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DOI	10.1016/j.energy.2021.120109
	Prediction of solar energy guided by pearson correlation using machine learning
	Jebli, Imane; Belouadha, Fatima-Zahra; Kabbaj, Mohammed Issam; Tilioua, Amine
通讯作者	Jebli, I (corresponding author), Mohammed V Univ Rabat, Mohammadia Sch Engineers, Comp Sci Dept, AMIPS Res Team,E3S Res Ctr, Ave Ibn Sina BP 765, Rabat 10090, Morocco.
来源期刊	ENERGY
ISSN	0360-5442
EISSN	1873-6785
出版年	2021
卷号	224
英文摘要	Solar energy forecasting represents a key element in increasing the competitiveness of solar power plants in the energy market and reducing the dependence on fossil fuels in economic and social development. This paper presents an approach for predicting solar energy, based on machine and deep learning techniques. The relevance of the studied models was evaluated for real-time and short-term solar energy forecasting to ensure optimized management and security requirements in this field while using an integral solution based on a single tool and an appropriate predictive model. The datasets we used in this study, represent data from 2016 to 2018 and are related to Errachidia which is a semi desert climate province in Morocco. Pearson correlation coefficient was deployed to identify the most relevant meteorological inputs from which the models should learn. RF and ANN have provided high accuracies against LR and SVR, which have reported very significant errors. ANN has shown good performance for both real-time and short-term predictions. The key findings were compared with Pirapora in Brazil, which is a tropical climate region, to show the quality and reproducibility of the study. (c) 2021 Elsevier Ltd. All rights reserved.
英文关键词	Solar energy prediction Machine and deep learning Linear regression Random forest Support vector regression
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000640521500013
WOS关键词	SUPPORT VECTOR MACHINE ; RADIATION ; MODEL ; OUTPUT ; OPTIMIZATION ; GENERATION
WOS类目	Thermodynamics ; Energy & Fuels
WOS研究方向	Thermodynamics ; Energy & Fuels
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/350077
作者单位	[Jebli, Imane; Belouadha, Fatima-Zahra; Kabbaj, Mohammed Issam] Mohammed V Univ Rabat, Mohammadia Sch Engineers, Comp Sci Dept, AMIPS Res Team,E3S Res Ctr, Ave Ibn Sina BP 765, Rabat 10090, Morocco; [Tilioua, Amine] Moulay Ismail Univ Meknes, Fac Sci & Tech Errachidia, Dept Phys, Res Team Thermal & Appl Thermodynam 2TA,Mech Ener, BP 509, Boutalamine, Errachidia, Morocco
推荐引用方式 GB/T 7714	Jebli, Imane,Belouadha, Fatima-Zahra,Kabbaj, Mohammed Issam,et al. Prediction of solar energy guided by pearson correlation using machine learning[J],2021,224.
APA	Jebli, Imane,Belouadha, Fatima-Zahra,Kabbaj, Mohammed Issam,&Tilioua, Amine.(2021).Prediction of solar energy guided by pearson correlation using machine learning.ENERGY,224.
MLA	Jebli, Imane,et al."Prediction of solar energy guided by pearson correlation using machine learning".ENERGY 224(2021).