction model. Forecasts of the mean seasonal discharge of the period April to September are derived

  2. Statistical forecast of seasonal discharge in Central Asia using observational records: development of a generic linear modelling tool for operational water resource management

    Science.gov (United States)

    Apel, Heiko; Abdykerimova, Zharkinay; Agalhanova, Marina; Baimaganbetov, Azamat; Gavrilenko, Nadejda; Gerlitz, Lars; Kalashnikova, Olga; Unger-Shayesteh, Katy; Vorogushyn, Sergiy; Gafurov, Abror

    2018-04-01

    The semi-arid regions of Central Asia crucially depend on the water resources supplied by the mountainous areas of the Tien Shan and Pamir and Altai mountains. During the summer months the snow-melt- and glacier-melt-dominated river discharge originating in the mountains provides the main water resource available for agricultural production, but also for storage in reservoirs for energy generation during the winter months. Thus a reliable seasonal forecast of the water resources is crucial for sustainable management and planning of water resources. In fact, seasonal forecasts are mandatory tasks of all national hydro-meteorological services in the region. In order to support the operational seasonal forecast procedures of hydro-meteorological services, this study aims to develop a generic tool for deriving statistical forecast models of seasonal river discharge based solely on observational records. The generic model structure is kept as simple as possible in order to be driven by meteorological and hydrological data readily available at the hydro-meteorological services, and to be applicable for all catchments in the region. As snow melt dominates summer runoff, the main meteorological predictors for the forecast models are monthly values of winter precipitation and temperature, satellite-based snow cover data, and antecedent discharge. This basic predictor set was further extended by multi-monthly means of the individual predictors, as well as composites of the predictors. Forecast models are derived based on these predictors as linear combinations of up to four predictors. A user-selectable number of the best models is extracted automatically by the developed model fitting algorithm, which includes a test for robustness by a leave-one-out cross-validation. Based on the cross-validation the predictive uncertainty was quantified for every prediction model. Forecasts of the mean seasonal discharge of the period April to September are derived every month from

  3. Implications of applying solar industry best practice resource estimation on project financing

    International Nuclear Information System (INIS)

    Pacudan, Romeo

    2016-01-01

    Solar resource estimation risk is one of the main solar PV project risks that influences lender’s decision in providing financing and in determining the cost of capital. More recently, a number of measures have emerged to mitigate this risk. The study focuses on solar industry’s best practice energy resource estimation and assesses its financing implications to the 27 MWp solar PV project study in Brunei Darussalam. The best practice in resource estimation uses multiple data sources through the measure-correlate-predict (MCP) technique as compared with the standard practice that rely solely on modelled data source. The best practice case generates resource data with lower uncertainty and yields superior high-confidence energy production estimate than the standard practice case. Using project financial parameters in Brunei Darussalam for project financing and adopting the international debt-service coverage ratio (DSCR) benchmark rates, the best practice case yields DSCRs that surpass the target rates while those of standard practice case stay below the reference rates. The best practice case could also accommodate higher debt share and have lower levelized cost of electricity (LCOE) while the standard practice case would require a lower debt share but having a higher LCOE. - Highlights: •Best practice solar energy resource estimation uses multiple datasets. •Multiple datasets are combined through measure-correlate-predict technique. •Correlated data have lower uncertainty and yields superior high-confidence energy production. •Best practice case yields debt-service coverage ratios (DSCRs) that surpass the benchmark rates. •Best practice case accommodates high debt share and have low levelized cost of electricity.

  4. Solar Energy Resource Analysis and Evaluation of Photovoltaic System Performance in Various Regions of Saudi Arabia

    Directory of Open Access Journals (Sweden)

    Ahmed Bilal Awan

    2018-04-01

    Full Text Available According to Vision 2030, the Kingdom of Saudi Arabia (K.S.A plans to harness 9.5 GW of energy from renewable energy sources, which includes a major part of solar PV generation. This massive implementation of solar projects requires an accurate assessment and analysis of solar resource data and PV site selection. This paper presents a detailed analysis of one-year solar radiation data and energy output of 100 kW PV systems at 44 different locations across the K.S.A. Coastal areas have a lower amount of global horizontal irradiance (GHI as compared to inland areas. Najran University station gives the highest annual electrical output of 172,083 kWh, yield factor of 1721, and capacity utilization factor of 19.6%. Sharurah and Timma TVTC are second and third best with respect to annual PV performance. Similarly, during high load summer season (April–October, Tabuk station is the best location for a PV power plant with an electrical output of 110,250 kWh, yield factor of 1102, and capacity utilization factor of 21.46%. Overall, the northern province of Tabuk is the most feasible region for a solar PV plant. The basic approach presented in this research study compares solar resource pattern and solar PV system output pattern with the load profile of the country. The site selected based on this criterion is recommended to be economically most feasible which can reduce the stress on electricity companies during high load seasons by clipping the peak load during daytime in the hot summer period.

  5. Analysis of the solar/wind resources in Southern Spain for optimal sizing of hybrid solar-wind power generation systems

    Science.gov (United States)

    Quesada-Ruiz, S.; Pozo-Vazquez, D.; Santos-Alamillos, F. J.; Lara-Fanego, V.; Ruiz-Arias, J. A.; Tovar-Pescador, J.

    2010-09-01

    A drawback common to the solar and wind energy systems is their unpredictable nature and dependence on weather and climate on a wide range of time scales. In addition, the variation of the energy output may not match with the time distribution of the load demand. This can partially be solved by the use of batteries for energy storage in stand-alone systems. The problem caused by the variable nature of the solar and wind resources can be partially overcome by the use of energy systems that uses both renewable resources in a combined manner, that is, hybrid wind-solar systems. Since both resources can show complementary characteristics in certain location, the independent use of solar or wind systems results in considerable over sizing of the batteries system compared to the use of hybrid solar-wind systems. Nevertheless, to the day, there is no single recognized method for properly sizing these hybrid wind-solar systems. In this work, we present a method for sizing wind-solar hybrid systems in southern Spain. The method is based on the analysis of the wind and solar resources on daily scale, particularly, its temporal complementary characteristics. The method aims to minimize the size of the energy storage systems, trying to provide the most reliable supply.

  6. Competition partition of soil and solar radiation resources between soybean cultivars and concurrent genotypes

    International Nuclear Information System (INIS)

    Bianchi, M.A.; Fleck, N.G.; Dillenburg, L.R.

    2006-01-01

    Plants compete for environmental resources located below and over soil surface. Physical separation of competition allows understanding the relative importance of each fraction, as well as identifying possible differences among species. The aim of this research was to separate the individual effects resulting from competition for soil or solar radiation resources, between soybean and concurrent plants. Thus, experiments using pots were carried out at UFRGS, in Porto Alegre-RS, in 2001 and 2002. The treatments tested resulted from the combinations of two concurrent genotypes (crop and competitor) and four competition conditions (absence of competition, competition for soil and solar radiation, competition for soil resources, and competition for solar radiation). Soybean cultivars IAS 5 and FEPAGRO RS 10 represented the crop, whereas radish forage and the soybean cultivar FUNDACEP 33 were the competitors tested. Morpho-physiological variables were evaluated in the soybean plants and radish forage. Growth of the soybean plants was most affected by soil resources competition, with RS 10 cultivar being more competitive than IAS 5.Radish forage did not interfere in the growth of soybean cultivars but it benefited from soybean presence. (author) 6

  7. Renewable Resources: a national catalog of model projects. Volume 1. Northeast Solar Energy Center Region

    Energy Technology Data Exchange (ETDEWEB)

    None

    1980-07-01

    This compilation of diverse conservation and renewable energy projects across the United States was prepared through the enthusiastic participation of solar and alternate energy groups from every state and region. Compiled and edited by the Center for Renewable Resources, these projects reflect many levels of innovation and technical expertise. In many cases, a critique analysis is presented of how projects performed and of the institutional conditions associated with their success or failure. Some 2000 projects are included in this compilation; most have worked, some have not. Information about all is presented to aid learning from these experiences. The four volumes in this set are arranged in state sections by geographic region, coinciding with the four Regional Solar Energy Centers. The table of contents is organized by project category so that maximum cross-referencing may be obtained. This volume includes information on the Northeast Solar Energy Center Region. (WHK).

  8. Renewable Resources: a national catalog of model projects. Volume 3. Southern Solar Energy Center Region

    Energy Technology Data Exchange (ETDEWEB)

    None

    1980-07-01

    This compilation of diverse conservation and renewable energy projects across the United States was prepared through the enthusiastic participation of solar and alternate energy groups from every state and region. Compiled and edited by the Center for Renewable Resources, these projects reflect many levels of innovation and technical expertise. In many cases, a critique analysis is presented of how projects performed and of the institutional conditions associated with their success or failure. Some 2000 projects are included in this compilation; most have worked, some have not. Information about all is presented to aid learning from these experiences. The four volumes in this set are arranged in state sections by geographic region, coinciding with the four Regional Solar Energy Centers. The table of contents is organized by project category so that maximum cross-referencing may be obtained. This volume includes information on the Southern Solar Energy Center Region. (WHK)

  9. Renewable Resources: a national catalog of model projects. Volume 4. Western Solar Utilization Network Region

    Energy Technology Data Exchange (ETDEWEB)

    None

    1980-07-01

    This compilation of diverse conservation and renewable energy projects across the United States was prepared through the enthusiastic participation of solar and alternate energy groups from every state and region. Compiled and edited by the Center for Renewable Resources, these projects reflect many levels of innovation and technical expertise. In many cases, a critique analysis is presented of how projects performed and of the institutional conditions associated with their success or failure. Some 2000 projects are included in this compilation; most have worked, some have not. Information about all is presented to aid learning from these experiences. The four volumes in this set are arranged in state sections by geographic region, coinciding with the four Regional Solar Energy Centers. The table of contents is organized by project category so that maximum cross-referencing may be obtained. This volume includes information on the Western Solar Utilization Network Region. (WHK)

  10. Forecast model for the evaluation of economic resources employed in the health care of patients with HIV infection

    Directory of Open Access Journals (Sweden)

    Sacchi P

    2012-05-01

    Full Text Available Paolo Sacchi1, Savino FA Patruno1, Raffaele Bruno1, Serena Maria Benedetta Cima1, Pietro Previtali2, Alessia Franchini2, Luca Nicolini3, Carla Rognoni4, Lucia Sacchi5, Riccardo Bellazzi4, Gaetano Filice11Divisione di Malattie Infettive e Tropicali - Fondazione IRCCS Policlinico San Matteo, Pavia, Italy; 2Università degli Studi di Pavia – Facoltà di Economia, Pavia, Italy; 3Controllo di Gestione Fondazione IRCCS Policlinico San Matteo di Pavia, Pavia, Italy; 4Dipartimento di Informatica e Sistemistica, Universita' degli Studi di Pavia, Pavia, Italy; 5Department of Information Systems and Computing, Brunel University, London, UKBackground and aims: The total health care cost for human immunodeficiency virus (HIV patients has constantly grown in recent years. To date, there is no information about how this trend will behave over the next few years. The aim of the present study is to define a pharmacoeconomic model for the forecast of the costs of a group of chronically treated patients followed over the period 2004–2009.Methods: A pharmacoeconomics model was built to describe the probability of transition among different health states and to modify the therapy over time. A Markov model was applied to evaluate the temporal evolution of the average cost. The health care resources exploited during hospitalization were analyzed by using an “activity-based costing” method.Results: The Markov model showed that the mean total cost, after an initial increase, tended to remain stable. A total of 20 clinical records were examined. The average daily cost for each patient was EUR 484.42, with a cost for admission of EUR 6781.88.Conclusion: The treatment of HIV infection in compliance with the guidelines is also effective from the payer perspective, as it allows a good health condition to be maintained and reduces the need and the costs of hospitalizations.Keywords: health care cost, HIV, Markov model, activity-based costing

  11. Modeling for sustainable use of biofuels, eolic and solar energy within the scope of the local Brazilian Integrated Planning of Energy Resources: case study of this plan in Aracatuba region, SP, Brazil; Modelagem para o aproveitamento sustentavel dos biocombustiveis, energia eolica e solar dentro do PIR (Planejamento Integrado de Recursos Energeticos) local: estudo de caso do PIR da regiao de Aracatuba, SP, Brasil

    Energy Technology Data Exchange (ETDEWEB)

    Bernal, Jonathas Luiz de Oliveira

    2009-07-01

    It is evaluated the wind power, solar energy resources and biofuels available in Aracatuba through integrated resources planning methodology. which seeks to systematize and qualify the impacts associated with the use of energy by integrating supply and demand and seeking the lowest full-cost recital characteristics of each energy resource in environmental, social, political and technical-economic dimensions . Working with the demand forecast for trend, sustainable energy scenarios, optimistic and sustainable-prime as a PIN for the integration of energy resources over time, and considering the vigilant of Energy-environmental parameters, fetching mapping meeting local demand and export of energy. Thus conclude that the energy resources considered may meet the requirements of demand in all scenarios, but with the possibility of exhaustion in certain scenarios with planning horizon larger than 30 years. (author)

  12. Monitoring and forecasting of radiation hazard from great solar energetic particle events by using on-line one-min neutron monitor and satellite data

    International Nuclear Information System (INIS)

    Dorman, L. I.

