An Innovative Metaheuristic Strategy for Solar Energy Management through a Neural Networks Framework

Hossein Moayedi and Amir Mosavi
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Hossein Moayedi: Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
Amir Mosavi: Faculty of Civil Engineering, Technische Universität Dresden, 01069 Dresden, Germany

Energies, 2021, vol. 14, issue 4, 1-18

Abstract: Proper management of solar energy as an effective renewable source is of high importance toward sustainable energy harvesting. This paper offers a novel sophisticated method for predicting solar irradiance (SIr) from environmental conditions. To this end, an efficient metaheuristic technique, namely electromagnetic field optimization (EFO), is employed for optimizing a neural network. This algorithm quickly mines a publicly available dataset for nonlinearly tuning the network parameters. To suggest an optimal configuration, five influential parameters of the EFO are optimized by an extensive trial and error practice. Analyzing the results showed that the proposed model can learn the SIr pattern and predict it for unseen conditions with high accuracy. Furthermore, it provided about 10% and 16% higher accuracy compared to two benchmark optimizers, namely shuffled complex evolution and shuffled frog leaping algorithm. Hence, the EFO-supervised neural network can be a promising tool for the early prediction of SIr in practice. The findings of this research may shed light on the use of advanced intelligent models for efficient energy development.

Keywords: solar irradiance; solar energy; solar power; electrical power modeling; metaheuristic; machine learning; artificial neural networks; artificial intelligence; big data; deep learning; photovoltaic (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: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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