  Renewable Energies (RES) penetration is progressing rapidly: in France, the
installed capacity of photovoltaic (PV) power rose from 26MW in 2007 to 8GW in
2017 [1]. Power generated by PV plants being highly dependent on variable
weather conditions, this ever-growing pace is raising issues regarding grid
stability and revenue optimization. To overcome these obstacles, PV forecasting
became an area of intense research. In this paper, we propose a low complexity
forecasting 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 main
strength of the proposed model lies in its ability to automatically select the
optimal sources of data according to the desired forecast horizon (from 15min
to 6h ahead) thanks to a feature selection procedure. To take advantage of the
growing 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 on
forecast performances. This plurality of sources enhances the forecasting
performances up to 40% in terms of RMSE compared to a reference model. The
evaluation process is carried out on nine PV plants from the Compagnie
Nationale du Rh{\^o}ne (CNR).