    2007-01-01

    The method of automatically determining the start of great solar energetic particle (SEP) events are described on the basis of cosmic ray (CR) one-min observations by neutron monitors in real-time scale. It is shown that the probabilities of false alarms and missed triggers are negligible. After the start of SEP event, it is automatically determined by the method of coupling functions the SEP energy spectrum and flux for each minute of observations. By solving the inverse problem during few first minutes of SEP event, diffusion coefficient in the interplanetary space, source function on the Sun, and time of ejection of SEP into solar wind are determined. For extending obtained results into small energy range we use also available from Internet the satellite one-min CR data. This make possible to give forecast of space-time variation of SEP for more than 2 days and estimate expected radiation dose for satellite and aircraft. With each new minute of observations, the quality of forecast increased, and after ∼30 min became near 100%. (authors)

  13. El Niño-Southern Oscillation and water resources in the headwaters region of the Yellow River: links and potential for forecasting

    Directory of Open Access Journals (Sweden)

    A. Lü

    2011-04-01

    Full Text Available This research explores the rainfall-El Niño-Southern Oscillation (ENSO and runoff-ENSO relationships and examines the potential for water resource forecasting using these relationships. The Southern Oscillation Index (SOI, Niño1.2, Niño3, Niño4, and Niño3.4 were selected as ENSO indicators for cross-correlation analyses of precipitation and runoff. There was a significant correlation (95% confidence level between precipitation and ENSO indicators during three periods: January, March, and from September to November. In addition, monthly streamflow and monthly ENSO indictors were significantly correlated during three periods: from January to March, June, and from October to December (OND, with lag periods between one and twelve months. Because ENSO events can be accurately predicted one to two years in advance using physical modeling of the coupled ocean-atmosphere system, the lead time for forecasting runoff using ENSO indicators in the Headwaters Region of the Yellow River could extend from one to 36 months. Therefore, ENSO may have potential as a powerful forecasting tool for water resources in the headwater regions of Yellow River.

  14. A Tool for Empirical Forecasting of Major Flares, Coronal Mass Ejections, and Solar Particle Events from a Proxy of Active-Region Free Magnetic Energy

    Science.gov (United States)

    Barghouty, A. F.; Falconer, D. A.; Adams, J. H., Jr.

    2010-01-01

    This presentation describes a new forecasting tool developed for and is currently being tested by NASA s Space Radiation Analysis Group (SRAG) at JSC, which is responsible for the monitoring and forecasting of radiation exposure levels of astronauts. The new software tool is designed for the empirical forecasting of M and X-class flares, coronal mass ejections, as well as solar energetic particle events. Its algorithm is based on an empirical relationship between the various types of events rates and a proxy of the active region s free magnetic energy, determined from a data set of approx.40,000 active-region magnetograms from approx.1,300 active regions observed by SOHO/MDI that have known histories of flare, coronal mass ejection, and solar energetic particle event production. The new tool automatically extracts each strong-field magnetic areas from an MDI full-disk magnetogram, identifies each as an NOAA active region, and measures a proxy of the active region s free magnetic energy from the extracted magnetogram. For each active region, the empirical relationship is then used to convert the free magnetic energy proxy into an expected event rate. The expected event rate in turn can be readily converted into the probability that the active region will produce such an event in a given forward time window. Descriptions of the datasets, algorithm, and software in addition to sample applications and a validation test are presented. Further development and transition of the new tool in anticipation of SDO/HMI is briefly discussed.

  15. Comparison of hourly surface downwelling solar radiation estimated from MSG-SEVIRI and forecast by the RAMS model with pyranometers over Italy

    Science.gov (United States)

    Federico, Stefano; Torcasio, Rosa Claudia; Sanò, Paolo; Casella, Daniele; Campanelli, Monica; Fokke Meirink, Jan; Wang, Ping; Vergari, Stefania; Diémoz, Henri; Dietrich, Stefano

    2017-06-01

    In this paper, we evaluate the performance of two global horizontal solar irradiance (GHI) estimates, one derived from Meteosat Second Generation (MSG) and another from the 1-day forecast of the Regional Atmospheric Modeling System (RAMS) mesoscale model. The horizontal resolution of the MSG-GHI is 3 × 5 km2 over Italy, which is the focus area of this study. For this paper, RAMS has the horizontal resolution of 4 km.The performances of the MSG-GHI estimate and RAMS-GHI 1-day forecast are evaluated for 1 year (1 June 2013-31 May 2014) against data of 12 ground-based pyranometers over Italy spanning a range of climatic conditions, i.e. from maritime Mediterranean to Alpine climate.Statistics for hourly GHI and daily integrated GHI are presented for the four seasons and the whole year for all the measurement sites. Different sky conditions are considered in the analysisResults for hourly data show an evident dependence on the sky conditions, with the root mean square error (RMSE) increasing from clear to cloudy conditions. The RMSE is substantially higher for Alpine stations in all the seasons, mainly because of the increase of the cloud coverage for these stations, which is not well represented at the satellite and model resolutions. Considering the yearly statistics computed from hourly data for the RAMS model, the RMSE ranges from 152 W m-2 (31 %) obtained for Cozzo Spadaro, a maritime station, to 287 W m-2 (82 %) for Aosta, an Alpine site. Considering the yearly statistics computed from hourly data for MSG-GHI, the minimum RMSE is for Cozzo Spadaro (71 W m-2, 14 %), while the maximum is for Aosta (181 W m-2, 51 %). The mean bias error (MBE) shows the tendency of RAMS to over-forecast the GHI, while no specific behaviour is found for MSG-GHI.Results for daily integrated GHI show a lower RMSE compared to hourly GHI evaluation for both RAMS-GHI 1-day forecast and MSG-GHI estimate. Considering the yearly evaluation, the RMSE of daily integrated GHI is at least 9

  16. Solar resource assessment in complex orography: a comparison of available datasets for the Trentino region

    Science.gov (United States)

    Laiti, Lavinia; Giovannini, Lorenzo; Zardi, Dino

    2015-04-01

    The accurate assessment of the solar radiation available at the Earth's surface is essential for a wide range of energy-related applications, such as the design of solar power plants, water heating systems and energy-efficient buildings, as well as in the fields of climatology, hydrology, ecology and agriculture. The characterization of solar radiation is particularly challenging in complex-orography areas, where topographic shadowing and altitude effects, together with local weather phenomena, greatly increase the spatial and temporal variability of such variable. At present, approaches ranging from surface measurements interpolation to orographic down-scaling of satellite data, to numerical model simulations are adopted for mapping solar radiation. In this contribution a high-resolution (200 m) solar atlas for the Trentino region (Italy) is presented, which was recently developed on the basis of hourly observations of global radiation collected from the local radiometric stations during the period 2004-2012. Monthly and annual climatological irradiation maps were obtained by the combined use of a GIS-based clear-sky model (r.sun module of GRASS GIS) and geostatistical interpolation techniques (kriging). Moreover, satellite radiation data derived by the MeteoSwiss HelioMont algorithm (2 km resolution) were used for missing-data reconstruction and for the final mapping, thus integrating ground-based and remote-sensing information. The results are compared with existing solar resource datasets, such as the PVGIS dataset, produced by the Joint Research Center Institute for Energy and Transport, and the HelioMont dataset, in order to evaluate the accuracy of the different datasets available for the region of interest.

  17. Wind and solar energy resources on the 'Roof of the World'

    Science.gov (United States)

    Zandler, Harald; Morche, Thomas; Samimi, Cyrus

    2015-04-01

    The Eastern Pamirs of Tajikistan, often referred to as 'Roof of the World', are an arid high mountain plateau characterized by severe energy poverty that may have great potential for renewable energy resources due to the prevailing natural conditions. The lack of energetic infrastructure makes the region a prime target for decentralized integration of wind and solar power. However, up to date no scientific attempt to assess the regional potential of these resources has been carried out. In this context, it is particularly important to evaluate if wind and solar energy are able to provide enough power to generate thermal energy, as other thermal energy carriers are scarce or unavailable and the existing alternative, local harvest of dwarf shrubs, is unsustainable due to the slow regeneration in this environment. Therefore, this study examines the feasibility of using wind and solar energy as thermal energy sources. Financial frame conditions were set on a maximum amount of five million Euros. This sum provides a realistic scenario as it is based on the current budget of the KfW development bank to finance the modernization of the local hydropower plant in the regions only city, Murghab, with about 1500 households. The basis for resource assessment is data of four climate stations, erected for this purpose in 2012, where wind speed, wind direction, global radiation and temperature are measured at a half hourly interval. These measurements confirm the expectation of a large photovoltaic potential and high panel efficiency with up to 84 percent of extraterrestrial radiation reaching the surface and only 16 hours of temperatures above 25°C were measured in two years at the village stations on average. As these observations are only point measurements, radiation data and the ASTER GDEM was used to train a GIS based solar radiation model to spatially extrapolate incoming radiation. With mean validation errors ranging from 5% in July (minimum) to 15% in December (maximum

  18. Solar System Exploration Augmented by In-Situ Resource Utilization: Human Mercury and Saturn Exploration

    Science.gov (United States)

    Palaszewski, Bryan

    2015-01-01

    Human and robotic missions to Mercury and Saturn are presented and analyzed. Unique elements of the local planetary environments are discussed and included in the analyses and assessments. Using historical studies of space exploration, in-situ resource utilization (ISRU), and industrialization all point to the vastness of natural resources in the solar system. Advanced propulsion benefitted from these resources in many way. While advanced propulsion systems were proposed in these historical studies, further investigation of nuclear options using high power nuclear thermal and nuclear pulse propulsion as well as advanced chemical propulsion can significantly enhance these scenarios. Updated analyses based on these historical visions will be presented. Nuclear thermal propulsion and ISRU enhanced chemical propulsion landers are assessed for Mercury missions. At Saturn, nuclear pulse propulsion with alternate propellant feed systems and Titan exploration with chemical propulsion options are discussed.

  19. An analysis of wind and solar energy resources for the State of Kuwait

    Science.gov (United States)

    Alhusainan, Haya Nasser

    Kuwait is an important producer of oil and gas. Its rapid socio-economic growth has been characterized by increasing population, high rates of urbanization, and substantial industrialization, which is transforming it into a large big energy consumer as well. In addition to urbanization, climatic conditions have played an important function in increasing demand for electricity in Kuwait. Electricity for thermal cooling has become essential in the hot desert climate, and its use has developed rapidly along with the economic development, urbanization, and population growth. This study examines the long-term wind and solar resources over the Kuwait to determine the feasibility of these resources as potential sustainable and renewable energy sources. The ultimate goal of this research is to help identify the potential role of renewable energy in Kuwait. This study will examine the drivers and requirements for the deployment of these energy sources and their possible integration into the electricity generation sector to illustrate how renewable energy can be a suitable resource for power production in Kuwait and to illustrate how they can also be used to provide electricity for the country. For this study, data from sixteen established stations monitored by the meteorological department were analyzed. A solar resource map was developed that identifies the most suitable locations for solar farm development. A range of different relevant variables, including, for example, electric networks, population zones, fuel networks, elevation, water wells, streets, and weather stations, were combined in a geospatial analysis to predict suitable locations for solar farm development and placement. An analysis of recommendations, future energy targets and strategies for renewable energy policy in Kuwait are then conducted. This study was put together to identify issues and opportunities related to renewable energy in the region, since renewable energy technologies are still limited in

  20. FORECASTING OF PRODUCTION OUTPUT FOR LIGHT INDUSTRY ENTERPRISES WITH PURPOSE TO DETERMINE THEIR POWER RESOURCES REQUIREMENTS. Part 1

    Directory of Open Access Journals (Sweden)

    V. N. Romaniuk

    2015-01-01

    industry as a whole. It has become possible on the basis of the results obtained in the process of studying its power and heat engineering problems. The results of an analytical calculation analysis of statistical and obtained forecasting data are considered as a basis for the development of scientifically substantiated proposals for modernization of energy supply systems and power resource saving of the industry that fully corresponds to the investigation objective. The result of the executed investigations, that is a significant reduction of the energy component in the production costs will be presented in the follow-up publications. 

