干旱区生态环境知识资源中心(Arid): Comparison of Implicit vs. Explicit Regime Identification in Machine Learning Methods for Solar Irradiance Prediction

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DOI	10.3390/en13030689
	Comparison of Implicit vs. Explicit Regime Identification in Machine Learning Methods for Solar Irradiance Prediction
	McCandless, Tyler; Dettling, Susan; Haupt, Sue Ellen
通讯作者	McCandless, Tyler
来源期刊	ENERGIES
EISSN	1996-1073
出版年	2020
卷号	13 期号:3
英文摘要	This work compares the solar power forecasting performance of tree-based methods that include implicit regime-based models to explicit regime separation methods that utilize both unsupervised and supervised machine learning techniques. Previous studies have shown an improvement utilizing a regime-based machine learning approach in a climate with diverse cloud conditions. This study compares the machine learning approaches for solar power prediction at the Shagaya Renewable Energy Park in Kuwait, which is in an arid desert climate characterized by abundant sunshine. The regime-dependent artificial neural network models undergo a comprehensive parameter and hyperparameter tuning analysis to minimize the prediction errors on a test dataset. The final results that compare the different methods are computed on an independent validation dataset. The results show that the tree-based methods, the regression model tree approach, performs better than the explicit regime-dependent approach. These results appear to be a function of the predominantly sunny conditions that limit the ability of an unsupervised technique to separate regimes for which the relationship between the predictors and the predictand would differ for the supervised learning technique.
英文关键词	solar power forecasting machine learning artificial intelligence regression tree artificial neural networks unsupervised learning supervised learning regime-identification
类型	Article
语种	英语
国家	USA
开放获取类型	Green Published, gold
收录类别	SCI-E
WOS记录号	WOS:000522489000182
WOS关键词	FORECASTING METHODS ; SYSTEM ; MODEL
WOS类目	Energy & Fuels
WOS研究方向	Energy & Fuels
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/314391
作者单位	Natl Ctr Atmospher Res, Boulder, CO 80305 USA
推荐引用方式 GB/T 7714	McCandless, Tyler,Dettling, Susan,Haupt, Sue Ellen. Comparison of Implicit vs. Explicit Regime Identification in Machine Learning Methods for Solar Irradiance Prediction[J],2020,13(3).
APA	McCandless, Tyler,Dettling, Susan,&Haupt, Sue Ellen.(2020).Comparison of Implicit vs. Explicit Regime Identification in Machine Learning Methods for Solar Irradiance Prediction.ENERGIES,13(3).
MLA	McCandless, Tyler,et al."Comparison of Implicit vs. Explicit Regime Identification in Machine Learning Methods for Solar Irradiance Prediction".ENERGIES 13.3(2020).