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Super multi-step wind speed forecasting system with training set extension and horizontal–vertical integration neural network

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  • Liu, Ling
  • Wang, Jujie

Abstract

With the rapid development of wind energy utilization, people pay more and more attention to improving the accuracy of wind speed prediction. However, the unstable and nonlinear nature of wind speed makes it difficult to predict accurately. Due to the accumulation of prediction errors, the prediction steps in many studies are very short. In this paper, a novel super multi-step forecasting system with structure of ”data extraction - horizontal multi-output prediction - vertical integration prediction” is constructed to make 7-day, 15-day and 30-day wind speed prediction without updating the data source. A new mutual information matrix is designed to select the decomposition number of the singular spectrum analysis. Two different types of bidirectional long short-term memory network are constructed to realize horizontal–vertical integration prediction. A new training set extension method is applied to improve the generalization ability of the neural network. Three wind speed data sources and five comparison models are used to evaluate the performance of the proposed system. The empirical results illustrate that: (a) the maximum mean absolute errors of prediction results of three sites are 1.371 m/s, 0.567 m/s and 0.580 m/s, respectively, which shows the model has better prediction performance; (b) the mean absolute errors of the three sites increased by -0.050 m/s, 0.079 m/s and 0.113 m/s, respectively, which shows the proposed system has no obvious accumulative errors; (c) the proposed forecasting system shows better performance than the comparison models.

Suggested Citation

  • Liu, Ling & Wang, Jujie, 2021. "Super multi-step wind speed forecasting system with training set extension and horizontal–vertical integration neural network," Applied Energy, Elsevier, vol. 292(C).
    • Handle: RePEc:eee:appene:v:292:y:2021:i:c:s0306261921003895
    DOI: 10.1016/j.apenergy.2021.116908
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    as
    1. 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).
    2. Chen, Xue-Jun & Zhao, Jing & Jia, Xiao-Zhong & Li, Zhong-Long, 2021. "Multi-step wind speed forecast based on sample clustering and an optimized hybrid system," Renewable Energy, Elsevier, vol. 165(P1), pages 595-611.
    3. Cao, Qing & Ewing, Bradley T. & Thompson, Mark A., 2012. "Forecasting wind speed with recurrent neural networks," European Journal of Operational Research, Elsevier, vol. 221(1), pages 148-154.
    4. 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.
    5. Niu, Tong & Wang, Jianzhou & Zhang, Kequan & Du, Pei, 2018. "Multi-step-ahead wind speed forecasting based on optimal feature selection and a modified bat algorithm with the cognition strategy," Renewable Energy, Elsevier, vol. 118(C), pages 213-229.
    6. Wang, Cong & Zhang, Hongli & Ma, Ping, 2020. "Wind power forecasting based on singular spectrum analysis and a new hybrid Laguerre neural network," Applied Energy, Elsevier, vol. 259(C).
    7. Niu, Xinsong & Wang, Jiyang, 2019. "A combined model based on data preprocessing strategy and multi-objective optimization algorithm for short-term wind speed forecasting," Applied Energy, Elsevier, vol. 241(C), pages 519-539.
    8. Wang, Jujie & Li, Yaning, 2018. "Multi-step ahead wind speed prediction based on optimal feature extraction, long short term memory neural network and error correction strategy," Applied Energy, Elsevier, vol. 230(C), pages 429-443.
    9. Yang, Wendong & Wang, Jianzhou & Niu, Tong & Du, Pei, 2019. "A hybrid forecasting system based on a dual decomposition strategy and multi-objective optimization for electricity price forecasting," Applied Energy, Elsevier, vol. 235(C), pages 1205-1225.
    10. 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.
    11. 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.
    12. Liu, Yongqi & Qin, Hui & Zhang, Zhendong & Pei, Shaoqian & Jiang, Zhiqiang & Feng, Zhongkai & Zhou, Jianzhong, 2020. "Probabilistic spatiotemporal wind speed forecasting based on a variational Bayesian deep learning model," Applied Energy, Elsevier, vol. 260(C).
    13. Feng, Qianqian & Sun, Xiaolei & Hao, Jun & Li, Jianping, 2021. "Predictability dynamics of multifactor-influenced installed capacity: A perspective of country clustering," Energy, Elsevier, vol. 214(C).
    14. Hao, Yan & Tian, Chengshi, 2019. "A novel two-stage forecasting model based on error factor and ensemble method for multi-step wind power forecasting," Applied Energy, Elsevier, vol. 238(C), pages 368-383.
    15. Xu, Xiaomin & Niu, Dongxiao & Xiao, Bowen & Guo, Xiaodan & Zhang, Lihui & Wang, Keke, 2020. "Policy analysis for grid parity of wind power generation in China," Energy Policy, Elsevier, vol. 138(C).
    Full references (including those not matched with items on IDEAS)

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