  1. Atmospheric Mining in the Outer Solar System: Resource Capturing, Storage, and Utilization

    Science.gov (United States)

    Palaszewski, Bryan

    2014-01-01

    Atmospheric mining in the outer solar system has been investigated as a means of fuel production for high energy propulsion and power. Fusion fuels such as helium 3 and hydrogen can be wrested from the atmospheres of Uranus and Neptune and either returned to Earth or used in-situ for energy production. Helium 3 and hydrogen (deuterium, etc.) were the primary gases of interest with hydrogen being the primary propellant for nuclear thermal solid core and gas core rocket-based atmospheric flight. A series of analyses were undertaken to investigate resource capturing aspects of atmospheric mining in the outer solar system. This included the gas capturing rate for hydrogen helium 4 and helium 3, storage options, and different methods of direct use of the captured gases. Additional supporting analyses were conducted to illuminate vehicle sizing and orbital transportation issues.

  2. Research on energy supply, demand and economy forecasting in Japan

    International Nuclear Information System (INIS)

    Shiba, Tsuyoshi; Kamezaki, Hiroshi; Yuyama, Tomonori; Suzuki, Atsushi

    1999-10-01

    This project aims to do research on forecasts of energy demand structure and electricity generation cost in each power plant in Japan in the 21st century, considering constructing successful FBR scenario. During the process of doing research on forecasts of energy demand structure in Japan, documents published from organizations in inside and outside of Japan were collected. These documents include prospects of economic growth rate, forecasts of amount for energy supply and demand, the maximum amount of introducing new energy resources, CO2 regulation, and evaluation of energy best mixture. Organizations in Japan such as Economic Council and Japan Energy Economic Research Institute have provided long-term forecasts until the early 21st century. Meanwhile, organizations overseas have provided forecasts of economic structure, and demand and supply for energy in OECD and East Asia including Japan. In connection with forecasts of electricity generation cost in each power plant, views on the ultimate reserves and cost of resources are reviewed in this report. According to some views on oil reserves, making assumptions based on reserves/production ratio, the maximum length of the time that oil reserves will last is 150 years. In addition, this report provides summaries of cost and potential role of various resources, including solar energy and wind energy; and views on waste, safety, energy security-related externality cost, and the price of transferring CO2 emission right. (author)

  3. Forecasting the impact of global changes on the water resources of a mountainous catchment in the Chilean Andes

    Science.gov (United States)

    Ruelland, D.; Campéon, C.; Dezetter, A.; Jourde, H.

    2012-04-01

    in a piezometer at the basin outlet are also in good agreement. The model thus provides encouraging simulations of groundwater and surface water dynamics when applied to various climatic conditions. Simulations are improved when a dam located in the upstream catchment is considered into the model. In contrast, integrating agricultural and domestic water withdrawals does not improve significantly the simulations. However, it allows assessing the ability of water resources to supply water demands by computing a water allocation index. The climatic scenarios forecast an increase in temperature of about 1-2°C and a 20-30% reduction in precipitation by the 2050 horizon. According to the hydrological simulations, the mean annual discharge of the upper Elqui River may decline by 30-40%, and the seasonal peak flow would occur earlier than in current conditions. As a result, the agricultural demands (90% of the water uses) may not be always satisfied, especially during the summer season, as shown by the future trends in the water allocation index. This calls for evaluating the efficiency of adaptation strategies consisting in an improvement of the irrigation system and of water management, which is the subject of ongoing research.

  4. Opening up the solar box: Cultural resource management and actor network theory in solar energy projects in the Mojave Desert

    Science.gov (United States)

    Gorrie, Bryan F.

    This project considers the ways that Actor-Network Theory (ANT) can be brought to bear upon Cultural Resource Management (CRM) practices on renewable energy projects. ANT is a way of making inquiry into scientific knowledge practices and as CRM is intended to preserve environmental, historic, and prehistoric resources, it necessarily involves certain kinds of knowledge generation about regions in which projects are being developed. Because the practice of CRM is complex, involving a range of actors from developers to biologists, native peoples to academics, private landholders to environmental and cultural activists, it is imperative to account for the interests of all stakeholders and to resist devolving into the polemical relations of winners and losers, good and bad participants, or simple situations of right and wrong. This project intends to account for the "matters of concern" of various actors, both primary and secondary, by examining the case study of a single solar installation project in the Mojave Desert. A theoretical description of ANT is provided at the beginning and the concerns of this theory are brought to bear upon the case study project through describing the project, discussing the laws governing CRM on federal lands and in the state of California, and providing the points of view of various interviewees who worked directly or indirectly on various aspects of CRM for the solar project. The creators of ANT claim that it is not a methodology but it does speak to ethnomethodologies in that it insists that there is always something more to learn from inquiring into and describing any given situation. These descriptions avoid generalizations, providing instead various points of entry, from diverse perspectives to the project. There is an invitation to avoid assuming that one knows all there is to know about a given situation and to choose instead to continue investigating and thus give voice to the more obscure, often marginalized, voices in the

  5. Lunar Solar Power System Driven Human Development of the Moon and Resource-Rich Exploration of the Inner Solar System

    Science.gov (United States)

    Criswell, D. R.

    2002-01-01

    available that can build fundamentally new infrastructure from the common silicate materials of asteroids and the moons of Mars. Commercial power can be beamed from the Moon to ion-propelled rockets and to industrial facilities throughout the inner solar systems (6, 7). The LSP System can establish the Earth and the Moon as a two-planet economy. Lunar and cis-lunar industry will grow through profitable activities. Exploration of the inner solar system can stage, at marginal cost, from the Moon and cis-lunar space rather than the surface of Earth. 1. World Energy Council (2000) Energy for Tomorrow's World - Acting Now!, 175pp., Atalink Projects Ltd, London. 2. Criswell, David R. (2001) Lunar Solar Power System: Industrial Research, Development, and Demonstration, Session 1.2.2: Hydroelectricity, Nuclear Energy and New Renewables, 18th World Energy Congress. [http://www.wec.co.uk] 3. Strong, Marice (2001) Where on Earth are We Going?, (See p. 351-352), 419pp., Random House (forward by Kofi Annan) 4. Criswell, D. R. And R. D. Waldron (1993), "International lunar base and the lunar-based power system to supply Earth with electric power," Acta Astronautica, 29, No. 6: 469-480. 5. Criswell, D. R. (1998), Lunar Solar Power: Lunar unit processes, scales, and challenges, 6 p.p. (ms), ExploSpace: Workshop on Space Exploration and Resources Exploitation, European Space Agency, Cagliari, Sardinia, (October 20 - 22). 6. Criswell, D. R. (1999), Commercial lunar solar power and sustainable growth of the two-planet economy, Proc. Third International Working Group on Lunar Exploration and Exploitation, Solar System Research, Vol. 33, #5, 356-362, Moscow, (October 11-14). 7. Criswell, D.R. 2000 (October) Commercial power for Earth and lunar industrial development, 7pp., 51st Congress of the International Astronautical Federation (IAF). (Rio de Janeiro, Brazil). Paper #IAA-00-IAA.13.2.06.

  6. Visual Resource Analysis for Solar Energy Zones in the San Luis Valley

    Energy Technology Data Exchange (ETDEWEB)

    Sullivan, Robert [Argonne National Laboratory (ANL), Argonne, IL (United States). Environmental Science Division; Abplanalp, Jennifer M. [Argonne National Laboratory (ANL), Argonne, IL (United States). Environmental Science Division; Zvolanek, Emily [Argonne National Laboratory (ANL), Argonne, IL (United States). Environmental Science Division; Brown, Jeffery [Bureau of Land Management, Washington, DC (United States). Dept. of the Interior

    2016-01-01

    This report summarizes the results of a study conducted by Argonne National Laboratory’s (Argonne’s) Environmental Science Division for the U.S. Department of the Interior Bureau of Land Management (BLM). The study analyzed the regional effects of potential visual impacts of solar energy development on three BLM-designated solar energy zones (SEZs) in the San Luis Valley (SLV) in Colorado, and, based on the analysis, made recommendations for or against regional compensatory mitigation to compensate residents and other stakeholders for the potential visual impacts to the SEZs. The analysis was conducted as part of the solar regional mitigation strategy (SRMS) task conducted by BLM Colorado with assistance from Argonne. Two separate analyses were performed. The first analysis, referred to as the VSA Analysis, analyzed the potential visual impacts of solar energy development in the SEZs on nearby visually sensitive areas (VSAs), and, based on the impact analyses, made recommendations for or against regional compensatory mitigation. VSAs are locations for which some type of visual sensitivity has been identified, either because the location is an area of high scenic value or because it is a location from which people view the surrounding landscape and attach some level of importance or sensitivity to what is seen from the location. The VSA analysis included both BLM-administered lands in Colorado and in the Taos FO in New Mexico. The second analysis, referred to as the SEZ Analysis, used BLM visual resource inventory (VRI) and other data on visual resources in the former Saguache and La Jara Field Offices (FOs), now contained within the San Luis Valley FO (SLFO), to determine whether the changes in scenic values that would result from the development of utility-scale solar energy facilities in the SEZs would affect the quality and quantity of valued scenic resources in the SLV region as a whole. If the regional effects were judged to be significant, regional

  7. Solar resources estimation combining digital terrain models and satellite images techniques

    Energy Technology Data Exchange (ETDEWEB)

    Bosch, J.L.; Batlles, F.J. [Universidad de Almeria, Departamento de Fisica Aplicada, Ctra. Sacramento s/n, 04120-Almeria (Spain); Zarzalejo, L.F. [CIEMAT, Departamento de Energia, Madrid (Spain); Lopez, G. [EPS-Universidad de Huelva, Departamento de Ingenieria Electrica y Termica, Huelva (Spain)

    2010-12-15

    One of the most important steps to make use of any renewable energy is to perform an accurate estimation of the resource that has to be exploited. In the designing process of both active and passive solar energy systems, radiation data is required for the site, with proper spatial resolution. Generally, a radiometric stations network is used in this evaluation, but when they are too dispersed or not available for the study area, satellite images can be utilized as indirect solar radiation measurements. Although satellite images cover wide areas with a good acquisition frequency they usually have a poor spatial resolution limited by the size of the image pixel, and irradiation must be interpolated to evaluate solar irradiation at a sub-pixel scale. When pixels are located in flat and homogeneous areas, correlation of solar irradiation is relatively high, and classic interpolation can provide a good estimation. However, in complex topography zones, data interpolation is not adequate and the use of Digital Terrain Model (DTM) information can be helpful. In this work, daily solar irradiation is estimated for a wide mountainous area using a combination of Meteosat satellite images and a DTM, with the advantage of avoiding the necessity of ground measurements. This methodology utilizes a modified Heliosat-2 model, and applies for all sky conditions; it also introduces a horizon calculation of the DTM points and accounts for the effect of snow covers. Model performance has been evaluated against data measured in 12 radiometric stations, with results in terms of the Root Mean Square Error (RMSE) of 10%, and a Mean Bias Error (MBE) of +2%, both expressed as a percentage of the mean value measured. (author)

  8. Solar System Exploration Augmented by Lunar and Outer Planet Resource Utilization: Historical Perspectives and Future Possibilities

    Science.gov (United States)

    Palaszewski, Bryan

    2014-01-01

    Establishing a lunar presence and creating an industrial capability on the Moon may lead to important new discoveries for all of human kind. Historical studies of lunar exploration, in-situ resource utilization (ISRU) and industrialization all point to the vast resources on the Moon and its links to future human and robotic exploration. In the historical work, a broad range of technological innovations are described and analyzed. These studies depict program planning for future human missions throughout the solar system, lunar launched nuclear rockets, and future human settlements on the Moon, respectively. Updated analyses based on the visions presented are presented. While advanced propulsion systems were proposed in these historical studies, further investigation of nuclear options using high power nuclear thermal propulsion, nuclear surface power, as well as advanced chemical propulsion can significantly enhance these scenarios. Robotic and human outer planet exploration options are described in many detailed and extensive studies. Nuclear propulsion options for fast trips to the outer planets are discussed. To refuel such vehicles, atmospheric mining in the outer solar system has also been investigated as a means of fuel production for high energy propulsion and power. Fusion fuels such as helium 3 (3He) and hydrogen (H2) can be wrested from the atmospheres of Uranus and Neptune and either returned to Earth or used in-situ for energy production. Helium 3 and H2 (deuterium, etc.) were the primary gases of interest with hydrogen being the primary propellant for nuclear thermal solid core and gas core rocket-based atmospheric flight. A series of analyses have investigated resource capturing aspects of atmospheric mining in the outer solar system. These analyses included the gas capturing rate, storage options, and different methods of direct use of the captured gases. While capturing 3He, large amounts of hydrogen and 4He are produced. With these two additional

  9. 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...

  10. Forecasting the space weather impact

    DEFF Research Database (Denmark)

    Crosby, N. B.; Veronig, A.; Robbrecht, E.

    2012-01-01

    The FP7 COronal Mass Ejections and Solar Energetic Particles (COMESEP) project is developing tools for forecasting geomagnetic storms and solar energetic particle (SEP) radiation storms. By analysis of historical data, complemented by the extensive data coverage of solar cycle 23, the key ingredi...

