The Experts below are selected from a list of 243 Experts worldwide ranked by ideXlab platform
Carlos F.m. Coimbra - One of the best experts on this subject based on the ideXlab platform.
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net load forecasts for solar integrated operational grid feeders
Solar Energy, 2017Co-Authors: Yinghao Chu, Hugo T C Pedro, Jan Kleissl, Amanpreet Kaur, Carlos F.m. CoimbraAbstract:Abstract This work proposes forecast Models for solar-integrated, utility-scale feeders in the San Diego Gas & Electric operating region. The Models predict the net load for horizons ranging from 10 to 30 min. The forecasting methods implemented include hybrid methods based on Artificial Neural Network (ANN) and Support Vector Regression (SVR), which are both coupled with image processing methods for sky images. These methods are compared against reference Persistence methods. Three enhancement methods are implemented to further decrease forecasting error: (1) decomposing the time series of the net load to remove low-frequency load variation due to daily human activities; (2) segregating the Model training between daytime and nighttime; and (3) incorporating sky image features as exogenous inputs in the daytime forecasts. The ANN and SVR Models are trained and validated using six-month measurements of the net load and assessed using common statistic metrics: MBE, MAPE, rRMSE, and forecast skill, which is defined as the reduction of RMSE over the RMSE of reference Persistence Model. Results for the independent testing set show that data-driven Models, with the enhancement methods, significantly outperform the reference Persistence Model, achieving forecasting skills (improvement over reference Persistence Model) as large as 43% depending on location, solar penetration and forecast horizons.
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day ahead resource forecasting for concentrated solar power integration
Renewable Energy, 2016Co-Authors: Lukas Nonnenmacher, Amanpreet Kaur, Carlos F.m. CoimbraAbstract:In this work, we validate and enhance previously proposed singe-input direct normal irradiance (DNI) Models based on numerical weather prediction (NWP) for intra-week forecasts with over 200,000 hours of ground measurements for 8 locations. Short latency re-forecasting methods to enhance the deterministic forecast accuracies are presented and discussed. The basic forecast is applied to 15 additional locations in North America with satellite-derived DNI data. The basic Model outperforms the Persistence Model at all 23 locations with a skill between 12.4% and 38.2%. The RMSE of the basic forecast is in the range of 204.9 W m−2 to 309.9 W m−2. The implementation of stochastic learning re-forecasting methods yields further reduction in error from 204.9 W m−2 to 176.5 W m−2. To a great extent, the errors are caused by inaccuracies in the NWP cloud prediction. Improved assessment of atmospheric turbidity has limited impact on reducing forecast errors. Our results suggest that NWP-based DNI forecasts are very capable of reducing power and net-load uncertainty introduced by concentrated solar power plants at all locations in North America. Operating reserves to balance uncertainty in day-ahead schedules can be reduced on average by an estimated 28.6% through the application of the basic forecast.
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benefits of solar forecasting for energy imbalance markets
Renewable Energy, 2016Co-Authors: Amanpreet Kaur, Lukas Nonnenmacher, Hugo T C Pedro, Carlos F.m. CoimbraAbstract:Abstract Short term electricity trading to balance generation and demand provides an economic opportunity to integrate larger shares of variable renewable energy sources in the power grid. Recently, many regulatory market environments are reorganized to allow short term electricity trading. This study seeks to quantify the benefits of solar forecasting for energy imbalance markets (EIM). State-of-the-art solar forecasts, covering forecast horizons ranging from 24 h to 5 min are proposed and compared against the currently used benchmark Models, Persistence (P) and smart Persistence (SP). The implemented reforecast of numerical weather prediction time series achieves a skill of 14.5% over the smart Persistence Model. Using the proposed forecasts for a forecast horizon of up to 75 min for a single 1 MW power plant reduces required flexibility reserves by 21% and 16.14%, depending on the allowed trading intervals (5 and 15 min). The probability of an imbalance, caused through wrong market bids from PV solar plants, can be reduced by 19.65% and 15.12% (for 5 and 15 min trading intervals). All EIM stakeholders benefit from accurate forecasting. Previous estimates on the benefits of EIMs, based on Persistence Model are conservative. It is shown that the design variables regulating the market time lines, the bidding and the binding schedules, drive the benefits of forecasting.
