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

文献类型：期刊论文

作者Sun, Shaolong1,2,3; Wang, Shouyang1,2,4; Zhang, Guowei1,2; Zheng, Jiali1,2
刊名SOLAR ENERGY
出版日期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
DOI10.1016/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
WOS记录号WOS:000430519400021
出版者PERGAMON-ELSEVIER SCIENCE LTD
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.

入库方式： OAI收割

来源：数学与系统科学研究院

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