  11. Possibilities of electricity generation from solar and other renewable resources in Turkey

    International Nuclear Information System (INIS)

    Tasdemiroglu, E.

    1993-01-01

    The paper begins by reviewing the conventional power generation in the country. Increasing power demand due to rapid industrialization as well as the environmental consequences of power generation will be discussed. The potential of renewable energy resources including solar, biomass, wind, and wave and their role in the power generation will be pointed out. Among the strong alternatives are thermal power plants, and rural electricity production by photovoltaic and by small wind machines. Finally, the technical economic difficulties in adapting renewable electricity generation systems for the conditions of the country will be discussed. (Author) 22 refs

  12. Quasi-static time-series simulation using OpenDSS in IEEE distribution feeder model with high PV penetration and its impact on solar forecasting

    Science.gov (United States)

    Mohammed, Touseef Ahmed Faisal

    Distribution System Simulator developed by Electric Power Research Institute, to simulate grid voltage profile with a large scale PV system under quasi-static time series considering variations of PV output in seconds, minutes, and the average daily load variations. A 13 bus IEEE distribution feeder model is utilized with distributed residential and commercial scale PV at different buses for simulation studies. Time series simulations are discussed for various modes of operation considering dynamic PV penetration at different time periods in a day. In addition, this thesis demonstrates simulations taking into account the presence of moving cloud for solar forecasting studies.

  13. Books and Other Resources for Education about the August 21, 2017, Solar Eclipse

    Science.gov (United States)

    Pasachoff, Jay M.; Fraknoi, Andrew; Kentrianakis, Michael

    2017-06-01

    As part of our work to reach and educate the 300+ million Americans of all ages about observing the August 21 solar eclipse, especially by being outdoors in the path of totality but also for those who will see only partial phases, we have compiled annotated lists of books, pamphlets, travel guides, websites, and other information useful for teachers, students, and the general public and made them available on the web, at conferences, and through webinars. Our list includes new eclipse books by David Barron, Anthony Aveni, Frank Close, Tyler Nordgren, John Dvorak, Michael Bakich, and others. We list websites accessible to the general public including those of the International Astronomical Union Working Group on Eclipses (http://eclipses.info, which has links to all the sites listed below); the AAS Eclipse 2017 Task Force (http://eclipse2017.aas.org); NASA Heliophysics (http://eclipse.nasa.gov); Fred Espenak (the updated successor to his authoritative "NASA website": http://EclipseWise.com); Michael Zeiler (http://GreatAmericanEclipse.com); Xavier Jubier (http://xjubier.free.fr/en/site_pages/solar_eclipses/); Jay Anderson (meteorology: http://eclipsophile.com); NASA's Eyes (http://eyes.nasa.gov/eyes-on-eclipse.html and its related app); the Astronomical Society of the Pacific (http://www.astrosociety.org/eclipse); Dan McGlaun (http://eclipse2017.org/); Bill Kramer (http://eclipse-chasers.com). Specialized guides include Dennis Schatz and Andrew Fraknoi's Solar Science for teachers (from the National Science Teachers Association:http://www.nsta.org/publications/press/extras/files/solarscience/SolarScienceInsert.pdf), and a printing with expanded eclipse coverage of Jay Pasachoff's, Peterson Field Guide to the Stars and Planets (14th printing of the fourth edition, 2016: http://solarcorona.com).A version of our joint list is to be published in the July issue of the American Journal of Physics as a Resource Letter on Eclipses, adding to JMP's 2010, "Resource Letter SP

  14. Solar System Exploration Augmented by In-Situ Resource Utilization: Mercury and Saturn Propulsion Investigations

    Science.gov (United States)

    Palaszewski, Bryan

    2016-01-01

    Human and robotic missions to Mercury and Saturn are presented and analyzed with a range of propulsion options. Historical studies of space exploration, in-situ resource utilization (ISRU), and industrialization all point to the vastness of natural resources in the solar system. Advanced propulsion benefitted from these resources in many ways. While advanced propulsion systems were proposed in these historical studies, further investigation of nuclear options using high power nuclear thermal and nuclear pulse propulsion as well as advanced chemical propulsion can significantly enhance these scenarios. Updated analyses based on these historical visions will be presented. Nuclear thermal propulsion and ISRU enhanced chemical propulsion landers are assessed for Mercury missions. At Saturn, nuclear pulse propulsion with alternate propellant feed systems and Titan exploration with chemical propulsion options are discussed. In-situ resource utilization was found to be critical in making Mercury missions more amenable for human visits. At Saturn, refueling using local atmospheric mining was found to be difficult to impractical, while refueling the Saturn missions from Uranus was more practical and less complex.

  15. Electric car with solar and wind energy may change the environment and economy: A tool for utilizing the renewable energy resource

    Science.gov (United States)

    Liu, Quanhua

    2014-01-01

    Energy and environmental issues are among the most important problems of public concern. Wind and solar energy may be one of the alternative solutions to overcome energy shortage and to reduce greenhouse gaseous emission. Using electric cars in cities can significantly improve the air quality there. Through our analyses and modeling on the basis of the National Centers for Environment Prediction data we confirm that the amount of usable solar and wind energy far exceeds the world's total energy demand, considering the feasibility of the technology being used. Storing the surplus solar and wind energy and then releasing this surplus on demand is an important approach to maintaining uninterrupted solar- and wind-generated electricity. This approach requires us to be aware of the available solar and wind energy in advance in order to manage their storage. Solar and wind energy depends on weather conditions and we know weather forecasting. This implies that solar and wind energy is predictable. In this article, we demonstrate how solar and wind energy can be forecasted. We provide a web tool that can be used by all to arrive at solar and wind energy amount at any location in the world. The tool is available at http://www.renewableenergyst.org. The website also provides additional information on renewable energy, which is useful to a wide range of audiences, including students, educators, and the general public.

  16. Forecast Combinations

    OpenAIRE

    Timmermann, Allan G

    2005-01-01

    Forecast combinations have frequently been found in empirical studies to produce better forecasts on average than methods based on the ex-ante best individual forecasting model. Moreover, simple combinations that ignore correlations between forecast errors often dominate more refined combination schemes aimed at estimating the theoretically optimal combination weights. In this paper we analyse theoretically the factors that determine the advantages from combining forecasts (for example, the d...

  17. Forecast combinations

    OpenAIRE

    Aiolfi, Marco; Capistrán, Carlos; Timmermann, Allan

    2010-01-01

    We consider combinations of subjective survey forecasts and model-based forecasts from linear and non-linear univariate specifications as well as multivariate factor-augmented models. Empirical results suggest that a simple equal-weighted average of survey forecasts outperform the best model-based forecasts for a majority of macroeconomic variables and forecast horizons. Additional improvements can in some cases be gained by using a simple equal-weighted average of survey and model-based fore...

  18. 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

  19. Integrating Solar Power onto the Electric Grid - Bridging the Gap between Atmospheric Science, Engineering and Economics

    Science.gov (United States)

    Ghonima, M. S.; Yang, H.; Zhong, X.; Ozge, B.; Sahu, D. K.; Kim, C. K.; Babacan, O.; Hanna, R.; Kurtz, B.; Mejia, F. A.; Nguyen, A.; Urquhart, B.; Chow, C. W.; Mathiesen, P.; Bosch, J.; Wang, G.

    2015-12-01

    One of the main obstacles to high penetrations of solar power is the variable nature of solar power generation. To mitigate variability, grid operators have to schedule additional reliability resources, at considerable expense, to ensure that load requirements are met by generation. Thus despite the cost of solar PV decreasing, the cost of integrating solar power will increase as penetration of solar resources onto the electric grid increases. There are three principal tools currently available to mitigate variability impacts: (i) flexible generation, (ii) storage, either virtual (demand response) or physical devices and (iii) solar forecasting. Storage devices are a powerful tool capable of ensuring smooth power output from renewable resources. However, the high cost of storage is prohibitive and markets are still being designed to leverage their full potential and mitigate their limitation (e.g. empty storage). Solar forecasting provides valuable information on the daily net load profile and upcoming ramps (increasing or decreasing solar power output) thereby providing the grid advance warning to schedule ancillary generation more accurately, or curtail solar power output. In order to develop solar forecasting as a tool that can be utilized by the grid operators we identified two focus areas: (i) develop solar forecast technology and improve solar forecast accuracy and (ii) develop forecasts that can be incorporated within existing grid planning and operation infrastructure. The first issue required atmospheric science and engineering research, while the second required detailed knowledge of energy markets, and power engineering. Motivated by this background we will emphasize area (i) in this talk and provide an overview of recent advancements in solar forecasting especially in two areas: (a) Numerical modeling tools for coastal stratocumulus to improve scheduling in the day-ahead California energy market. (b) Development of a sky imager to provide short term

  20. Temporal and spatial complementarity of wind and solar resources in Lower Silesia (Poland)

    Science.gov (United States)

    Jurasz, Jakub; Wdowikowski, Marcin; Kaźmierczak, Bartosz; Dąbek, Paweł

    2017-11-01

    This paper investigates the concept of temporal and spatial complementarity of wind and solar resources in Lower Silesia (south-wester Poland). For the purpose of our research we have used hourly load and energy yield from photovoltaics and wind turbines covering period 2010-2014. In order to assess the spatial complementarity we have divided the considered voivodeship into 74 squared regions with maximal area of 400 km2. The obtained results indicate an existence of temporal complementarity on a monthly time scale and a positive correlation between load and wind generation patterns (also on a monthly time scale). The temporal complementarity for hourly time series in relatively low but has potential to smooth the energy generation curves.

  1. SPADER - Science Planning Analysis and Data Estimation Resource for the NASA Parker Solar Probe Mission

    Science.gov (United States)

    Rodgers, D. J.; Fox, N. J.; Kusterer, M. B.; Turner, F. S.; Woleslagle, A. B.

    2017-12-01

    Scheduled to launch in July 2018, the Parker Solar Probe (PSP) will orbit the Sun for seven years, making a total of twenty-four extended encounters inside a solar radial distance of 0.25 AU. During most orbits, there are extended periods of time where PSP-Sun-Earth geometry dramatically reduces PSP-Earth communications via the Deep Space Network (DSN); there is the possibility that multiple orbits will have little to no high-rate downlink available. Science and housekeeping data taken during an encounter may reside on the spacecraft solid state recorder (SSR) for multiple orbits, potentially running the risk of overflowing the SSR in the absence of mitigation. The Science Planning Analysis and Data Estimation Resource (SPADER) has been developed to provide the science and operations teams the ability to plan operations accounting for multiple orbits in order to mitigate the effects caused by the lack of high-rate downlink. Capabilities and visualizations of SPADER are presented; further complications associated with file downlink priority and high-speed data transfers between instrument SSRs and the spacecraft SSR are discussed, as well as the long-term consequences of variations in DSN downlink parameters on the science data downlink.

  2. Solar energy

    International Nuclear Information System (INIS)

    Anon.

    1992-01-01

    This chapter discusses the role solar energy may have in the energy future of the US. The topics discussed in the chapter include the solar resource, solar architecture including passive solar design and solar collectors, solar-thermal concentrating systems including parabolic troughs and dishes and central receivers, photovoltaic cells including photovoltaic systems for home use, and environmental, health and safety issues

  3. Space Resource Utilization and Extending Human Presence Across the Solar System

    Science.gov (United States)

    Curreri, Peter A.

    2005-01-01

    investment enables commercial and private viability beyond Earth orbit. For example, analysis has shown the lunar oxygen production for propellant becomes commercially viable after the exploration program completes the R&D, and power from lunar derived photovoltaics could, according to past NASA sponsored studies, pay for themselves while supplying most of Earth's electrical energy after about 17 years. Besides the Moon and Mars the resources of the near Earth asteroids enable the building of large space structures and science payloads. Analysis has shown that one of the thousands of these objects (some as easily accessible in space as the Moon and Mars), 2 km dia, the size of a typical open pit mine, would cost the total global financial product of Earth for 30,000 years if we were to launch it from Earth. Beyond Mars, the belt asteroids have been calculated to contain enough materials for habitat and life to support 10 quadrillion people. Thus, the development and use of space resources enables the extension of human life through the solar system allowing humanity to move from a planetary to a solar system society.

  4. Forecasting Skill

    Science.gov (United States)

    1981-01-01

    for the third and fourth day precipitation forecasts. A marked improvement was shown for the consensus 24 hour precipitation forecast, and small... Zuckerberg (1980) found a small long term skill increase in forecasts of heavy snow events for nine eastern cities. Other National Weather Service...and maximum temperature) are each awarded marks 2, 1, or 0 according to whether the forecast is correct, 8 - *- -**■*- ———"—- - -■ t0m 1 MM—IB I

  5. 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.