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real time forecasting of solar irradiance ramps with smart image processing
Solar Energy, 2015Co-Authors: Yinghao Chu, Hugo T C Pedro, Carlos F.m. CoimbraAbstract:Abstract We develop a standalone, real-time solar forecasting computational platform to predict one minute averaged solar irradiance ramps ten minutes in advance. This platform integrates cloud tracking techniques using a low-cost fisheye network camera and artificial neural network (ANN) algorithms, where the former is used to introduce exogenous inputs and the latter is used to predict solar irradiance ramps. We train and validate the forecasting methodology with measured irradiance and sky imaging data collected for a six-month period, and apply it operationally to forecast both global horizontal irradiance and direct normal irradiance at two separate locations characterized by different micro-climates (coastal and continental) in California. The performance of the operational forecasts is assessed in terms of common statistical metrics, and also in terms of three proposed ramp metrics, used to assess the quality of ramp predictions. Results show that the forecasting platform proposed in this work outperforms the reference Persistence Model for both locations.
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Short-term reforecasting of power output from a 48 MWe solar PV plant
Solar Energy, 2015Co-Authors: Yinghao Chu, Seyyed M.i. Gohari, Bryan Urquhart, Hugo T C Pedro, Jan Kleissl, Carlos F.m. CoimbraAbstract:A smart, real-time reforecast method is applied to the intra-hour prediction of power generated by a 48 MWe photovoltaic (PV) plant. This reforecasting method is developed based on artificial neural network (ANN) optimization schemes and is employed to improve the performance of three baseline prediction Models: (1) a physical deterministic Model based on cloud tracking techniques; (2) an auto-regressive moving average (ARMA) Model; and (3) a k-th Nearest Neighbor (kNN) Model. Using the measured power data from the PV plant, the performance of all forecasts is assessed in terms of common error statistics (mean bias, mean absolute error and root mean square error) and forecast skill over the reference Persistence Model. With the reforecasting method, the forecast skills of the three baseline Models are significantly increased for time horizons of 5, 10, and 15. min. This study demonstrates the effectiveness of the optimized reforecasting method in reducing learnable errors produced by a diverse set of forecast methodologies.
J Tovarpescador - One of the best experts on this subject based on the ideXlab platform.
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evaluation of the wrf Model solar irradiance forecasts in andalusia southern spain
Solar Energy, 2012Co-Authors: V Larafanego, J A Ruizarias, D Pozovazquez, F J Santosalamillos, J TovarpescadorAbstract:Abstract In this work, we evaluate the reliability of three-days-ahead global horizontal irradiance (GHI) and direct normal irradiance (DNI) forecasts provided by the WRF mesoscale atmospheric Model for Andalusia (southern Spain). GHI forecasts were produced directly by the Model, while DNI forecasts were obtained based on a physical post-processing procedure using the WRF outputs and satellite retrievals. Hourly time resolution and 3 km spatial resolution estimates were tested against ground measurements collected at four radiometric stations along the years 2007 and 2008. The evaluation was carried out independently for different forecast horizons (1, 2 and 3 days ahead), the different seasons of the year and three different sky conditions: clear, cloudy and overcast. Results showed that the WRF Model presents considerable skill in forecasting both GHI and DNI, overall, better than a trivial Persistence Model. Nevertheless, both MBE and RMSE values presented a marked dependence on the sky conditions and season of the year. Particularly, for 24 h lead time, the MBE of the forecasted GHI was 2% for clear-skies and 18% for cloudy conditions. However, the MBE of the forecasted DNI increased up to about 10% and 75% for clear and cloudy conditions, respectively. Regarding RMSE values, in the case of forecasted GHI, results ranged from below 10% under clear-skies to 50% for cloudy conditions. In the case of forecasted DNI, RMSE ranged from 20% to 100% for clear and cloudy skies, respectively. This proved the higher sensitivity of DNI to the sky conditions. In general, an increment of the MBE and RMSE values with the cloudiness was observed. This reflects a still limited ability of the WRF Model to properly forecast cloudy conditions compared to clear skies. Nevertheless, the Model was able to accurately forecast steep changes in the sky (cloudiness) conditions. Finally, WRF performed considerable better than the Persistence Model for clear skies both for GHI and DNI, with relative RMSE values about a half. However, for cloudy conditions, performance was similar.
