A decomposition-clustering-ensemble learning approach for solar radiation forecasting

doi:10.1016/j.solener.2018.02.006

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	A decomposition-clustering-ensemble learning approach for solar radiation forecasting
	Sun, Shaolong 1,2,3; Wang, Shouyang1,2,4 ; Zhang, Guowei 1,2; Zheng, Jiali 1,2
	2018-03-15
发表期刊	SOLAR ENERGY
ISSN	0038-092X
卷号	163 页码:189-199
摘要	A decomposition-clustering-ensemble (DCE) learning approach is proposed for solar radiation forecasting in this paper. In the proposed DCE learning approach, (1) ensemble empirical mode decomposition (EEMD) is used to decompose the original solar radiation data into several intrinsic mode functions (IMFs) and a residual component; (2) least square support vector regression (LSSVR) is performed to forecast IMFs and residual component respectively with parameters optimized by gravitational search algorithm (GSA); (3) Kmeans method is adopted to cluster all component forecasting results; (4) another GSA-LSSVR method is applied to ensemble the component forecasts of each cluster and the final forecasting results are obtained by means of corresponding cluster's ensemble weights. To verify the performance of the proposed DCE learning approach, solar radiation data in Beijing is introduced for empirical analysis. The results of out-of-sample forecasting power show that the DCE learning approach produces smaller NRMSE, MAPE and better directional forecasts than all other benchmark models, reaching up to accuracy rate of 2.96%, 2.83% and 88.24% respectively in the one-day-ahead forecasting. This indicates that the proposed DCE learning approach is a relatively promising framework for forecasting solar radiation by means of level accuracy, directional accuracy and robustness.
关键词	Solar radiation forecasting Decomposition-clustering-ensemble learning approach Ensemble empirical mode decomposition Least square support vector regression
DOI	10.1016/j.solener.2018.02.006
语种	英语
资助项目	National Natural Science Foundation of China[71373262]
WOS研究方向	Energy & Fuels
WOS类目	Energy & Fuels
WOS记录号	WOS:000430519400021
出版者	PERGAMON-ELSEVIER SCIENCE LTD
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/30174
专题	系统科学研究所
通讯作者	Wang, Shouyang
作者单位	1.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Econ & Management, Beijing 100190, Peoples R China 3.City Univ Hong Kong, Dept Syst Engn & Engn Management, Tat Chee Ave, Kowloon, Hong Kong, Peoples R China 4.Chinese Acad Sci, Ctr Forecasting Sci, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Sun, Shaolong,Wang, Shouyang,Zhang, Guowei,et al. A decomposition-clustering-ensemble learning approach for solar radiation forecasting[J]. SOLAR ENERGY,2018,163:189-199.
APA	Sun, Shaolong,Wang, Shouyang,Zhang, Guowei,&Zheng, Jiali.(2018).A decomposition-clustering-ensemble learning approach for solar radiation forecasting.SOLAR ENERGY,163,189-199.
MLA	Sun, Shaolong,et al."A decomposition-clustering-ensemble learning approach for solar radiation forecasting".SOLAR ENERGY 163(2018):189-199.