  Photovoltaic (PV) power generation has emerged as one of the lead renewable
energy sources. Yet, its production is characterized by high uncertainty, being
dependent on weather conditions like solar irradiance and temperature.
Predicting PV production, even in the 24-hour forecast, remains a challenge and
leads energy providers to left idling - often carbon emitting - plants. In this
paper, we introduce a Long-Term Recurrent Convolutional Network using Numerical
Weather Predictions (NWP) to predict, in turn, PV production in the 24-hour and
48-hour forecast horizons. This network architecture fully leverages both
temporal and spatial weather data, sampled over the whole geographical area of
interest. We train our model on an NWP dataset from the National Oceanic and
Atmospheric Administration (NOAA) to predict spatially aggregated PV production
in Germany. We compare its performance to the persistence model and
state-of-the-art methods.