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evaluation of the wrf Model solar irradiance forecasts in andalusia southern spain
Solar Energy, 2012Co-Authors: V Larafanego, J A Ruizarias, D Pozovazquez, F J Santosalamillos, J TovarpescadorAbstract:Abstract In this work, we evaluate the reliability of three-days-ahead global horizontal irradiance (GHI) and direct normal irradiance (DNI) forecasts provided by the WRF mesoscale atmospheric Model for Andalusia (southern Spain). GHI forecasts were produced directly by the Model, while DNI forecasts were obtained based on a physical post-processing procedure using the WRF outputs and satellite retrievals. Hourly time resolution and 3 km spatial resolution estimates were tested against ground measurements collected at four radiometric stations along the years 2007 and 2008. The evaluation was carried out independently for different forecast horizons (1, 2 and 3 days ahead), the different seasons of the year and three different sky conditions: clear, cloudy and overcast. Results showed that the WRF Model presents considerable skill in forecasting both GHI and DNI, overall, better than a trivial Persistence Model. Nevertheless, both MBE and RMSE values presented a marked dependence on the sky conditions and season of the year. Particularly, for 24 h lead time, the MBE of the forecasted GHI was 2% for clear-skies and 18% for cloudy conditions. However, the MBE of the forecasted DNI increased up to about 10% and 75% for clear and cloudy conditions, respectively. Regarding RMSE values, in the case of forecasted GHI, results ranged from below 10% under clear-skies to 50% for cloudy conditions. In the case of forecasted DNI, RMSE ranged from 20% to 100% for clear and cloudy skies, respectively. This proved the higher sensitivity of DNI to the sky conditions. In general, an increment of the MBE and RMSE values with the cloudiness was observed. This reflects a still limited ability of the WRF Model to properly forecast cloudy conditions compared to clear skies. Nevertheless, the Model was able to accurately forecast steep changes in the sky (cloudiness) conditions. Finally, WRF performed considerable better than the Persistence Model for clear skies both for GHI and DNI, with relative RMSE values about a half. However, for cloudy conditions, performance was similar.
V Larafanego - One of the best experts on this subject based on the ideXlab platform.
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evaluation of the wrf Model solar irradiance forecasts in andalusia southern spain
Solar Energy, 2012Co-Authors: V Larafanego, J A Ruizarias, D Pozovazquez, F J Santosalamillos, J TovarpescadorAbstract:Abstract In this work, we evaluate the reliability of three-days-ahead global horizontal irradiance (GHI) and direct normal irradiance (DNI) forecasts provided by the WRF mesoscale atmospheric Model for Andalusia (southern Spain). GHI forecasts were produced directly by the Model, while DNI forecasts were obtained based on a physical post-processing procedure using the WRF outputs and satellite retrievals. Hourly time resolution and 3 km spatial resolution estimates were tested against ground measurements collected at four radiometric stations along the years 2007 and 2008. The evaluation was carried out independently for different forecast horizons (1, 2 and 3 days ahead), the different seasons of the year and three different sky conditions: clear, cloudy and overcast. Results showed that the WRF Model presents considerable skill in forecasting both GHI and DNI, overall, better than a trivial Persistence Model. Nevertheless, both MBE and RMSE values presented a marked dependence on the sky conditions and season of the year. Particularly, for 24 h lead time, the MBE of the forecasted GHI was 2% for clear-skies and 18% for cloudy conditions. However, the MBE of the forecasted DNI increased up to about 10% and 75% for clear and cloudy conditions, respectively. Regarding RMSE values, in the case of forecasted GHI, results ranged from below 10% under clear-skies to 50% for cloudy conditions. In the case of forecasted DNI, RMSE ranged from 20% to 100% for clear and cloudy skies, respectively. This proved the higher sensitivity of DNI to the sky conditions. In general, an increment of the MBE and RMSE values with the cloudiness was observed. This reflects a still limited ability of the WRF Model to properly forecast cloudy conditions compared to clear skies. Nevertheless, the Model was able to accurately forecast steep changes in the sky (cloudiness) conditions. Finally, WRF performed considerable better than the Persistence Model for clear skies both for GHI and DNI, with relative RMSE values about a half. However, for cloudy conditions, performance was similar.
