Skip to Main content Skip to Navigation
Journal articles

A New Approach for Satellite-Based Probabilistic Solar Forecasting with Cloud Motion Vectors

Abstract : Probabilistic solar forecasting is an issue of growing relevance for the integration of photovoltaic (PV) energy. However, for short-term applications, estimating the forecast uncertainty is challenging and usually delegated to statistical models. To address this limitation, the present work proposes an approach which combines physical and statistical foundations and leverages on satellite-derived clear-sky index (kc) and cloud motion vectors (CMV), both traditionally used for deterministic forecasting. The forecast uncertainty is estimated by using the CMV in a different way than the one generally used by standard CMV-based forecasting approach and by implementing an ensemble approach based on a Gaussian noise-adding step to both the kc and the CMV estimations. Using 15-min average ground-measured Global Horizontal Irradiance (GHI) data for two locations in France as reference, the proposed model shows to largely surpass the baseline probabilistic forecast Complete History Persistence Ensemble (CH-PeEn), reducing the Continuous Ranked Probability Score (CRPS) between 37% and 62%, depending on the forecast horizon. Results also show that this is mainly driven by improving the model’s sharpness, which was measured using the Prediction Interval Normalized Average Width (PINAW) metric.
Complete list of metadata
Contributor : Thierry Ranchin Connect in order to contact the contributor
Submitted on : Thursday, March 17, 2022 - 9:06:26 AM
Last modification on : Saturday, March 19, 2022 - 3:06:28 AM

Links full text



Thomas Carrière, Rodrigo Amaro E Silva, Fuqiang Zhuang, Yves-Marie Saint-Drenan, Philippe Blanc. A New Approach for Satellite-Based Probabilistic Solar Forecasting with Cloud Motion Vectors. Energies, MDPI, 2021, 14 (16), pp.4951. ⟨10.3390/en14164951⟩. ⟨hal-03611247⟩



Record views