Predicting Solar Radiation Using Optimized Generalized Regression Neural Network

Mohammad Ehteram (), Akram Seifi () and Fatemeh Barzegari Banadkooki ()
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Mohammad Ehteram: Semnan University, Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering
Akram Seifi: Vali-e-Asr University of Rafsanjan, Department of Water Science and Engineering, College of Agriculture
Fatemeh Barzegari Banadkooki: Payame Noor University, Agricultural Department

Chapter Chapter 16 in Application of Machine Learning Models in Agricultural and Meteorological Sciences, 2023, pp 163-174 from Springer

Abstract: Abstract One of the most important components of the hydrological cycle is solar radiation. Three stations in Iran were used to predict monthly solar radiation (SOR) using the optimized generalized regression neural network (GRNN). The Henry gas solubility optimization (HGSO), antlion optimization (ANO), and salp swarm algorithm (SSA) were used to adjust the parameters of the GRNN. Sunny hours had the highest correlation with SOR at all stations. Furthermore, the GRNN-HGSO model outperformed the other methods. At Mazandaran station, the median of observed data, GRNN-HGSO, GRNN-ANO, GRNN-SSA, and GRNN model was 19 MJ m−2, 19 MJ m−2, 19 MJ m−2, 21 MJ m−2, and 24 MJ m−2, respectively. In this study, soft computing models had a high ability to predict SOR in different climates. Using the models of the current study, decision-makers can identify the regions with the highest SRO. These regions are suitable for the construction of power plants.

Keywords: GRNN; Optimization algorithms; Solar radiation; Meteorological data (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-19-9733-4_16

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DOI: 10.1007/978-981-19-9733-4_16

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