Photovoltaic Energy Production Forecasting through Machine Learning Methods: A Scottish Solar Farm Case Study
L. Cabezón, L. G. B. Ruiz, D. Criado-Ramón, Eulalia Jadraque Gago, M.C. Pegalajar
Abstract
Photovoltaic solar energy is booming due to the continuous improvement in photovoltaic panel efficiency along with a downward trend in production costs. In addition, the European Union is committed to easing the implementation of renewable energy in many companies in order to obtain funding to install their own panels. Nonetheless, the nature of solar energy is intermittent and uncontrollable. This leads us to an uncertain scenario which may cause instability in photovoltaic systems. This research addresses this problem by implementing intelligent models to predict the production of solar energy. Real data from a solar farm in Scotland was utilized in this study. Finally, the models were able to accurately predict the energy to be produced in the next hour using historical information as predictor variables.