  6. Magnetogram Forecast: An All-Clear Space Weather Forecasting System

    Science.gov (United States)

    Barghouty, Nasser; Falconer, David

    2015-01-01

    Solar flares and coronal mass ejections (CMEs) are the drivers of severe space weather. Forecasting the probability of their occurrence is critical in improving space weather forecasts. The National Oceanic and Atmospheric Administration (NOAA) currently uses the McIntosh active region category system, in which each active region on the disk is assigned to one of 60 categories, and uses the historical flare rates of that category to make an initial forecast that can then be adjusted by the NOAA forecaster. Flares and CMEs are caused by the sudden release of energy from the coronal magnetic field by magnetic reconnection. It is believed that the rate of flare and CME occurrence in an active region is correlated with the free energy of an active region. While the free energy cannot be measured directly with present observations, proxies of the free energy can instead be used to characterize the relative free energy of an active region. The Magnetogram Forecast (MAG4) (output is available at the Community Coordinated Modeling Center) was conceived and designed to be a databased, all-clear forecasting system to support the operational goals of NASA's Space Radiation Analysis Group. The MAG4 system automatically downloads nearreal- time line-of-sight Helioseismic and Magnetic Imager (HMI) magnetograms on the Solar Dynamics Observatory (SDO) satellite, identifies active regions on the solar disk, measures a free-energy proxy, and then applies forecasting curves to convert the free-energy proxy into predicted event rates for X-class flares, M- and X-class flares, CMEs, fast CMEs, and solar energetic particle events (SPEs). The forecast curves themselves are derived from a sample of 40,000 magnetograms from 1,300 active region samples, observed by the Solar and Heliospheric Observatory Michelson Doppler Imager. Figure 1 is an example of MAG4 visual output

  7. A Groundwater Model to Assess Water Resource Impacts at the Brenda Solar Energy Zone

    Energy Technology Data Exchange (ETDEWEB)

    Quinn, John [Argonne National Lab. (ANL), Argonne, IL (United States); Carr, Adrianne E. [Argonne National Lab. (ANL), Argonne, IL (United States); Greer, Chris [Argonne National Lab. (ANL), Argonne, IL (United States); Bowen, Esther E. [Argonne National Lab. (ANL), Argonne, IL (United States)

    2013-12-01

    The purpose of this study is to develop a groundwater flow model to examine the influence of potential groundwater withdrawal to support utility-scale solar energy development at the Brenda Solar Energy Zone (SEZ), as a part of the Bureau of Land Management’s (BLM’s) Solar Energy Program.

  8. A Groundwater Model to Assess Water Resource Impacts at the Imperial East Solar Energy Zone

    Energy Technology Data Exchange (ETDEWEB)

    Quinn, John [Argonne National Lab. (ANL), Argonne, IL (United States); Greer, Chris [Argonne National Lab. (ANL), Argonne, IL (United States); O' Connor, Ben L. [Argonne National Lab. (ANL), Argonne, IL (United States); Tompson, Andrew F.B. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

    2013-12-01

    The purpose of this study is to develop a groundwater flow model to examine the influence of potential groundwater withdrawal to support the utility-scale solar energy development at the Imperial East Solar Energy Zone (SEZ) as a part of the Bureau of Land Management’s (BLM) solar energy program.

  9. FORECASTING OF PRODUCTION OUTPUT FOR LIGHT INDUSTRY ENTERPRISES WITH PURPOSE TO DETERMINE THEIR POWER RESOURCES REQUIREMENTS. Part 2

    Directory of Open Access Journals (Sweden)

    V. N. Romaniuk

    2015-01-01

    Full Text Available The proposed paper is a continuation of the work, published in the previous issue. It is destined for specialists who are engaged in solution of problems pertaining to efficiency of light industry in Belarus which is considered as one of the branches being significantly involved in formation of national economy, its export potential and social climate. It is extremely relevant to reduce production costs in order to preserve and add strength to the branch positions in the markets. Production of natural, synthetic and knitted materials and their subsequent treatment are unreasonably power-consuming processes. Fundamental solution of the problem for reduction of production costs is to decrease its energy component and this can be achieved due to transition to modern heat and power systems in technological complexes of enterprises.The first part of the paper has considered a range of issues that served as a basis for obtaining statistical models in order to forecast the required production volume of main knitted and textile materials.  Forecasting has been prepared   for a period of more than 20 years because it has been predetermined by development horizon of comprehensive modernization of energy supply systems at enterprises of light industry. The production volumes essentially differ from those that have been stipulated in the programs of light industry.The present paper shows selection of statistical game-type for determination of  build-up rate of the production volume for every textile enterprise. The so-called “Game with Nature” has been selected as a game which is widely used for solution of analogous demand problems. The statistical forecasting models of production obtained in the first part are applied in the second part of the paper which considers strategies for development of individual enterprises. JSC :Baranovichi Industrial Cotton Association” has been taken an example in the paper. Production volumes of any enterprise are

  10. Atmospheric Mining in the Outer Solar System: Resource Capturing, Exploration, and Exploitation

    Science.gov (United States)

    Palaszewski, Bryan

    2015-01-01

    Atmospheric mining in the outer solar system (AMOSS) has been investigated as a means of fuel production for high-energy propulsion and power. Fusion fuels such as helium 3 (He-3) and hydrogen can be wrested from the atmospheres of Uranus and Neptune and either returned to Earth or used in-situ for energy production. 3He and hydrogen (deuterium, etc.) were the primary gases of interest, with hydrogen being the primary propellant for nuclear thermal solid core and gas core rocket-based atmospheric flight. A series of analyses were undertaken to investigate resource capturing aspects of AMOSS. These analyses included the gas capturing rate, storage options, and different methods of direct use of the captured gases. Additional supporting analyses were conducted to illuminate vehicle sizing and orbital transportation issues. While capturing 3He, large amounts of hydrogen and helium 4 (He-4) are produced. With these two additional gases, the potential exists for fueling small and large fleets of additional exploration and exploitation vehicles. Additional aerospacecraft or other aerial vehicles (UAVs, balloons, rockets, etc.) could fly through the outer-planet atmosphere to investigate cloud formation dynamics, global weather, localized storms or other disturbances, wind speeds, the poles, and so forth. Deep-diving aircraft (built with the strength to withstand many atmospheres of pressure) powered by the excess hydrogen or 4He may be designed to probe the higher density regions of the gas giants.

  11. Evaluation of conventional and high-performance routine solar radiation measurements for improved solar resource, climatological trends, and radiative modeling

    Energy Technology Data Exchange (ETDEWEB)

    Gueymard, Christian A. [Solar Consulting Services, P.O. Box 392, Colebrook, NH 03576 (United States); Myers, Daryl R. [National Renewable Energy Laboratory, 1617 Cole Blvd., Golden, CO 80401-3305 (United States)

    2009-02-15

    The solar renewable energy community depends on radiometric measurements and instrumentation for data to design and monitor solar energy systems, and develop and validate solar radiation models. This contribution evaluates the impact of instrument uncertainties contributing to data inaccuracies and their effect on short-term and long-term measurement series, and on radiation model validation studies. For the latter part, transposition (horizontal-to-tilt) models are used as an example. Confirming previous studies, it is found that a widely used pyranometer strongly underestimates diffuse and global radiation, particularly in winter, unless appropriate corrective measures are taken. Other types of measurement problems are also discussed, such as those involved in the indirect determination of direct or diffuse irradiance, and in shadowband correction methods. The sensitivity of the predictions from transposition models to inaccuracies in input radiation data is demonstrated. Caution is therefore issued to the whole community regarding drawing detailed conclusions about solar radiation data without due attention to the data quality issues only recently identified. (author)

  12. Sustainable Water Resources for Communities under Climate Change: Can State-of-the-Art Forecasting Inform Decision-Making in Data Sparse Regions?

    Science.gov (United States)

    Mayer, A.; Vivoni, E.; Halvorsen, K.; Robles-Morua, A.; Dana, K.; Che, D.; Mirchi, A.; Kossak, D.; Casteneda, M.

    2013-05-01

    In this project, we are studying decision-making for water resources management in anticipation of climate change in the Sonora River Basin, Mexico as a case study for the broader arid and semiarid southwestern North America. The goal of the proposed project is to determine whether water resources systems modeling, developed within a participatory framework, can contribute to the building of management strategies in a context of water scarcity, conflicting water uses and highly variable and changing climate conditions. The participatory modeling approach will be conducted through a series of three workshops, designed to encourage substantive participation from a broad range of actors, including representatives from federal and local government agencies, water use sectors, non-governmental organizations, and academics. Participants will guide the design of supply- and demand-side management strategies and selection of climate change and infrastructure management scenarios using state-of-the-art engineering tools. These tools include a water resources systems framework, a spatially-explicit hydrologic model, the use of forecasted climate scenarios under 21st century climate change, and observations obtained from field and satellite sensors. Through the theory of planned behavior, the participatory modeling process will be evaluated to understand if, and to what extent, the engineering tools are useful in the uncertain and politically-complex setting. Pre- and post-workshop surveys will be used in this evaluation. For this contribution, we present the results of the first collaborative modeling workshop that will be held in March 2013, where we will develop the initial modeling framework in collaboration with workshop participants.

  13. Renewable Resources: a national catalog of model projects. Volume 2. Mid-American Solar Energy Complex Region

    Energy Technology Data Exchange (ETDEWEB)

    None

    1980-07-01

    This compilation of diverse conservation and renewable energy projects across the United States was prepared through the enthusiastic participation of solar and alternate energy groups from every state and region. Compiled and edited by the Center for Renewable Resources, these projects reflect many levels of innovation and technical expertise. In many cases, a critique analysis is presented of how projects performed and of the institutional conditions associated with their success or failure. Some 2000 projects are included in this compilation; most have worked, some have not. Information about all is presented to aid learning from these experiences. The four volumes in this set are arranged in state sections by geographic region, coinciding with the four Regional Solar Energy Centers. The table of contents is organized by project category so that maximum cross-referencing may be obtained. This volume includes information on the Mid-American Solar Energy Complex Region. (WHK)

  14. 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

  15. Final Report for Annex II--Assessment of Solar Radiation Resources In Saudi Arabia, 1998-2000

    Energy Technology Data Exchange (ETDEWEB)

    Myers, D. R.; Wilcox, S. M.; Marion, W. F.; Al-Abbadi, N. M.; Mahfoodh, M.; Al-Otaibi, Z.

    2002-04-01

    The Final Report for Annex II - Assessment of Solar Radiation Resources in Saudi Arabia 1998-2000 summarizes the accomplishment of work performed, results achieved, and products produced under Annex II, a project established under the Agreement for Cooperation in the Field of Renewable Energy Research and Development between the Kingdom of Saudi Arabia and the United States. The report covers work and accomplishments from January 1998 to December 2000. A previous progress report, Progress Report for Annex II - Assessment of Solar Radiation Resources in Saudi Arabia 1993-1997, NREL/TP-560-29374, summarizes earlier work and technical transfer of information under the project. The work was performed in at the National Renewable Energy Laboratory (NREL) in Golden, Colorado, at the King Abdulaziz City for Science and Technology (KACST) in Riyadh, Saudi Arabia, and at selected weather stations of the Saudi Meteorological and Environmental Protection Administration (MEPA).

  16. Towards the intrahour forecasting of direct normal irradiance using sky-imaging data.

    Science.gov (United States)

    Nou, Julien; Chauvin, Rémi; Eynard, Julien; Thil, Stéphane; Grieu, Stéphane

    2018-04-01

    Increasing power plant efficiency through improved operation is key in the development of Concentrating Solar Power (CSP) technologies. To this end, one of the most challenging topics remains accurately forecasting the solar resource at a short-term horizon. Indeed, in CSP plants, production is directly impacted by both the availability and variability of the solar resource and, more specifically, by Direct Normal Irradiance (DNI). The present paper deals with a new approach to the intrahour forecasting (the forecast horizon [Formula: see text] is up to [Formula: see text] ahead) of DNI, taking advantage of the fact that this quantity can be split into two terms, i.e. clear-sky DNI and the clear sky index. Clear-sky DNI is forecasted from DNI measurements, using an empirical model (Ineichen and Perez, 2002) combined with a persistence of atmospheric turbidity. Moreover, in the framework of the CSPIMP (Concentrating Solar Power plant efficiency IMProvement) research project, PROMES-CNRS has developed a sky imager able to provide High Dynamic Range (HDR) images. So, regarding the clear-sky index, it is forecasted from sky-imaging data, using an Adaptive Network-based Fuzzy Inference System (ANFIS). A hybrid algorithm that takes inspiration from the classification algorithm proposed by Ghonima et al. (2012) when clear-sky anisotropy is known and from the hybrid thresholding algorithm proposed by Li et al. (2011) in the opposite case has been developed to the detection of clouds. Performance is evaluated via a comparative study in which persistence models - either a persistence of DNI or a persistence of the clear-sky index - are included. Preliminary results highlight that the proposed approach has the potential to outperform these models (both persistence models achieve similar performance) in terms of forecasting accuracy: over the test data used, RMSE (the Root Mean Square Error) is reduced of about [Formula: see text], with [Formula: see text], and [Formula: see

  17. Improved Modeling Tools Development for High Penetration Solar

    Energy Technology Data Exchange (ETDEWEB)

    Washom, Byron [Univ. of California, San Diego, CA (United States); Meagher, Kevin [Power Analytics Corporation, San Diego, CA (United States)

    2014-12-11

    One of the significant objectives of the High Penetration solar research is to help the DOE understand, anticipate, and minimize grid operation impacts as more solar resources are added to the electric power system. For Task 2.2, an effective, reliable approach to predicting solar energy availability for energy generation forecasts using the University of California, San Diego (UCSD) Sky Imager technology has been demonstrated. Granular cloud and ramp forecasts for the next 5 to 20 minutes over an area of 10 square miles were developed. Sky images taken every 30 seconds are processed to determine cloud locations and cloud motion vectors yielding future cloud shadow locations respective to distributed generation or utility solar power plants in the area. The performance of the method depends on cloud characteristics. On days with more advective cloud conditions, the developed method outperforms persistence forecasts by up to 30% (based on mean absolute error). On days with dynamic conditions, the method performs worse than persistence. Sky Imagers hold promise for ramp forecasting and ramp mitigation in conjunction with inverter controls and energy storage. The pre-commercial Sky Imager solar forecasting algorithm was documented with licensing information and was a Sunshot website highlight.

