An innovative metaheuristic strategy for solar energy management through a neural networks framework

Research output: Contribution to journalResearch articleContributedpeer-review

Contributors

  • Hossein Moayedi - , Duy Tan University (Author)
  • Amir Mosavi - , TUD Dresden University of Technology, Norwegian University of Life Sciences, Óbuda University, Oxford Brookes University (OBU) (Author)

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.

Details

Original languageEnglish
Article number1196
JournalEnergies
Volume14
Issue number4
Publication statusPublished - 2 Feb 2021
Peer-reviewedYes

Keywords

Sustainable Development Goals

Keywords

  • Artificial intelligence, Artificial neural networks, Big data, Deep learning, Electrical power modeling, Machine learning, Metaheuristic, Photovoltaic, Solar energy, Solar irradiance, Solar power