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

doi:10.1016/j.solener.2018.02.006

CORC > 数学与系统科学研究院 > 中国科学院数学与系统科学研究院 > 系统科学研究所

	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
刊名	SOLAR ENERGY
	2018-03-15
卷号	163 页码:189-199
关键词	Solar radiation forecasting Decomposition-clustering-ensemble learning approach Ensemble empirical mode decomposition Least square support vector regression
ISSN号	0038-092X
DOI	10.1016/j.solener.2018.02.006
英文摘要	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.
资助项目	National Natural Science Foundation of China[71373262]
WOS研究方向	Energy & Fuels
语种	英语
出版者	PERGAMON-ELSEVIER SCIENCE LTD
WOS记录号	WOS:000430519400021
内容类型	期刊论文
源URL	[http://ir.amss.ac.cn/handle/2S8OKBNM/30173]
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.