  18. Forecast of nuclear energetics

    Energy Technology Data Exchange (ETDEWEB)

    Sikora, W

    1976-01-01

    The forecast concerning the development of nuclear energetics is presented. Some information on economics of nuclear power plants is given. The nuclear fuel reserves are estimated on the background of power resources of the world. The safety and environment protection problems are mentioned.

  19. Climate Forecast System

    Science.gov (United States)

    Weather Service NWS logo - Click to go to the NWS home page Climate Forecast System Home News Organization Web portal to all Federal, state and local government Web resources and services. The NCEP Climate when using the CFS Reanalysis (CFSR) data. Saha, Suranjana, and Coauthors, 2010: The NCEP Climate

  20. Solar PV resource for higher penetration through a combined spatial aggregation with wind

    CSIR Research Space (South Africa)

    Bischof-Niemz, ST

    2016-06-01

    Full Text Available between wind and solar PV and how these would be reflected in the power system. The benefits of spatial distribution of renewables are well understood, but the impact of the combined spatial aggregation of wind and solar PV is central to the design...

  1. EnviroAtlas - Average Direct Normal Solar resources kWh/m2/Day by 12-Digit HUC for the Conterminous United States

    Data.gov (United States)

    U.S. Environmental Protection Agency — The annual average direct normal solar resources by 12-Digit Hydrologic Unit (HUC) was estimated from maps produced by the National Renewable Energy Laboratory for...

  2. A Solar Atlas for Building-Integrated Photovoltaic Electricity Resource Assessment

    DEFF Research Database (Denmark)

    Möller, Bernd; Nielsen, Steffen; Sperling, Karl

    While photovoltaic energy gathers momentum as power costs increase and panel costs decrease, the total technical and economic potentials for building integrated solar energy in Denmark remain largely unidentified. The current net metering feed-in scheme is restricted to 6kW plant size, limiting...... large scale application. This paper presents a solar atlas based on a high-resolution digital elevation model (DEM) of all 2.9 million buildings in the country, combined with a building register. The 1.6 m resolution DEM has been processed into global radiation input, solar energy output and production....... The continuous assessment of solar electricity generation potentials by marginal costs, ownership and plant type presented in the paper may be used for defining long term policies for the development of photovoltaic energy, as well as political instruments such as a multi-tier feed-in tariff....

  3. Toward evaluating the effect of climate change on investments in the water resources sector: insights from the forecast and analysis of hydrological indicators in developing countries

    International Nuclear Information System (INIS)

    Strzepek, Kenneth; Jacobsen, Michael; Boehlert, Brent; Neumann, James

    2013-01-01

    The World Bank has recently developed a method to evaluate the effects of climate change on six hydrological indicators across 8951 basins of the world. The indicators are designed for decision-makers and stakeholders to consider climate risk when planning water resources and related infrastructure investments. Analysis of these hydrological indicators shows that, on average, mean annual runoff will decline in southern Europe; most of Africa; and in southern North America and most of Central and South America. Mean reference crop water deficit, on the other hand, combines temperature and precipitation and is anticipated to increase in nearly all locations globally due to rising global temperatures, with the most dramatic increases projected to occur in southern Europe, southeastern Asia, and parts of South America. These results suggest overall guidance on which regions to focus water infrastructure solutions that could address future runoff flow uncertainty. Most important, we find that uncertainty in projections of mean annual runoff and high runoff events is higher in poorer countries, and increases over time. Uncertainty increases over time for all income categories, but basins in the lower and lower-middle income categories are forecast to experience dramatically higher increases in uncertainty relative to those in the upper-middle and upper income categories. The enhanced understanding of the uncertainty of climate projections for the water sector that this work provides strongly support the adoption of rigorous approaches to infrastructure design under uncertainty, as well as design that incorporates a high degree of flexibility, in response to both risk of damage and opportunity to exploit water supply ‘windfalls’ that might result, but would require smart infrastructure investments to manage to the greatest benefit. (letter)

  4. Toward evaluating the effect of climate change on investments in the water resources sector: insights from the forecast and analysis of hydrological indicators in developing countries

    Science.gov (United States)

    Strzepek, Kenneth; Jacobsen, Michael; Boehlert, Brent; Neumann, James

    2013-12-01

    The World Bank has recently developed a method to evaluate the effects of climate change on six hydrological indicators across 8951 basins of the world. The indicators are designed for decision-makers and stakeholders to consider climate risk when planning water resources and related infrastructure investments. Analysis of these hydrological indicators shows that, on average, mean annual runoff will decline in southern Europe; most of Africa; and in southern North America and most of Central and South America. Mean reference crop water deficit, on the other hand, combines temperature and precipitation and is anticipated to increase in nearly all locations globally due to rising global temperatures, with the most dramatic increases projected to occur in southern Europe, southeastern Asia, and parts of South America. These results suggest overall guidance on which regions to focus water infrastructure solutions that could address future runoff flow uncertainty. Most important, we find that uncertainty in projections of mean annual runoff and high runoff events is higher in poorer countries, and increases over time. Uncertainty increases over time for all income categories, but basins in the lower and lower-middle income categories are forecast to experience dramatically higher increases in uncertainty relative to those in the upper-middle and upper income categories. The enhanced understanding of the uncertainty of climate projections for the water sector that this work provides strongly support the adoption of rigorous approaches to infrastructure design under uncertainty, as well as design that incorporates a high degree of flexibility, in response to both risk of damage and opportunity to exploit water supply ‘windfalls’ that might result, but would require smart infrastructure investments to manage to the greatest benefit.

  5. 7 CFR 612.7 - Forecast user responsibility.

    Science.gov (United States)

    2010-01-01

    ... 7 Agriculture 6 2010-01-01 2010-01-01 false Forecast user responsibility. 612.7 Section 612.7 Agriculture Regulations of the Department of Agriculture (Continued) NATURAL RESOURCES CONSERVATION SERVICE, DEPARTMENT OF AGRICULTURE CONSERVATION OPERATIONS SNOW SURVEYS AND WATER SUPPLY FORECASTS § 612.7 Forecast user responsibility. The forecast use...

  6. 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...

  7. Strategic Forecasting

    DEFF Research Database (Denmark)

    Duus, Henrik Johannsen

    2016-01-01

    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...... 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...

  8. Meteorological Forecasting for renewable energy plants. A case study of two energy plants in Spain

    OpenAIRE

    López, Andrés Robalino; Mena-Nieto, Ángel

    2015-01-01

    Energy resources are the engines that drive every economy [1], [4], [14], Therefore, it is necessary to develop their exploitation in a friendlier, environmentally and sustainable way indeed it is a critically needed nowadays. Then, it is necessary to improve efficiency and optimize renewable energy in order that replace polluting energy sources. This work aims to relate the use of forecasting on meteorological variables such as wind speed, wind direction, solar radiation, among others, obtai...

  9. Solar Energy.

    Science.gov (United States)

    Eaton, William W.

    Presented is the utilization of solar radiation as an energy resource principally for the production of electricity. Included are discussions of solar thermal conversion, photovoltic conversion, wind energy, and energy from ocean temperature differences. Future solar energy plans, the role of solar energy in plant and fossil fuel production, and…

  10. Dynamic SEP event probability forecasts

    Science.gov (United States)

    Kahler, S. W.; Ling, A.

    2015-10-01

    The forecasting of solar energetic particle (SEP) event probabilities at Earth has been based primarily on the estimates of magnetic free energy in active regions and on the observations of peak fluxes and fluences of large (≥ M2) solar X-ray flares. These forecasts are typically issued for the next 24 h or with no definite expiration time, which can be deficient for time-critical operations when no SEP event appears following a large X-ray flare. It is therefore important to decrease the event probability forecast with time as a SEP event fails to appear. We use the NOAA listing of major (≥10 pfu) SEP events from 1976 to 2014 to plot the delay times from X-ray peaks to SEP threshold onsets as a function of solar source longitude. An algorithm is derived to decrease the SEP event probabilities with time when no event is observed to reach the 10 pfu threshold. In addition, we use known SEP event size distributions to modify probability forecasts when SEP intensity increases occur below the 10 pfu event threshold. An algorithm to provide a dynamic SEP event forecast, Pd, for both situations of SEP intensities following a large flare is derived.

  11. Toward a Marine Ecological Forecasting System

    Science.gov (United States)

    2010-06-01

    coral bleaching , living resource distribution, and pathogen progression). An operational ecological forecasting system depends upon the assimilation of...space scales (e.g., harmful algal blooms, dissolved oxygen concentration (hypoxia), water quality/beach closures, coral bleaching , living resource...advance. Two beaches in Lake Michigan have been selected for initial implementation. Forecasting Coral Bleaching in relation to Ocean Temperatures

  12. Evaluation of solar radiation abundance and electricity production capacity for application and development of solar energy

    Energy Technology Data Exchange (ETDEWEB)

    Rahim, Mustamin [Department of Architecture, Khairun University, Ternate (Indonesia); Environmental and Renewable Energy Systems Division, Graduate School of Engineering, Gifu University (Japan); Yoshino, Jun; Yasuda, Takashi [Environmental and Renewable Energy Systems Division, Graduate School of Engineering, Gifu University (Japan)

    2012-07-01

    This study was undertaken to analyze solar radiation abundance to ascertain the potential of solar energy as an electrical energy resource. Local weather forecasting for predicting solar radiation is performed using a meteorological model MM5. The prediction results are compared with observed results obtained from the Japan Meteorological Agency for verification of the data accuracy. Results show that local weather forecasting has high accuracy. Prediction of solar radiation is similar with observation results. Monthly average values of solar radiation are sufficiently good during March–September. Electrical energy generated by photovoltaic cells is almost proportional to the solar radiation amount. Effects of clouds on solar radiation can be removed by monthly averaging. The balance between supply and demand of electricity can be estimated using a standard curve obtained from the temporal average. When the amount of solar radiation every hour with average of more than 100 km radius area does not yield the standard curve, we can estimate the system of storage and auxiliary power necessary based on the evaluated results of imbalance between supply and demand.

  13. 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...

  14. Atmospheric Mining in the Outer Solar System: Outer Planet In-Space Bases and Moon Bases for Resource Processing

    Science.gov (United States)

    Palaszewski, Bryan

    2017-01-01

    Atmospheric mining in the outer solar system has been investigated as a means of fuel production for high energy propulsion and power. Fusion fuels such as Helium 3 (3He) and deuterium can be wrested from the atmospheres of Uranus and Neptune and either returned to Earth or used in-situ for energy production. Helium 3 and deuterium were the primary gases of interest with hydrogen being the primary propellant for nuclear thermal solid core and gas core rocket-based atmospheric flight. A series of analyses were undertaken to investigate resource capturing aspects of atmospheric mining in the outer solar system. This included the gas capturing rate, storage options, and different methods of direct use of the captured gases. While capturing 3He, large amounts of hydrogen and 4He are produced. The propulsion and transportation requirements for all of the major moons of Uranus and Neptune are presented. Analyses of orbital transfer vehicles (OTVs), landers, factories, and the issues with in-situ resource utilization (ISRU) low gravity processing factories are included. Preliminary observations are presented on near-optimal selections of moon base orbital locations, OTV power levels, and OTV and lander rendezvous points. Several artificial gravity in-space base designs and orbital sites at Uranus and Neptune and the OTV requirements to support them are also addressed.

