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• W4307134494 endingPage "7851" @default.
• W4307134494 startingPage "7851" @default.
• W4307134494 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." @default.
• W4307134494 created "2022-10-28" @default.
• W4307134494 creator A5030660368 @default.
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• W4307134494 date "2022-10-23" @default.
• W4307134494 modified "2023-09-26" @default.
• W4307134494 title "Performance of Two Variable Machine Learning Models to Forecast Monthly Mean Diffuse Solar Radiation across India under Various Climate Zones" @default.
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• W4307134494 doi "https://doi.org/10.3390/en15217851" @default.
• W4307134494 hasPublicationYear "2022" @default.
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