Performance of Two Variable Machine Learning Models to Forecast Monthly Mean Diffuse Solar Radiation across India under Various Climate Zones

Jawed Mustafa (), Shahid Husain (), Saeed Alqaed, Uzair Ali Khan and Basharat Jamil
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Jawed Mustafa: Mechanical Engineering Department, College of Engineering, Najran University, P.O. Box 1988, Najran 61441, Saudi Arabia
Shahid Husain: Department of Mechanical Engineering, Zakir Husain College of Engineering and Technology, Aligarh Muslim University, Aligarh 202002, India
Saeed Alqaed: Mechanical Engineering Department, College of Engineering, Najran University, P.O. Box 1988, Najran 61441, Saudi Arabia
Uzair Ali Khan: Department of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan
Basharat Jamil: Computer Science and Statistics Department, Universidad Rey Juan Carlos, Mostoles, 28933 Madrid, Spain

Energies, 2022, vol. 15, issue 21, 1-32

Abstract: For the various climatic zones of India, machine learning (ML) models are created in the current work to forecast monthly-average diffuse solar radiation (DSR). The long-term solar radiation data are taken from Indian Meteorological Department (IMD), Pune, provided for 21 cities that span all of India’s climatic zones. The diffusion coefficient and diffuse fraction are the two groups of ML models with dual input parameters (sunshine ratio and clearness index) that are built and compared (each category has seven models). To create ML models, two well-known ML techniques, random forest (RF) and k-nearest neighbours (KNN), are used. The proposed ML models are compared with well-known models that are found in the literature. The ML models are ranked according to their overall and within predictive power using the Global Performance Indicator (GPI). It is discovered that KNN models generally outperform RF models. The results reveal that in diffusion coefficient models perform well than diffuse fraction models. Moreover, functional form 2 is the best followed by form 6. The ML models created here can be effectively used to accurately forecast DSR in various climates.

Keywords: machine learning; diffuse fraction; sunshine ratio; clearness index; diffusion coefficient (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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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