A Comparative Assessment of Predicting Daily Solar Radiation Using Bat Neural Network (BNN), Generalized Regression Neural Network (GRNN), and Neuro-Fuzzy (NF) System: A Case Study

Highly accurate estimating of daily solar radiation by developing an intelligent and robust model has been a subject of prominent concern for many researchers in the past few years. The precise prediction of solar radiation is of great interest and importance to improve the incorporation of solar po...

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Vydáno v:Energies (Basel) Ročník 11; číslo 5; s. 1188
Hlavní autoři: Lotfinejad, Mohammad Mehdi, Hafezi, Reza, Khanali, Majid, Hosseini, Seyed Sina, Mehrpooya, Mehdi, Shamshirband, Shahaboddin
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 08.05.2018
Témata:
artificial neural network
bat algorithm
data mining
Neural networks
prediction
solar radiation
Iran
ISSN:1996-1073, 1996-1073
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Shrnutí:Highly accurate estimating of daily solar radiation by developing an intelligent and robust model has been a subject of prominent concern for many researchers in the past few years. The precise prediction of solar radiation is of great interest and importance to improve the incorporation of solar power plants. In this study, a novel multilayer framework for a particular combination of the bat algorithm (BA) and neural networks (NN) is proposed, which is called bat neural network (BNN), aimed at predicting daily solar radiation over Iran. For appraising the performance of the proposed BNN, daily solar radiation data from four cities of Iran including Jask, Kermanshah, Ramsar, and Tehran are analyzed. The results indicate that among the tested models, BNN gains the best performance in the prediction of daily solar radiation. Among various soft computing approaches, the BA, which is inspired by the nature of microbats’ behaviour, has a significant impact on the optimization of this study.
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ISSN:1996-1073
1996-1073
DOI:10.3390/en11051188