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evaluation of the wrf Model solar irradiance forecasts in andalusia southern spain
Solar Energy, 2012Co-Authors: V Larafanego, J A Ruizarias, D Pozovazquez, F J Santosalamillos, J TovarpescadorAbstract:Abstract In this work, we evaluate the reliability of three-days-ahead global horizontal irradiance (GHI) and direct normal irradiance (DNI) forecasts provided by the WRF mesoscale atmospheric Model for Andalusia (southern Spain). GHI forecasts were produced directly by the Model, while DNI forecasts were obtained based on a physical post-processing procedure using the WRF outputs and satellite retrievals. Hourly time resolution and 3 km spatial resolution estimates were tested against ground measurements collected at four radiometric stations along the years 2007 and 2008. The evaluation was carried out independently for different forecast horizons (1, 2 and 3 days ahead), the different seasons of the year and three different sky conditions: clear, cloudy and overcast. Results showed that the WRF Model presents considerable skill in forecasting both GHI and DNI, overall, better than a trivial Persistence Model. Nevertheless, both MBE and RMSE values presented a marked dependence on the sky conditions and season of the year. Particularly, for 24 h lead time, the MBE of the forecasted GHI was 2% for clear-skies and 18% for cloudy conditions. However, the MBE of the forecasted DNI increased up to about 10% and 75% for clear and cloudy conditions, respectively. Regarding RMSE values, in the case of forecasted GHI, results ranged from below 10% under clear-skies to 50% for cloudy conditions. In the case of forecasted DNI, RMSE ranged from 20% to 100% for clear and cloudy skies, respectively. This proved the higher sensitivity of DNI to the sky conditions. In general, an increment of the MBE and RMSE values with the cloudiness was observed. This reflects a still limited ability of the WRF Model to properly forecast cloudy conditions compared to clear skies. Nevertheless, the Model was able to accurately forecast steep changes in the sky (cloudiness) conditions. Finally, WRF performed considerable better than the Persistence Model for clear skies both for GHI and DNI, with relative RMSE values about a half. However, for cloudy conditions, performance was similar.
Philippe Lauret - One of the best experts on this subject based on the ideXlab platform.
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A benchmarking of machine learning techniques for solar radiation forecasting in an insular context
Solar Energy, 2016Co-Authors: Philippe Lauret, Mathieu David, Ted Soubdhan, Cyril Voyant, Philippe PoggiAbstract:In this paper, we propose a benchmarking of supervised machine learning techniques (neural networks, Gaussian processes and support vector machines) in order to forecast the Global Horizontal solar Irradiance (GHI). We also include in this benchmark a simple linear autoregressive (AR) Model as well as two naive Models based on Persistence of the GHI and Persistence of the clear sky index (denoted herein scaled Persistence Model). The Models are calibrated and validated with data from three French islands: Corsica (41.91°N; 8.73°E), Guadeloupe (16.26°N; 61.51°W) and Reunion (21.34°S ; 55.49°E). The main findings of this work are, that for hour ahead solar forecasting, the machine learning techniques slightly improve the performances exhibited by the linear AR and the scaled Persistence Model. However, the improvement appears to be more pronounced in case of unstable sky conditions. These nonlinear techniques start to outperform their simple counterparts for forecasting horizons greater than one hour.
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use of satellite data to improve solar radiation forecasting with bayesian artificial neural networks
Solar Energy, 2015Co-Authors: Mazorra L Aguiar, Mathieu David, B Pereira, F Diaz, Philippe LauretAbstract:Solar forecasting has become an important issue for power systems planning and operating, especially in islands grids. Power generation and grid utilities need day ahead, intra-day and intra-hour Global Horizontal solar Irradiance (GHI) forecasts for operations. In this paper, we focus on intra-day solar forecasting with forecast horizons ranging from 1 h to 6 h ahead. An Artificial Neural Networks (ANN) Model is proposed to forecast GHI using ground measurement data and satellite data (from Helioclim-3) as inputs. In order to compare the forecasting results obtained by the proposed ANN Model, we also include in this work a simple nai¨ve Model, based on the Persistence of the clear sky index (smart Persistence Model), as well as another reference Model, the climatological mean Model. The Models were trained and tested for two ground measurements stations in Gran Canaria Island, Pozo (south) and Las Palmas (in the north). Firstly, ANN was trained and tested only with past ground measurement irradiance and compared by means of relative metrics with nai¨ve Models. While this first step led to better performances, forecasting skills were improved by including exogenous inputs to the Model by using GHI satellite data from surrounding area.
Krithika Seetharaman - One of the best experts on this subject based on the ideXlab platform.
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day ahead wind speed forecasting using f arima Models
Renewable Energy, 2009Co-Authors: Rajesh Kavasseri, Krithika SeetharamanAbstract:With the integration of wind energy into electricity grids, it is becoming increasingly important to obtain accurate wind speed/power forecasts. Accurate wind speed forecasts are necessary to schedule dispatchable generation and tariffs in the day-ahead electricity market. This paper examines the use of fractional-ARIMA or f-ARIMA Models to Model, and forecast wind speeds on the day-ahead (24h) and two-day-ahead (48h) horizons. The Models are applied to wind speed records obtained from four potential wind generation sites in North Dakota. The forecasted wind speeds are used in conjunction with the power curve of an operational (NEG MICON, 750kW) turbine to obtain corresponding forecasts of wind power production. The forecast errors in wind speed/power are analyzed and compared with the Persistence Model. Results indicate that significant improvements in forecasting accuracy are obtained with the proposed Models compared to the Persistence method.