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Hybrid forecasting method for wind power integrating spatial correlation and corrected numerical weather prediction
Hu, Shuai1; Xiang, Yue1; Zhang, Hongcai2; Xie, Shanyi3; Li, Jianhua6; Gu, Chenghong4; Sun, Wei5; Liu, Junyong1
2021-07-01
Source PublicationApplied Energy
ISSN0306-2619
Volume293
Abstract

Wind power generation rapidly grows worldwide with declining costs and the pursuit of decarbonised energy systems. However, the utilization of wind energy remains challenging due to its strong stochastic nature. Accurate wind power forecasting is one of the effective ways to address this problem. Meteorological data are generally regarded as critical inputs for wind power forecasting. However, the direct use of numerical weather prediction in forecasting may not provide a high degree of accuracy due to unavoidable uncertainties, particularly for areas with complex topography. This study proposes a hybrid short-term wind power forecasting method, which integrates the corrected numerical weather prediction and spatial correlation into a Gaussian process. First, the Gaussian process model is built using the optimal combination of different kernel functions. Then, a correction model for the wind speed is designed by using an automatic relevance determination algorithm to correct the errors in the primary numerical weather prediction. Moreover, the spatial correlation of wind speed series between neighbouring wind farms is extracted to complement the input data. Finally, the modified numerical weather prediction and spatial correlation are incorporated into the hybrid model to enable reliable forecasting. The actual data in East China are used to demonstrate its performance. In comparison with the basic Gaussian process, in different seasons, the forecasting accuracy is improved by 7.02%–29.7% by using additional corrected numerical weather prediction, by 0.65–10.23% after integrating with the spatial correlation, and by 10.88–37.49% through using the proposed hybrid method.

KeywordGaussian Process Hybrid Model Kernel Function Numerical Weather Prediction Spatial Correlation Wind Power Forecasting
DOI10.1016/j.apenergy.2021.116951
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaEnergy & Fuels ; Engineering
WOS SubjectEnergy & Fuels ; Engineering, Chemical
WOS IDWOS:000649311400005
Scopus ID2-s2.0-85105832155
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Cited Times [WOS]:7   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionTHE STATE KEY LABORATORY OF INTERNET OF THINGS FOR SMART CITY (UNIVERSITY OF MACAU)
Corresponding AuthorXiang, Yue
Affiliation1.College of Electrical Engineering, Sichuan University, Chengdu, 610065, China
2.The State Key Laboratory of Internet of Things for Smart City and Department of Electrical and Computer Engineering, University of Macau, Macau, 999078, China
3.Electric Power Research Institute of Guangdong Power Grid Corporation, Guangzhou, 510080, China
4.Department of Electronic and Electrical Engineering, University of Bath, Bath, BA2 7AY, United Kingdom
5.School of Engineering, University of Edinburgh, Edinburgh, EH9 3DW, United Kingdom
6.Southwest Electric Power Design Institute Co., Ltd. Of China Power Engineering Consulting Group, 610021, China
Recommended Citation
GB/T 7714
Hu, Shuai,Xiang, Yue,Zhang, Hongcai,et al. Hybrid forecasting method for wind power integrating spatial correlation and corrected numerical weather prediction[J]. Applied Energy,2021,293.
APA Hu, Shuai,Xiang, Yue,Zhang, Hongcai,Xie, Shanyi,Li, Jianhua,Gu, Chenghong,Sun, Wei,&Liu, Junyong.(2021).Hybrid forecasting method for wind power integrating spatial correlation and corrected numerical weather prediction.Applied Energy,293.
MLA Hu, Shuai,et al."Hybrid forecasting method for wind power integrating spatial correlation and corrected numerical weather prediction".Applied Energy 293(2021).
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