Photovoltaic Energy Production Forecasting through Machine Learning Methods: A Scottish Solar Farm Case Study

L. Cabezón, L. G. B. Ruiz (), D. Criado-Ramón, E. J. Gago and M. C. Pegalajar
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L. Cabezón: Bluetab, IBM Company, 28020 Madrid, Spain
L. G. B. Ruiz: Department of Software Engineering, University of Granada, 18071 Granada, Spain
D. Criado-Ramón: Department of Computer Science and Artificial Intelligence, University of Granada, 18071 Granada, Spain
E. J. Gago: Engineering Construction and Project Management, School of Civil Engineering, University of Granada, 18071 Granada, Spain
M. C. Pegalajar: Department of Computer Science and Artificial Intelligence, University of Granada, 18071 Granada, Spain

Energies, 2022, vol. 15, issue 22, 1-14

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.

Keywords: photovoltaic energy; machine learning; energy forecasting; solar farm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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