UM
A Multi-Model Combination Approach for Probabilistic Wind Power Forecasting
Lin, You1,2; Yang, Ming1; Wan, Can2; Wang, Jianhui3; Song, Yonghua2,4
2019-01
Source PublicationIEEE TRANSACTIONS ON SUSTAINABLE ENERGY
ISSN1949-3029
Volume10Issue:1Pages:226-237
AbstractShort-term probabilistic wind power forecasting can provide critical quantified uncertainty information of wind generation for power system operation and control. It would be difficult to develop a universal forecasting model dominating over other alternative models because of the inherent stochastic nature of wind power. Therefore, a novel multi-model combination (MMC) approach for probabilistic wind power forecasting is proposed in this paper to exploit the advantages of different forecasting models. The proposed approach can combine different forecasting models those provide different kinds of probability density functions to improve the performance of probabilistic forecasting. Three probabilistic forecasting models based on the sparse Bayesian learning, kernel density estimation, and beta distribution fitting are used to form the combined model. The parameters of the MMC model are solved by two-step optimization. Comprehensive numerical studies illustrate the effectiveness of the proposed MMC approach.
KeywordTerms-Multi-model combination probabilistic forecasting wind power uncertainty
DOI10.1109/TSTE.2018.2831238
URLView the original
Indexed BySCI
Language英语
WOS Research AreaScience & Technology - Other Topics ; Energy & Fuels ; Engineering
WOS SubjectGREEN & SUSTAINABLE SCIENCE & TECHNOLOGY ; Energy & Fuels ; Engineering, Electrical & Electronic
WOS IDWOS:000454223400022
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
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Citation statistics
Cited Times [WOS]:4   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Affiliation1.Shandong Univ, Key Lab Power Syst Intelligent Dispatch & Control, Jinan 250061, Shandong, Peoples R China;
2.Zhejiang Univ, Coll Elect Engn, Hangzhou 310027, Peoples R China;
3.Southern Methodist Univ, Dept Elect Engn, Dallas, TX 75205 USA;
4.Univ Macau, Dept Elect & Comp Engn, Macau, Peoples R China
Recommended Citation
GB/T 7714
Lin, You,Yang, Ming,Wan, Can,et al. A Multi-Model Combination Approach for Probabilistic Wind Power Forecasting[J]. IEEE TRANSACTIONS ON SUSTAINABLE ENERGY,2019,10(1):226-237.
APA Lin, You,Yang, Ming,Wan, Can,Wang, Jianhui,&Song, Yonghua.(2019).A Multi-Model Combination Approach for Probabilistic Wind Power Forecasting.IEEE TRANSACTIONS ON SUSTAINABLE ENERGY,10(1),226-237.
MLA Lin, You,et al."A Multi-Model Combination Approach for Probabilistic Wind Power Forecasting".IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 10.1(2019):226-237.
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