Direct Solar Radiation Prediction Based on Soft-Computing Algorithms Including Novel Predictive Atmospheric Variables

Salcedo‐Sanz, Sancho; Casanova-Mateo, C.; Pastor-Sánchez, A.; Gallo-Marazuela, D.; Labajo-Salazar, A.; Portilla-Figueras, Antonio

doi:10.1007/978-3-642-41278-3_39

Cited by 8 publications

(1 citation statement)

References 15 publications

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“…They used hybridization of linear regression and HS models with a temperature difference as input. Recently, numerous ML models were developed to predict GSR. Some of the ML techniques reported by researchers are support vector machines regression procedures, 25–27 various types of neural networks, 28,29 Gaussian Processes, 30 and hybrid methodologies, Mix of these and alternate procedures 31–33 . It has been observed in almost all the cases that ML techniques showed terrific results in these solar radiation estimation scenarios.…”

Section: Literature Reviewmentioning

confidence: 99%

Development of machine learning models based on air temperature for estimation of global solar radiation in India

Husain

Khan

2021

Env Prog and Sustain Energy

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The designing of solar thermal systems need accurate information on global solar radiation (GSR). In the present study, six machine learning models, for example, random forest, k‐nearest neighbors, Gaussian process regression, support vector machine, multilayer perception, and XGBoost, are developed for GSR prediction with only air temperature as input for different climatic zones of India. The performance of machine learning models is also compared with some well‐known empirical models. The results show that generally, the performance of the machine learning models is better than empirical models, though, for a few climatic zones, empirical models give a better prediction. The top‐performing models are k‐nearest neighbors and XGBoost. Thus, we highly recommend temperature‐based models to predict GSR in the regions of India where only air temperature data are available. The accurate information of future GSR can be easily obtained by combining future air temperature forecasts with KNN/XGBoost models. These models can be extremely helpful in designing solar thermal systems in those regions where solar radiation facility is not available.

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