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Combined forecasting models for wind energy forecasting: A case study in China


  • Xiao, Ling
  • Wang, Jianzhou
  • Dong, Yao
  • Wu, Jie


As the energy crisis becomes a greater concern, wind energy, as one of the most promising renewable energy resources, becomes more widely used. Thus, wind energy forecasting plays an important role in wind energy utilization, especially wind speed forecasting, which is a vital component of wind energy management. In view of its importance, numerous wind speed forecasts have been proposed, each with advantages and disadvantages. Searching for more effective wind speed forecasts in wind energy management is a challenging task. As proposed, combined models have desirable forecasting abilities for wind speed. This paper reviewed the combined models for wind speed predictions and classified the combined wind speed forecasting approaches. To further study the combined models, two combination models, the no negative constraint theory (NNCT) combination model and the artificial intelligence algorithm combination model, are proposed. The hourly average wind speed data of three wind turbines in the Chengde region of China are used to illustrate the effectiveness of the proposed combination models, and the results show that the proposed combination models can always provide desirable forecasting results compared to the existing traditional combination models.

Suggested Citation

  • Xiao, Ling & Wang, Jianzhou & Dong, Yao & Wu, Jie, 2015. "Combined forecasting models for wind energy forecasting: A case study in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 44(C), pages 271-288.
  • Handle: RePEc:eee:rensus:v:44:y:2015:i:c:p:271-288
    DOI: 10.1016/j.rser.2014.12.012

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    References listed on IDEAS

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    1. Wang, Ce & Li, Bing-Bing & Liang, Qiao-Mei & Wang, Jin-Cheng, 2018. "Has China’s coal consumption already peaked? A demand-side analysis based on hybrid prediction models," Energy, Elsevier, vol. 162(C), pages 272-281.
    2. Xiao, Liye & Shao, Wei & Liang, Tulu & Wang, Chen, 2016. "A combined model based on multiple seasonal patterns and modified firefly algorithm for electrical load forecasting," Applied Energy, Elsevier, vol. 167(C), pages 135-153.
    3. Liu, Hui & Chen, Chao, 2019. "Multi-objective data-ensemble wind speed forecasting model with stacked sparse autoencoder and adaptive decomposition-based error correction," Applied Energy, Elsevier, vol. 254(C).
    4. Wu, Zhuochun & Xia, Xiangjie & Xiao, Liye & Liu, Yilin, 2020. "Combined model with secondary decomposition-model selection and sample selection for multi-step wind power forecasting," Applied Energy, Elsevier, vol. 261(C).
    5. Yang, Zhongshan & Wang, Jian, 2018. "A hybrid forecasting approach applied in wind speed forecasting based on a data processing strategy and an optimized artificial intelligence algorithm," Energy, Elsevier, vol. 160(C), pages 87-100.
    6. Yang, Zhongshan & Wang, Jian, 2018. "A combination forecasting approach applied in multistep wind speed forecasting based on a data processing strategy and an optimized artificial intelligence algorithm," Applied Energy, Elsevier, vol. 230(C), pages 1108-1125.
    7. Dong, Qingli & Sun, Yuhuan & Li, Peizhi, 2017. "A novel forecasting model based on a hybrid processing strategy and an optimized local linear fuzzy neural network to make wind power forecasting: A case study of wind farms in China," Renewable Energy, Elsevier, vol. 102(PA), pages 241-257.
    8. Shi, Rui-jing & Fan, Xiao-chao & He, Ying, 2017. "Comprehensive evaluation index system for wind power utilization levels in wind farms in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 461-471.
    9. He, Yongda & Lin, Boqiang, 2018. "Forecasting China's total energy demand and its structure using ADL-MIDAS model," Energy, Elsevier, vol. 151(C), pages 420-429.
    10. Li, Jingrui & Wang, Rui & Wang, Jianzhou & Li, Yifan, 2018. "Analysis and forecasting of the oil consumption in China based on combination models optimized by artificial intelligence algorithms," Energy, Elsevier, vol. 144(C), pages 243-264.
    11. Li, Hongmin & Wang, Jianzhou & Lu, Haiyan & Guo, Zhenhai, 2018. "Research and application of a combined model based on variable weight for short term wind speed forecasting," Renewable Energy, Elsevier, vol. 116(PA), pages 669-684.
    12. Xiao, Liye & Shao, Wei & Yu, Mengxia & Ma, Jing & Jin, Congjun, 2017. "Research and application of a combined model based on multi-objective optimization for electrical load forecasting," Energy, Elsevier, vol. 119(C), pages 1057-1074.
    13. Song, Jingjing & Wang, Jianzhou & Lu, Haiyan, 2018. "A novel combined model based on advanced optimization algorithm for short-term wind speed forecasting," Applied Energy, Elsevier, vol. 215(C), pages 643-658.
    14. Zhang, Yachao & Le, Jian & Liao, Xiaobing & Zheng, Feng & Li, Yinghai, 2019. "A novel combination forecasting model for wind power integrating least square support vector machine, deep belief network, singular spectrum analysis and locality-sensitive hashing," Energy, Elsevier, vol. 168(C), pages 558-572.
    15. Zheng, Chong Wei & Li, Chong Yin & Pan, Jing & Liu, Ming Yang & Xia, Lin Lin, 2016. "An overview of global ocean wind energy resource evaluations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 1240-1251.
    16. Zhang, Kequan & Qu, Zongxi & Dong, Yunxuan & Lu, Haiyan & Leng, Wennan & Wang, Jianzhou & Zhang, Wenyu, 2019. "Research on a combined model based on linear and nonlinear features - A case study of wind speed forecasting," Renewable Energy, Elsevier, vol. 130(C), pages 814-830.
    17. Wu, Zhuochun & Xiao, Liye, 2019. "A structure with density-weighted active learning-based model selection strategy and meteorological analysis for wind speed vector deterministic and probabilistic forecasting," Energy, Elsevier, vol. 183(C), pages 1178-1194.
    18. Qian, Zheng & Pei, Yan & Zareipour, Hamidreza & Chen, Niya, 2019. "A review and discussion of decomposition-based hybrid models for wind energy forecasting applications," Applied Energy, Elsevier, vol. 235(C), pages 939-953.
    19. Zheng, Chong Wei & Wang, Qing & Li, Chong Yin, 2017. "An overview of medium- to long-term predictions of global wave energy resources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 1492-1502.
    20. Feng, Cong & Cui, Mingjian & Hodge, Bri-Mathias & Zhang, Jie, 2017. "A data-driven multi-model methodology with deep feature selection for short-term wind forecasting," Applied Energy, Elsevier, vol. 190(C), pages 1245-1257.


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