  15. Incorporating Forecast Uncertainty in Utility Control Center

    Energy Technology Data Exchange (ETDEWEB)

    Makarov, Yuri V.; Etingov, Pavel V.; Ma, Jian

    2014-07-09

    Uncertainties in forecasting the output of intermittent resources such as wind and solar generation, as well as system loads are not adequately reflected in existing industry-grade tools used for transmission system management, generation commitment, dispatch and market operation. There are other sources of uncertainty such as uninstructed deviations of conventional generators from their dispatch set points, generator forced outages and failures to start up, load drops, losses of major transmission facilities and frequency variation. These uncertainties can cause deviations from the system balance, which sometimes require inefficient and costly last minute solutions in the near real-time timeframe. This Chapter considers sources of uncertainty and variability, overall system uncertainty model, a possible plan for transition from deterministic to probabilistic methods in planning and operations, and two examples of uncertainty-based fools for grid operations.This chapter is based on work conducted at the Pacific Northwest National Laboratory (PNNL)

  16. Solar System Exploration Augmented by In-Situ Resource Utilization: Human Planetary Base Issues for Mercury and Saturn

    Science.gov (United States)

    Palaszewski, Bryan A.

    2017-01-01

    Human and robotic missions to Mercury and Saturn are presented and analyzed with a range of propulsion options. Historical studies of space exploration, planetary spacecraft, and astronomy, in-situ resource utilization (ISRU), and industrialization all point to the vastness of natural resources in the solar system. Advanced propulsion benefitted from these resources in many ways. While advanced propulsion systems were proposed in these historical studies, further investigation of nuclear options using high power nuclear thermal and nuclear pulse propulsion as well as advanced chemical propulsion can significantly enhance these scenarios. Updated analyses based on these historical visions are presented. Nuclear thermal propulsion and ISRU enhanced chemical propulsion landers are assessed for Mercury missions. At Saturn, nuclear pulse propulsion with alternate propellant feed systems and Saturn moon exploration with chemical propulsion and nuclear electric propulsion options are discussed. Issues with using in-situ resource utilization on Mercury missions are discussed. At Saturn, the best locations for exploration and the use of the moons Titan and Enceladus as central locations for Saturn moon exploration is assessed.

  17. A Hybrid Model for Forecasting Sales in Turkish Paint Industry

    OpenAIRE

    Alp Ustundag

    2009-01-01

    Sales forecasting is important for facilitating effective and efficient allocation of scarce resources. However, how to best model and forecast sales has been a long-standing issue. There is no best forecasting method that is applicable in all circumstances. Therefore, confidence in the accuracy of sales forecasts is achieved by corroborating the results using two or more methods. This paper proposes a hybrid forecasting model that uses an artificial intelligence method (AI) w...

  18. Multidisciplinary research program directed toward utilization of solar energy through bioconversion of renewable resources. Progress report

    Energy Technology Data Exchange (ETDEWEB)

    Finnerty, W. R.

    1976-07-01

    Progress is reported in four research areas of solar bioconversion. The first program deals with the genetic selection of superior trees, physiological basis of vigor, tissue culture, haploid cell lines, and somatic hybridization. The second deals with the physiology of paraquat-induced oleoresin biogenesis. Separate abstracts were prepared for the other two program areas: biochemical basis of paraquat-induced oleoresin production in pines and biochemistry of methanogenesis. (JSR)

  19. Allocating resources and products in multi-hybrid multi-cogeneration: What fractions of heat and power are renewable in hybrid fossil-solar CHP?

    International Nuclear Information System (INIS)

    Beretta, Gian Paolo; Iora, Paolo; Ghoniem, Ahmed F.

    2014-01-01

    A general method for the allocation of resources and products in multi-resource/multi-product facilities is developed with particular reference to the important two-resource/two-product case of hybrid fossil and solar/heat and power cogeneration. For a realistic case study, we show how the method allows to assess what fractions of the power and heat should be considered as produced from the solar resource and hence identified as renewable. In the present scenario where the hybridization of fossil power plants by solar-integration is gaining increasing attention, such assessment is of great importance in the fair and balanced development of local energy policies based on granting incentives to renewables resources. The paper extends to the case of two-resource/two-product hybrid cogeneration, as well as to general multi-resource/multi-generation, three of the allocation methods already available for single-resource/two-product cogeneration and for two-resource/single-product hybrid facilities, namely, the ExRR (Exergy-based Reversible-Reference) method, the SRSPR (Single Resource Separate Production Reference) method, and the STALPR (Self-Tuned-Average-Local-Productions-Reference) method. For the case study considered we show that, unless the SRSPR reference efficiencies are constantly updated, the differences between the STALPR and SRSPR methods become important as hybrid and cogeneration plants take up large shares of the local energy production portfolio. - Highlights: • How much of the heat and power in hybrid solar-fossil cogeneration are renewable? • We define and compare three allocation methods for hybrid cogeneration. • Classical and exergy allocation are based on prescribed reference efficiencies. • Adaptive allocation is based on the actual average efficiencies in the local area. • Differences among methods grow as hybrid CHP (heat and power cogeneration) gains large market fractions

  20. Practice of Meteorological Services in Turpan Solar Eco-City in China (Invited)

    Science.gov (United States)

    Shen, Y.; Chang, R.; He, X.; Jiang, Y.; Zhao, D.; Ma, J.

    2013-12-01

    Turpan Solar Eco-City is located in Gobi in Northwest China, which is one of the National New Energy Demonstration Urban. The city was planed and designed from October of 2008 and constructed from May of 2010, and the first phase of the project has been completed by October of 2013. Energy supply in Turpan Solar Eco-City is mainly from PV power, which is installed in all of the roof and the total capacity is 13.4MW. During the planning and designing of the city, and the running of the smart grid, meteorological services have played an important role. 1) Solar Energy Resource Assessment during Planning Phase. According to the observed data from meteorological stations in recent 30 years, solar energy resource was assessed and available PV power generation capacity was calculated. The results showed that PV power generation capacity is 1.3 times the power consumption, that is, solar energy resource in Turpan is rich. 2) Key Meteorological Parameters Determination for Architectural Design. A professional solar energy resource station was constructed and the observational items included Global Horizontal Irradiance, Inclined Total Solar Irradiance at 30 degree, Inclined Total Solar Irradiance at local latitude, and so on. According these measured data, the optical inclined angle for PV array was determined, that is, 30 degree. The results indicated that the annual irradiation on inclined plane with optimal angle is 1.4% higher than the inclined surface with latitude angle, and 23.16% higher than the horizontal plane. The diffuse ratio and annual variation of the solar elevation angle are two major factors that influence the irradiation on inclined plane. 3) Solar Energy Resource Forecast for Smart Grid. Weather Research Forecast (WRF) model was used to forecast the hourly solar radiation of future 72 hours and the measured irradiance data was used to forecast the minutely solar radiation of future 4 hours. The forecast results were submitted to smart grid and used to

  1. Solar energy

    Science.gov (United States)

    Rapp, D.

    1981-01-01

    The book opens with a review of the patterns of energy use and resources in the United States, and an exploration of the potential of solar energy to supply some of this energy in the future. This is followed by background material on solar geometry, solar intensities, flat plate collectors, and economics. Detailed attention is then given to a variety of solar units and systems, including domestic hot water systems, space heating systems, solar-assisted heat pumps, intermediate temperature collectors, space heating/cooling systems, concentrating collectors for high temperatures, storage systems, and solar total energy systems. Finally, rights to solar access are discussed.

  2. 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...

  3. Comparison of Hourly Solar Radiation from a Ground–Based Station, Remote Sensing and Weather Forecast Models at a Coastal Site of South Italy (Lamezia Terme)

    DEFF Research Database (Denmark)

    Feudo, Teresa Lo; Avolio, Elenio; Gullì, Daniel

    2015-01-01

    The solar radiation is a critical input parameter when working with solar energy and radiation dependent surface processes. In this study, we present preliminary results from an inter-comparison between hourly values from a pyranometer, MSG-SEVIRI sensor and two meso-scale models, WRF and RAMS, i...

  4. Forecasting of Processes in Complex Systems for Real-World Problems

    Czech Academy of Sciences Publication Activity Database

    Pelikán, Emil

    2014-01-01

    Roč. 24, č. 6 (2014), s. 567-589 ISSN 1210-0552 Institutional support: RVO:67985807 Keywords : complex systems * data assimilation * ensemble forecasting * forecasting * global solar radiation * judgmental forecasting * multimodel forecasting * pollution Subject RIV: IN - Informatics, Computer Science Impact factor: 0.479, year: 2014

  5. Reduction of solar photovoltaic resources due to air pollution in China.

    Science.gov (United States)

    Li, Xiaoyuan; Wagner, Fabian; Peng, Wei; Yang, Junnan; Mauzerall, Denise L

    2017-11-07

    Solar photovoltaic (PV) electricity generation is expanding rapidly in China, with total capacity projected to be 400 GW by 2030. However, severe aerosol pollution over China reduces solar radiation reaching the surface. We estimate the aerosol impact on solar PV electricity generation at the provincial and regional grid levels in China. Our approach is to examine the 12-year (2003-2014) average reduction in point-of-array irradiance (POAI) caused by aerosols in the atmosphere. We apply satellite-derived surface irradiance data from the NASA Clouds and the Earth's Radiant Energy System (CERES) with a PV performance model (PVLIB-Python) to calculate the impact of aerosols and clouds on POAI. Our findings reveal that aerosols over northern and eastern China, the most polluted regions, reduce annual average POAI by up to 1.5 kWh/m 2 per day relative to pollution-free conditions, a decrease of up to 35%. Annual average reductions of POAI over both northern and eastern China are about 20-25%. We also evaluate the seasonal variability of the impact and find that aerosols in this region are as important as clouds in winter. Furthermore, we find that aerosols decrease electricity output of tracking PV systems more than those with fixed arrays: over eastern China, POAI is reduced by 21% for fixed systems at optimal angle and 34% for two-axis tracking systems. We conclude that PV system performance in northern and eastern China will benefit from improvements in air quality and will facilitate that improvement by providing emission-free electricity. Published under the PNAS license.

  6. Nanostructured Organic Solar Cells

    DEFF Research Database (Denmark)

    Radziwon, Michal Jędrzej; Rubahn, Horst-Günter; Madsen, Morten

    Recent forecasts for alternative energy generation predict emerging importance of supporting state of art photovoltaic solar cells with their organic equivalents. Despite their significantly lower efficiency, number of application niches are suitable for organic solar cells. This work reveals...... the principles of bulk heterojunction organic solar cells fabrication as well as summarises major differences in physics of their operation....

  7. Production of solar radiation bankable datasets from high-resolution solar irradiance derived with dynamical downscaling Numerical Weather prediction model

    Directory of Open Access Journals (Sweden)

    Yassine Charabi

    2016-11-01

    Full Text Available A bankable solar radiation database is required for the financial viability of solar energy project. Accurate estimation of solar energy resources in a country is very important for proper siting, sizing and life cycle cost analysis of solar energy systems. During the last decade an important progress has been made to develop multiple solar irradiance database (Global Horizontal Irradiance (GHI and Direct Normal Irradiance (DNI, using satellite of different resolution and sophisticated models. This paper assesses the performance of High-resolution solar irradiance derived with dynamical downscaling Numerical Weather Prediction model with, GIS topographical solar radiation model, satellite data and ground measurements, for the production of bankable solar radiation datasets. For this investigation, NWP model namely Consortium for Small-scale Modeling (COSMO is used for the dynamical downscaling of solar radiation. The obtained results increase confidence in solar radiation data base obtained from dynamical downscaled NWP model. The mean bias of dynamical downscaled NWP model is small, on the order of a few percents for GHI, and it could be ranked as a bankable datasets. Fortunately, these data are usually archived in the meteorological department and gives a good idea of the hourly, monthly, and annual incident energy. Such short time-interval data are valuable in designing and operating the solar energy facility. The advantage of the NWP model is that it can be used for solar radiation forecast since it can estimate the weather condition within the next 72–120 hours. This gives a reasonable estimation of the solar radiation that in turns can be used to forecast the electric power generation by the solar power plant.

  8. Ensemble forecasting using sequential aggregation for photovoltaic power applications

    International Nuclear Information System (INIS)

    Thorey, Jean

    2017-01-01

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

  9. Exploring the interactions between forecast accuracy, risk perception and perceived forecast reliability in reservoir operator's decision to use forecast

    Science.gov (United States)

    Shafiee-Jood, M.; Cai, X.

