Forecast analysis of hybrid renewable energies using deep learning: A case study with data from the Capricorn FLX 202004 weather station in Biskra, Algeria

The intermittent nature of renewable energy sources makes it extremely challenging to predict the quantity of energy produced. This is due to the continuous variation in weather conditions, including the amount of solar radiation at the production site, wind speed, temperature, rainfall, and other factors. In our work, we conducted an analytical study using a massive dateset collected by the Capricorn FLX 202004 weather station, which is affiliated with the Laboratory of Hydraulic and Hydrological Engineering and Water Resources Development at the University of Biskra, Algeria (LARHYSS). In this analytical study, we utilized deep learning methods , which has proven its ability in analyzing big data. We performed this process to facilitate the fieldwork carried out by companies interested in investing in clean electricity production in the region. Through this statistical weather forecasting technique, we provided valuable insights for researchers in the field of designing emerging energy production networks to identify the best locations for constructing hybrid power stations that combine solar and wind energy.