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A regime-based approach for integrating wind information in spatio-temporal solar forecasting models
Spatio-temporal solar forecasting based on statistical models seldom integrates wind information. An AutoRegressive with eXogenous input (ARX) model was tested using global horizontal irradiation records from a set of pyranometers deployed in Oahu, Hawaii, USA, where northeasterly winds are predominant. When irradiance is forecasted 10-s ahead, interesting forecast skills (up to 30.8%) can be achieved when a site has upwind neighbors available. However, when forecast skill is mapped as a function of wind direction at 850 hPa (from an ERA 5 reanalysis), negative skill values can be observed when nondominant winds occur. A wind regime-based approach is proposed, where different ARX models are built for different wind direction intervals, which substantially improves the forecasting accuracy for the underperforming wind directions. When the regime definition also takes into account wind speed, the ARX model detects spatial patterns for faster winds, with several nondominant directions achieving skill scores higher than 20%. Replacing the wind reanalysis by historical forecasts from ERA 5 reduced the overall skill by less than 0.1%.
A regime-based approach for integrating wind information in spatio-temporal solar forecasting models
Spatio-temporal solar forecasting based on statistical models seldom integrates wind information. An AutoRegressive with eXogenous input (ARX) model was tested using global horizontal irradiation records from a set of pyranometers deployed in Oahu, Hawaii, USA, where northeasterly winds are predominant. When irradiance is forecasted 10-s ahead, interesting forecast skills (up to 30.8%) can be achieved when a site has upwind neighbors available. However, when forecast skill is mapped as a function of wind direction at 850 hPa (from an ERA 5 reanalysis), negative skill values can be observed when nondominant winds occur. A wind regime-based approach is proposed, where different ARX models are built for different wind direction intervals, which substantially improves the forecasting accuracy for the underperforming wind directions. When the regime definition also takes into account wind speed, the ARX model detects spatial patterns for faster winds, with several nondominant directions achieving skill scores higher than 20%. Replacing the wind reanalysis by historical forecasts from ERA 5 reduced the overall skill by less than 0.1%.
A regime-based approach for integrating wind information in spatio-temporal solar forecasting models
Amaro e Silva, R. (author) / Haupt, S. E. (author) / Brito, M. C. (author)
2019-09-01
9 pages
Article (Journal)
Electronic Resource
English
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