This work presents a data-intensive solution to predict Photovoltaïque energy (PV) production. PV and other renewable sources have widely spread in recent years. Although those sources provide an environmentally-friendly solution, their integration is a real challenge in terms of power management as it depends on meteorological conditions. The ability to predict those variable sources considering meteorological uncertainty plays a key role in the management of the energy supply needs and reserves. This paper presents an easy-to-use methodology to predict PV production using time series analyses and sampling algorithms. The aim is to provide a forecasting model to set the day-ahead grid electricity need. This information useful for power dispatching plans and grid charge control. The main novelties of our approach is to provide an easy implemented and flexible solution that combines classification algorithms to predict the PV plant efficiency considering weather conditions and nonlinear regression to predict weather forecasted errors in order to improve prediction results. The results are based on the data collected in the Technople's micro grid in Sierre (Switzerland) described further in the paper. The best experimental results have been obtained using hourly historical weather measures (radiation and temperature) and PV production as training inputs and weather forecasted parameters as prediction inputs. Considering a 10 month dataset and despite the presence of 17 missing days, we achieved a Percentage Mean Absolute Deviation (PMAD) of 20% in August and 21% in September. Better results can be obtained with a larger dataset but as more historical data were not available, other months have not been tested.