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

Hossein Moayedi,Amir Mosavi

doi:10.3390/en14041196

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.

Highlights

Today, solar energy (SE) is considered a promising renewable source because of its advantages, such as inexhaustible supply, environmental friendliness, universality, and high capacity [1,2]
Errori = solar irradiance (SIr) irecorded − SIr i predicted, the error of prediction for a total of N instances is calculated by the RMSE and mean absolute error (MAE) indices
The models use this data to evaluate their testing ability. In this of 18 regard, the SIr is forecasted for the testing instances and these values are compared10with the expected values

Summary

Introduction

Solar energy (SE) is considered a promising renewable source because of its advantages, such as inexhaustible supply, environmental friendliness, universality, and high capacity [1,2]. As a mimic of human brain operations, the artificial neural network (ANN) is founded on a set of algorithms aiming to recognize underlying correspondence among a group of input–output data [81,82,83,84,85]. The ANN represents a sophisticated nonlinear approach that has been proposed as a popular tool for Energies 2021, 14, 1196 the artificial neural network (ANN) is founded on a set of algorithms aiming to recognize underlying correspondence among a group of input–output data [81,82,83,84,85] In another sense, the ANN represents a sophisticated nonlinear approach that has been proposed as a popular tool fortasks different modeling tasksnotions [86].

Methods

Results

Conclusion