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

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.

Detection of COVID-19 severity using blood gas analysis parameters and Harris hawks optimized extreme learning machine

“…Since its introduction, it has been used in many situations, such as parameter estimation of photovoltaic models. It is a superior method compared to all other algorithms in recent years [ [33] , [34] , [35] , [36] ], such as the colony predation algorithm (CPA) [ 37 ]. HHO has the unique feature that Harris hawks can cooperate in groups to chase prey and adjust the chase pattern according to the dynamics of the situation and the escape pattern of the prey.…”

Predicting the Parameters of Vortex Bladeless Wind Turbine Using Deep Learning Method of Long Short-Term Memory

“…These values obtained from LSTM methods, presented in discussed figures, were compared with the results of the numerical investigations carried out by the CFD-FEM method. According to the comparative analyses performed for the important parameters in this research, it is possible to understand the performance of the artificial intelligence method by using the concept of root mean square error (RMSE) [22]. Five comparative relationships are explained here, as shown in Table 4.…”

“…The special type of the VBT 1 m high was studied in this research [7]. Not only does this special design lack any blades for a rotational movement, but it also has a mast part for oscillating in any direction [22,23]. The vortex shedding phenomenon exerts lift force, which causes the structure to fluctuate crosswise.…”

Multi-Threshold Image Segmentation of Maize Diseases Based on Elite Comprehensive Particle Swarm Optimization and Otsu

“…Mirjalili et al ( 2014 ) proposed the metaheuristic algorithm grey wolf optimization (GWO) in 2014, a variant of the PSO with a metaphor, as proven in the recent works (Villalón et al, 2020 ). Similar to other metaheuristic approaches (Ala et al, 2020 ; Seifi et al, 2020 ; Moayedi and Mosavi, 2021a , b ), the algorithm is inspired by the social hierarchy and hunting strategies of gray wildlife wolves and it has been applied to various problems due to its simple idea (Heidari and Pahlavani, 2017 ; Aljarah et al, 2019 ; Heidari et al, 2019 ; Tang et al, 2020 ). Regardless of its defect, we still can see some performance features in this method (Niu et al, 2019 ; Hu et al, 2021 ).…”