Version 1
: Received: 3 January 2021 / Approved: 5 January 2021 / Online: 5 January 2021 (10:45:10 CET)
How to cite:
Moayedi, H.; Mosavi, A. An Innovative Metaheuristic Strategy for Solar Energy Management Through a Neural Framework. Preprints2021, 2021010075. https://doi.org/10.20944/preprints202101.0075.v1
Moayedi, H.; Mosavi, A. An Innovative Metaheuristic Strategy for Solar Energy Management Through a Neural Framework. Preprints 2021, 2021010075. https://doi.org/10.20944/preprints202101.0075.v1
Moayedi, H.; Mosavi, A. An Innovative Metaheuristic Strategy for Solar Energy Management Through a Neural Framework. Preprints2021, 2021010075. https://doi.org/10.20944/preprints202101.0075.v1
APA Style
Moayedi, H., & Mosavi, A. (2021). An Innovative Metaheuristic Strategy for Solar Energy Management Through a Neural Framework. Preprints. https://doi.org/10.20944/preprints202101.0075.v1
Chicago/Turabian Style
Moayedi, H. and Amir Mosavi. 2021 "An Innovative Metaheuristic Strategy for Solar Energy Management Through a Neural Framework" Preprints. https://doi.org/10.20944/preprints202101.0075.v1
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 non-linearly tuning the network parameters. To suggest an optimal configuration, five influential parameters of the EFO (i.e., NPop, R_rate, Ps_rate, P_field, and N_field) 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
Power plant; Electrical power modeling; Metaheuristic strategy; Water cycle algorithm
Subject
Engineering, Automotive Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
