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

Mohammad Mehdi Lotfinejad, Reza Hafezi, Majid Khanali, Seyed Sina Hosseini, Mehdi Mehrpooya and Shahaboddin Shamshirband
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Mohammad Mehdi Lotfinejad: Department of Computer Engineering and Information Technology, Payame Noor University (PNU), Tehran 19395-4697, Iran
Reza Hafezi: Technology Foresight group, Department of Management, Science and Technology, Amirkabir University of Technology (Tehran Polytechnic), Tehran 15916-34311, Iran
Majid Khanali: Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj 4111, Iran
Seyed Sina Hosseini: Renewable Energies and Environment Department, Faculty of New Sciences and Technologies, University of Tehran, Tehran 14176-14418, Iran
Mehdi Mehrpooya: Renewable Energies and Environment Department, Faculty of New Sciences and Technologies, University of Tehran, Tehran 14176-14418, Iran
Shahaboddin Shamshirband: Department of Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh, Vietnam
Authors registered in the RePEc Author Service: Shahab S Band

Energies, 2018, vol. 11, issue 5, 1-15

Abstract: 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.

Keywords: solar radiation; prediction; artificial neural network; data mining; bat algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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