DL[formula omitted]F: A Deep Learning model for the Local Forecasting of renewable sources

Abstract

This work presents the first version of DL2F, a powerful Deep Learning model specifically designed to forecast four essential weather variables that influence solar power potential: Global Horizontal Irradiance (GHI), temperature, atmospheric pressure, and relative humidity. The model uses a time series for each variable as input and the forecast provided by the well-known Fifth-Generation Mesoscale Model (MM5) system. Then, it generates new predictions for each variable and the subsequent 3 days. The suggested approach improves the MM5 system’s accuracy, effectively mitigating its tendency to misestimate real data in some specific seasons. In essence, DL2F is a regressor able to refine the forecasts generated by the MM5 system and improve its overall precision. The model’s architecture is based on a set of recurrent neural networks of the Gated Recurrent Unit (GRU) type.