    2017-12-01

    Advances in streamflow forecasts at different time scales offer a promise for proactive flood management and improved risk management. Despite the huge potential, previous studies have found that water resources managers are often not willing to incorporate streamflow forecasts information in decisions making, particularly in risky situations. While low accuracy of forecasts information is often cited as the main reason, some studies have found that implementation of streamflow forecasts sometimes is impeded by institutional obstacles and behavioral factors (e.g., risk perception). In fact, a seminal study by O'Connor et al. (2005) found that risk perception is the strongest determinant of forecast use while managers' perception about forecast reliability is not significant. In this study, we aim to address this issue again. However, instead of using survey data and regression analysis, we develop a theoretical framework to assess the user-perceived value of streamflow forecasts. The framework includes a novel behavioral component which incorporates both risk perception and perceived forecast reliability. The framework is then used in a hypothetical problem where reservoir operator should react to probabilistic flood forecasts with different reliabilities. The framework will allow us to explore the interactions among risk perception and perceived forecast reliability, and among the behavioral components and information accuracy. The findings will provide insights to improve the usability of flood forecasts information through better communication and education.

  10. Estimation of solar energy resources for low salinity water desalination in several regions of Russia

    Science.gov (United States)

    Tarasenko, A. B.; Kiseleva, S. V.; Shakun, V. P.; Gabderakhmanova, T. S.

    2018-01-01

    This paper focuses on estimation of demanded photovoltaic (PV) array areas and capital expenses to feed a reverse osmosis desalination unit (1 m3/day fresh water production rate). The investigation have been made for different climatic conditions of Russia using regional data on ground water salinity from different sources and empirical dependence of specific energy consumption on salinity and temperature. The most optimal results were obtained for Krasnodar, Volgograd, Crimea Republic and some other southern regions. Combination of salinity, temperature and solar radiation level there makes reverse osmosis coupled with photovoltaics very attractive to solve infrastructure problems in rural areas. Estimation results are represented as maps showing PV array areas and capital expenses for selected regions.

  11. Improved Forecasts of Solar Particle Events using Eruptive Event Generators based on Gibson-Low and Titov-Demoulin Magnetic Configurations, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — Radiation hazards constitute a serious risk to human and robotic space operations beyond Low-Earth orbit. Primary contributors to space radiation include Solar...

  12. Verification of Global Radiation Forecasts from the Ensemble Prediction System at DMI

    DEFF Research Database (Denmark)

    Lundholm, Sisse Camilla

    To comply with an increasing demand for sustainable energy sources, a solar heating unit is being developed at the Technical University of Denmark. To make optimal use — environmentally and economically —, this heating unit is equipped with an intelligent control system using forecasts of the heat...... consumption of the house and the amount of available solar energy. In order to make the most of this solar heating unit, accurate forecasts of the available solar radiation are esstential. However, because of its sensitivity to local meteorological conditions, the solar radiation received at the surface...... of the Earth can be highly fluctuating and challenging to forecast accurately. To comply with the accuracy requirements to forecasts of both global, direct, and diffuse radiation, the uncertainty of these forecasts is of interest. Forecast uncertainties can become accessible by running an ensemble of forecasts...

  13. Optimal electricity development by increasing solar resources in diesel-based micro grid of island society in Thailand

    Directory of Open Access Journals (Sweden)

    Prachuab Peerapong

    2017-11-01

    Full Text Available Isolated grid diesel-based systems have been a basic electricity system in islands in developing countries. Nevertheless, the increasing diesel price and the higher cost of diesel transport to a long distance to the remote islands make the diesel-based systems unsustainable. This study analyzes the viability to increase solar photovoltaic (PV resources in the existing diesel-based systems. The hybrid PV/diesel system is not only reducing the cost of electricity generation but also decreasing the harmful emissions from fossil fuels. This study uses net present cost (NPC to evaluate the optimum PV/diesel system configurations for installation in isolated island in Thailand. The results of analyses show that the optimal case PV/diesel system can decrease COE from $0.429/kWh to $0.374/kWh when compared to the existing diesel-based system and can decrease emissions both carbon dioxide of 796.61 tons/yr and other gases of 21.47 tons/yr. The hybrid PV/diesel system also reduces diesel fuel consumption of 302,510 liters per year as a result from an optimal of 41% PV resource shares in this system.

  14. The Development of a Long-Term, Continually Updated Global Solar Resource at 10 km Resolution: Preliminary Results From Test Processing and Continuing Plans

    Science.gov (United States)

    Stackhouse, P.; Perez, R.; Sengupta, M.; Knapp, K.; Cox, Stephen; Mikovitz, J. Colleen; Zhang, T.; Hemker, K.; Schlemmer, J.; Kivalov, S.

    2014-01-01

    Background: Considering the likelihood of global climatic weather pattern changes and the global competition for energy resources, there is an increasing need to provide improved and continuously updated global Earth surface solar resource information. Toward this end, a project was funded under the NASA Applied Science program involving the National Aeronautics and Space Administration (NASA) Langley Research Center (LaRC), National Renewable Energy Laboratory (NREL), the State University of New York/Albany (SUNY) and the NOAA National Climatic Data Center (NCDC) to provide NREL with a global long-term advanced global solar mapping production system for improved depiction of historical solar resources and variability and to provide a mechanism for continual updates of solar resource information. This new production system is made possible by the efforts of NOAA and NASA to completely reprocess the International Satellite Cloud Climatology Project (ISCCP) data set that provides satellite visible and infrared radiances together with retrieved cloud and surface properties on a 3-hourly basis beginning from July 1983. The old version of the ISCCP data provided this information for all the world TMs available geosynchronous satellite systems and NOAA TMs AVHRR data sets at a 30 km effective resolution. This new version aims to provide a new and improved satellite calibration at an effective 10 km resolution. Thus, working with SUNY, NASA will develop and test an improved production system that will enable NREL to continually update the Earth TM solar resource. Objective and Methods: In this presentation, we provide a general overview of this project together with samples of the new solar irradiance mapped data products and comparisons to surface measurements at various locations across the world. An assessment of the solar resource values relative to calibration uncertainty and assumptions are presented. Errors resulting assumptions in snow cover and background aerosol

  15. Long term vision on the use of the renewable energies in Mexico: Solar energy. First Part: Evaluation of the Solar Resource in Mexico (Annexe 6-I in 'A vision of year 2030 on the use of the renewable energies in Mexico'); Vision a largo plazo sobre la utilizacion de las energias renovables en Mexico: Energia solar. Primera Parte: Evaluacion del Recurso Solar en Mexico (Anexo 6-I en 'Una vision al 2030 de la utilizacion de las energias renovables en Mexico')

    Energy Technology Data Exchange (ETDEWEB)

    Estrada Gasca, Claudio A; Arancibia Bulnes, Camilo A; Dorantes Rodriguez, Ruben; Islas Samperio, Jorge; Muhlia Velasquez, Agustin [Universidad Nacional Autonoma de Mexico (Mexico)

    2005-08-15

    The application of the solar energy requires an evaluation of the solar resource. It is understood by evaluation the determination of the amount of solar energy available to be used in an application; from the point of view of the present applications it is advisable to distinguish two: the direct solar radiation and the diffuse solar radiation, that conform what it is known as the global solar radiation, or hemispheric. All the solar collectors have capacity to use the direct radiation, their capacity to use diffuse radiation depends on the concentration factor of the radiation that characterizes them. Another distinction that can be done is the measurement of different parts from the spectrum. It is not simple to predict the value of the solar radiation in a site or given moment, this has implications in the design of solar facilities, which are constructed to operate during a large number of years. [Spanish] La aplicacion de la energia solar requiere una evaluacion del recurso solar. Se entiende por evaluacion a la determinacion de la cantidad de energia solar disponible para ser utilizada en una aplicacion; desde el punto de vista de las aplicaciones actuales conviene distinguir dos: la radiacion solar directa y la radiacion solar difusa, que conforman lo que se conoce como la radiacion solar global, o hemisferica. Todos los colectores solares tienen capacidad de utilizar la radiacion directa, su capacidad de usar radiacion difusa depende del factor de concentracion de la radiacion que los caracteriza. Otra distincion que se puede hacer es la medicion de diferentes partes del espectro. No es sencillo predecir el valor de la radiacion solar en un sitio o momento dado, esto tiene implicaciones en el diseno de instalaciones solares, las cuales se construyen para operar durante un numero grande de anos.

  16. Investigations on forecast-based operating strategies for solar thermal power plants with integrated storage capacity; Untersuchungen zu vorhersagebasierten Betriebsstrategien fuer solarthermische Kraftwerke mit integrierter Speicherkapazitaet

    Energy Technology Data Exchange (ETDEWEB)

    Wittmann, Michael Karl

    2012-07-01

    This publication describes a method for scheduling the operation of a power plant storage. The purpose of operation scheduling is to determine the economically optimum yield achievable in the course of daily power plant operation. The optimum operation schedule for the storage is determined based on Dynamic Programming Algorithms. Besides its focus on operation scheduling the publication investigates the effects of imperfect weather and price forecasts on electricity production and thus on the operator's economic results. It assesses the current Spanish legislation as well as other incentive scenarios in terms of their impact on operators' feed-in behaviour.

  17. Study on Forecasting Method of Hour-to-hour Solar Radiation over Photovoltaic Power Generation Region of Caidamu Basin%柴达木光伏发电地区逐时太阳辐射预报方法研究

    Institute of Scientific and Technical Information of China (English)

    保广裕; 张景华; 钱有海; 当周卓玛; 杨莲

    2012-01-01

    Based on the conventional meteorological data of sunshine and surface temperature of 10 weather stations as well as the solar radiation data of Gangchai and Germu over Caidamu basin in 2005~2009,the impact of weather circulation situation and its impacting system upon solar energy photovoltaic power generation is analyzed.From the study of synoptic meteorology and statistics,a practical forecasting index and forecasting method is tracked out,and by which a dynamic forecasting method and forecasting service system of hour-to-hour solar radiation over the photovoltaic power generation region of Caidamu basin is established.%本文利用2005~2009年柴达木盆地10个气象站的日照、地面温度等常规气象资料以及刚察和格尔木的辐射资料,分析了柴达木盆地影响太阳能光伏发电高影响天气的环流形势与影响系统。从天气学和统计学方面进行了研究,探索出了实用的预报指标和预报方法,建立了柴达木盆地太阳能光伏地区逐时太阳辐射动态预报方法和预报服务系统。

  18. Climate Prediction Center - Outlooks: CFS Forecast of Seasonal Climate

    Science.gov (United States)

    National Weather Service NWS logo - Click to go to the NWS home page Climate Prediction Center Home Site government Web resources and services. CFS Seasonal Climate Forecasts CFS Forecast of Seasonal Climate discontinued after October 2012. This page displays seasonal climate anomalies from the NCEP coupled forecast

  19. Integration of Behind-the-Meter PV Fleet Forecasts into Utility Grid System Operations

    Energy Technology Data Exchange (ETDEWEB)

    Hoff, Thomas Hoff [Clean Power Research, L.L.C., Napa, CA (United States); Kankiewicz, Adam [Clean Power Research, L.L.C., Napa, CA (United States)

    2016-02-26

    Four major research objectives were completed over the course of this study. Three of the objectives were to evaluate three, new, state-of-the-art solar irradiance forecasting models. The fourth objective was to improve the California Independent System Operator’s (ISO) load forecasts by integrating behind-the-meter (BTM) PV forecasts. The three, new, state-of-the-art solar irradiance forecasting models included: the infrared (IR) satellite-based cloud motion vector (CMV) model; the WRF-SolarCA model and variants; and the Optimized Deep Machine Learning (ODML)-training model. The first two forecasting models targeted known weaknesses in current operational solar forecasts. They were benchmarked against existing operational numerical weather prediction (NWP) forecasts, visible satellite CMV forecasts, and measured PV plant power production. IR CMV, WRF-SolarCA, and ODML-training forecasting models all improved the forecast to a significant degree. Improvements varied depending on time of day, cloudiness index, and geographic location. The fourth objective was to demonstrate that the California ISO’s load forecasts could be improved by integrating BTM PV forecasts. This objective represented the project’s most exciting and applicable gains. Operational BTM forecasts consisting of 200,000+ individual rooftop PV forecasts were delivered into the California ISO’s real-time automated load forecasting (ALFS) environment. They were then evaluated side-by-side with operational load forecasts with no BTM-treatment. Overall, ALFS-BTM day-ahead (DA) forecasts performed better than baseline ALFS forecasts when compared to actual load data. Specifically, ALFS-BTM DA forecasts were observed to have the largest reduction of error during the afternoon on cloudy days. Shorter term 30 minute-ahead ALFS-BTM forecasts were shown to have less error under all sky conditions, especially during the morning time periods when traditional load forecasts often experience their largest

  20. kosh 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...

  1. kpdt 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...

  2. kewr 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...

  3. kiso 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...

  4. kpga 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...

  5. kbkw 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. ktcl 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. pgwt 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. kpsp 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. kbih 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. kdnl 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. kart 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. kilm 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. kpne 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. kabi 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...

  15. ptpn Terminal Aerodrome Forecast

    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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