Open Access

ARTICLE

Optimal Location to Use Solar Energy in an Urban Situation

by Ngakan Ketut Acwin Dwijendra^1,*, Indrajit Patra², N. Bharath Kumar³, Iskandar Muda⁴, Elsayed M. Tag El Din⁵

1 Faculty of Engineering, Udayana University, Bali, 80361, Indonesia
2 An Independent Researcher, NIT Durgapur, West Bengal, 700027, India
3 Department of Electrical and Electronics Engineering, Vignan’s Foundation for Science Technology and Research, Guntur, 522001, India
4 Department of Doctoral Program, Faculty Economic and Business, Universitas Sumatera Utara, Medan, 20222, Indonesia
5 Electrical Engineering Department, Faculty of Engineering & Technology, Future University in Egypt, New Cairo, 11835, Egypt

* Corresponding Author: Ngakan Ketut Acwin Dwijendra. Email:

Computers, Materials & Continua 2023, 75(1), 815-829. https://doi.org/10.32604/cmc.2023.034297

Received 13 July 2022; Accepted 03 September 2022; Issue published 06 February 2023

Abstract

This study conducted in Lima, Peru, a combination of spatial decision making system and machine learning was utilized to identify potential solar power plant construction sites within the city. Sundial measurements of solar radiation, precipitation, temperature, and altitude were collected for the study. Gene Expression Programming (GEP), which is based on the evolution of intelligent models, and Artificial Neural Networks (ANN) were both utilized in this investigation, and the results obtained from each were compared. Eighty percent of the data was utilized during the training phase, while the remaining twenty percent was utilized during the testing phase. On the basis of the findings, it was determined that the GEP is the most suitable network for predicting the location. The test state’s Nash-Sutcliffe efficiency (NSE) was 0.90, and its root-mean-square error (RMSE) was 0.04. Following the generation of the final map based on the results of the GEP model, it was determined that 9.2% of the province’s study area is suitable for the construction of photovoltaic solar power plants, while 53.5% is acceptable and 37.3% is unsuitable. The ANN model reveals that only 1.7% of the study area is suitable for the construction of photovoltaic solar power plants, while 66.8% is acceptable and 31.5% is unsuitable.

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Keywords

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