Compressive Spatio-Temporal Forecasting of Meteorological Quantities and Photovoltaic Power

This paper presents a solar power forecasting scheme, which uses spatial and temporal time series data along with a photovoltaic (PV) power conversion model. The PV conver- sion model uses the forecast of three different variables, namely, irradiance on the tilted plane, ambient temperature, and wind speed, in order to estimate the power produced by a PV plant at the grid connection terminals. The forecast values are obtained using a spatio-temporal method that uses the data recorded from a target meteorological station as well as data of its surrounding stations. The proposed forecasting method exploits the sparsity of correlations between time series data in a collection of sta- tions. The performance of both the PV conversion model and the spatio-temporal algorithm is evaluated using high-resolution real data recorded in various locations in Italy. Comparison with other benchmark methods illustrates that the proposed method significantly improves the solar power forecasts, particularly over short-term horizons.