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Renewable Energies (RES) penetration is progressing rapidly: in France, theinstalled capacity of photovoltaic (PV) power rose from 26MW in 2007 to 8GW in2017 [1]. Power generated by PV plants being highly dependent on variableweather conditions, this ever-growing pace is raising issues regarding gridstability and revenue optimization. To overcome these obstacles, PV forecastingbecame an area of intense research. In this paper, we propose a low complexityforecasting model able to operate with multiple heterogenous sources of data(power measurements, satellite images and Numerical Weather Predictions (NWP)).Being non-parametric, this model can be extended to include inputs. The mainstrength of the proposed model lies in its ability to automatically select theoptimal sources of data according to the desired forecast horizon (from 15minto 6h ahead) thanks to a feature selection procedure. To take advantage of thegrowing number of PV plants, a Spatio-Temporal (ST) approach is implemented.This approach considers the dependencies between spatially distributed plants.Each source has been studied incrementally so as to quantify their impact onforecast performances. This plurality of sources enhances the forecastingperformances up to 40% in terms of RMSE compared to a reference model. Theevaluation process is carried out on nine PV plants from the CompagnieNationale du Rh{\^o}ne (CNR).

Short-term photovoltaic generation forecasting using multiple heterogenous sources of data