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A decomposition-clustering-ensemble learning approach for solar radiation forecasting
Sun, Shaolong1,2,3; Wang, Shouyang1,2,4; Zhang, Guowei1,2; Zheng, Jiali1,2
2018-03-15
Source PublicationSOLAR ENERGY
ISSN0038-092X
Volume163Pages:189-199
AbstractA 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.
KeywordSolar radiation forecasting Decomposition-clustering-ensemble learning approach Ensemble empirical mode decomposition Least square support vector regression
DOI10.1016/j.solener.2018.02.006
Language英语
Funding ProjectNational Natural Science Foundation of China[71373262]
WOS Research AreaEnergy & Fuels
WOS SubjectEnergy & Fuels
WOS IDWOS:000430519400021
PublisherPERGAMON-ELSEVIER SCIENCE LTD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/30174
Collection系统科学研究所
Affiliation1.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
Recommended Citation
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.
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