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Wind speed forecasting based on variational mode decomposition and improved echo state network

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  • Hu, Huanling
  • Wang, Lin
  • Tao, Rui

Abstract

Accurate wind speed forecasting is conducive to power system operation, peak regulation, security analysis, and energy trading. This study proposes a hybrid model named VMD-DE-ESN incorporating variational mode decomposition (VMD) and differential evolution (DE) and echo state network (ESN) for wind speed forecasting. In the proposed model, VMD is applied to decompose the wind speed series to eliminate noise and mine the main features of original series, DE is utilized to optimize three important parameters of echo state network, and the improved ESN is used to forecast each decomposed subseries. The final forecasting results are obtained by summarizing the forecasting results of all subseries. To validate the accuracy and stability of the proposed model, four wind speed datasets collected from Sotavento wind farm in Galicia, northwestern Spain are used for forecasting. Mean absolute percentage errors of the proposed model in four datasets are 2.0161%, 3.4153%, 2.1544%, and 2.8478% respectively, which are much lower than those of nine comparative models. Therefore, the proposed model has satisfactory performance and is suitable for wind speed forecasting.

Suggested Citation

  • Hu, Huanling & Wang, Lin & Tao, Rui, 2021. "Wind speed forecasting based on variational mode decomposition and improved echo state network," Renewable Energy, Elsevier, vol. 164(C), pages 729-751.
    • Handle: RePEc:eee:renene:v:164:y:2021:i:c:p:729-751
    DOI: 10.1016/j.renene.2020.09.109
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    References listed on IDEAS

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    1. Guo, Zhenhai & Zhao, Weigang & Lu, Haiyan & Wang, Jianzhou, 2012. "Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model," Renewable Energy, Elsevier, vol. 37(1), pages 241-249.
    2. Kiplangat, Dennis C. & Asokan, K. & Kumar, K. Satheesh, 2016. "Improved week-ahead predictions of wind speed using simple linear models with wavelet decomposition," Renewable Energy, Elsevier, vol. 93(C), pages 38-44.
    3. Kavasseri, Rajesh G. & Seetharaman, Krithika, 2009. "Day-ahead wind speed forecasting using f-ARIMA models," Renewable Energy, Elsevier, vol. 34(5), pages 1388-1393.
    4. Arcos Jiménez, Alfredo & Zhang, Long & Gómez Muñoz, Carlos Quiterio & García Márquez, Fausto Pedro, 2020. "Maintenance management based on Machine Learning and nonlinear features in wind turbines," Renewable Energy, Elsevier, vol. 146(C), pages 316-328.
    5. Xiaomin Xu & Dongxiao Niu & Ming Fu & Huicong Xia & Han Wu, 2015. "A Multi Time Scale Wind Power Forecasting Model of a Chaotic Echo State Network Based on a Hybrid Algorithm of Particle Swarm Optimization and Tabu Search," Energies, MDPI, vol. 8(11), pages 1-21, November.
    6. 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.
    7. Qu, Zongxi & Mao, Wenqian & Zhang, Kequan & Zhang, Wenyu & Li, Zhipeng, 2019. "Multi-step wind speed forecasting based on a hybrid decomposition technique and an improved back-propagation neural network," Renewable Energy, Elsevier, vol. 133(C), pages 919-929.
    8. Wang, Long & Wang, Tongguang & Wu, Jianghai & Chen, Guoping, 2017. "Multi-objective differential evolution optimization based on uniform decomposition for wind turbine blade design," Energy, Elsevier, vol. 120(C), pages 346-361.
    9. Wang, Deyun & Luo, Hongyuan & Grunder, Olivier & Lin, Yanbing, 2017. "Multi-step ahead wind speed forecasting using an improved wavelet neural network combining variational mode decomposition and phase space reconstruction," Renewable Energy, Elsevier, vol. 113(C), pages 1345-1358.
    10. Hoolohan, Victoria & Tomlin, Alison S. & Cockerill, Timothy, 2018. "Improved near surface wind speed predictions using Gaussian process regression combined with numerical weather predictions and observed meteorological data," Renewable Energy, Elsevier, vol. 126(C), pages 1043-1054.
    11. Wang, Lin & Lv, Sheng-Xiang & Zeng, Yu-Rong, 2018. "Effective sparse adaboost method with ESN and FOA for industrial electricity consumption forecasting in China," Energy, Elsevier, vol. 155(C), pages 1013-1031.
    12. Tian, Chengshi & Hao, Yan & Hu, Jianming, 2018. "A novel wind speed forecasting system based on hybrid data preprocessing and multi-objective optimization," Applied Energy, Elsevier, vol. 231(C), pages 301-319.
    13. Liu, Da & Niu, Dongxiao & Wang, Hui & Fan, Leilei, 2014. "Short-term wind speed forecasting using wavelet transform and support vector machines optimized by genetic algorithm," Renewable Energy, Elsevier, vol. 62(C), pages 592-597.
    14. Cassola, Federico & Burlando, Massimiliano, 2012. "Wind speed and wind energy forecast through Kalman filtering of Numerical Weather Prediction model output," Applied Energy, Elsevier, vol. 99(C), pages 154-166.
    15. Hu, Huanling & Wang, Lin & Peng, Lu & Zeng, Yu-Rong, 2020. "Effective energy consumption forecasting using enhanced bagged echo state network," Energy, Elsevier, vol. 193(C).
    16. Li, Gong & Shi, Jing, 2010. "On comparing three artificial neural networks for wind speed forecasting," Applied Energy, Elsevier, vol. 87(7), pages 2313-2320, July.
    17. Liu, Hui & Tian, Hong-qi & Pan, Di-fu & Li, Yan-fei, 2013. "Forecasting models for wind speed using wavelet, wavelet packet, time series and Artificial Neural Networks," Applied Energy, Elsevier, vol. 107(C), pages 191-208.
    18. Hu, Huanling & Wang, Lin & Lv, Sheng-Xiang, 2020. "Forecasting energy consumption and wind power generation using deep echo state network," Renewable Energy, Elsevier, vol. 154(C), pages 598-613.
    19. Liu, Hui & Chen, Chao, 2019. "Data processing strategies in wind energy forecasting models and applications: A comprehensive review," Applied Energy, Elsevier, vol. 249(C), pages 392-408.
    20. Li, Chen & Zhu, Zhijie & Yang, Hufang & Li, Ranran, 2019. "An innovative hybrid system for wind speed forecasting based on fuzzy preprocessing scheme and multi-objective optimization," Energy, Elsevier, vol. 174(C), pages 1219-1237.
    21. Wang, Shouxiang & Zhang, Na & Wu, Lei & Wang, Yamin, 2016. "Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method," Renewable Energy, Elsevier, vol. 94(C), pages 629-636.
    22. Wang, Lin & Hu, Huanling & Ai, Xue-Yi & Liu, Hua, 2018. "Effective electricity energy consumption forecasting using echo state network improved by differential evolution algorithm," Energy, Elsevier, vol. 153(C), pages 801-815.
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