Short-term Forecasting of Photovoltaic Generation based on Conditioned Learning of Geopotential Fields

Renewable energy sources (RES) are characterized by high variability and limited predictability, mostly due to their dependence on meteorological factors. This variability presents technical and financial challenges for RES integration into grids and electricity markets. To address these challenges, it is important to accurately predict the future amount of energy production. In this paper we propose a spot statistical forecasting model for very short-term horizons (from a few minutes up to six hours ahead). This model is based on a combination of heterogeneous inputs with a conditioned learning approach. Spatio-temporal inputs (measurements from geographically distributed PV sites and satellite images) are used to enhance short-term predictability, while a weather analog approach enables adaptability to changes in meteorological conditions by considering the most relevant past observations. The performance evaluations are carried out on a case study featuring nine PV plants located in France, over a one-year period.