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

Energies

Fuqiang Zhuang¹  Philippe Blanc¹  Yves-Marie Saint-Drenan¹  Rodrigo Amaro e Silva¹  Thomas Carrière² 

[1] O.I.E. Centre Observation, Impacts, Energy, MINES ParisTech, PSL Research University, 06904 Sophia Antipolis, France;SOLAÏS, 06560 Sophia Antipolis, France;

关键词: solar;    PV;    geostationary satellite;    cloud motion vector;    probabilistic forecast;   

DOI  :  10.3390/en14164951

来源: DOAJ

【 摘 要 】

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 ($k_c$) 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 $k_c$ 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.

【 授权许可】

Unknown