Solar photovoltaic power prediction using different machine learning methods

The main aim of the present study is to explore the relationship between numerous input parameters and the solar photovoltaic (PV) power using machine learning (ML) models. Two different ML approaches such as support vector machine (SVM) and Gaussian process regression (GPR) were considered and comp...

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Autor principal:	Bouchaib Zazoum
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2022
Materias:	Solar energy PV panel Machine learning Support vector machine (SVM) Gaussian process regression (GPR) Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/d24baf40c5bc423898ed2234b4239df8
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id	oai:doaj.org-article:d24baf40c5bc423898ed2234b4239df8
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spelling	oai:doaj.org-article:d24baf40c5bc423898ed2234b4239df82021-12-04T04:35:12ZSolar photovoltaic power prediction using different machine learning methods2352-484710.1016/j.egyr.2021.11.183https://doaj.org/article/d24baf40c5bc423898ed2234b4239df82022-04-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2352484721013287https://doaj.org/toc/2352-4847The main aim of the present study is to explore the relationship between numerous input parameters and the solar photovoltaic (PV) power using machine learning (ML) models. Two different ML approaches such as support vector machine (SVM) and Gaussian process regression (GPR) were considered and compared. The basic input parameters including solar PV panel temperature, ambient temperature, solar flux, time of the day and relative humidity were considered for predicting the solar PV power. The results showed that among the proposed ML approaches, Matern 5/2 GPR algorithm provided the optimal performance; whereas cubic SVM had the worst performance. Furthermore, the predicted output results are in good agreement with the experimental values, indicating that the proposed ML approaches are appropriate for use in predicting the power of different solar PV panel. Additionally, to showcase the effectiveness and the accuracy of SVM and GPR models in forecasting solar PV power, the results of these models are compared using root mean squared error (RMSE) and mean absolute error (MAE) criteria.Bouchaib ZazoumElsevierarticleSolar energyPV panelMachine learningSupport vector machine (SVM)Gaussian process regression (GPR)Electrical engineering. Electronics. Nuclear engineeringTK1-9971ENEnergy Reports, Vol 8, Iss , Pp 19-25 (2022)
institution	DOAJ
collection	DOAJ
language	EN
topic	Solar energy PV panel Machine learning Support vector machine (SVM) Gaussian process regression (GPR) Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Solar energy PV panel Machine learning Support vector machine (SVM) Gaussian process regression (GPR) Electrical engineering. Electronics. Nuclear engineering TK1-9971 Bouchaib Zazoum Solar photovoltaic power prediction using different machine learning methods
description	The main aim of the present study is to explore the relationship between numerous input parameters and the solar photovoltaic (PV) power using machine learning (ML) models. Two different ML approaches such as support vector machine (SVM) and Gaussian process regression (GPR) were considered and compared. The basic input parameters including solar PV panel temperature, ambient temperature, solar flux, time of the day and relative humidity were considered for predicting the solar PV power. The results showed that among the proposed ML approaches, Matern 5/2 GPR algorithm provided the optimal performance; whereas cubic SVM had the worst performance. Furthermore, the predicted output results are in good agreement with the experimental values, indicating that the proposed ML approaches are appropriate for use in predicting the power of different solar PV panel. Additionally, to showcase the effectiveness and the accuracy of SVM and GPR models in forecasting solar PV power, the results of these models are compared using root mean squared error (RMSE) and mean absolute error (MAE) criteria.
format	article
author	Bouchaib Zazoum
author_facet	Bouchaib Zazoum
author_sort	Bouchaib Zazoum
title	Solar photovoltaic power prediction using different machine learning methods
title_short	Solar photovoltaic power prediction using different machine learning methods
title_full	Solar photovoltaic power prediction using different machine learning methods
title_fullStr	Solar photovoltaic power prediction using different machine learning methods
title_full_unstemmed	Solar photovoltaic power prediction using different machine learning methods
title_sort	solar photovoltaic power prediction using different machine learning methods
publisher	Elsevier
publishDate	2022
url	https://doaj.org/article/d24baf40c5bc423898ed2234b4239df8
work_keys_str_mv	AT bouchaibzazoum solarphotovoltaicpowerpredictionusingdifferentmachinelearningmethods
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Solar photovoltaic power prediction using different machine learning methods

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