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Hybrid model based on VMD decomposition, clustering analysis, long short memory network, ensemble learning and error complementation for short-term wind speed forecasting assisted by Flink platform

Author

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  • Sun, Zexian
  • Zhao, Mingyu
  • Zhao, Guohong

Abstract

Carving out the stochastic wind speed is still a challenge due to its intrinsic nature. With the wide spread of Internet of Things technology, the amount of data have presented explosive growth, thereby enhancing the difficulty of capturing its inherent characteristics. Therefore, the improvement of training efficiency requires keeping abreast of the forecasting accuracy and stability, which still has the large promotion space. These drawbacks motivate the propose of the hybrid model based on the variational mode decomposition(VMD), clustering analysis, LSTM network, stacking ensemble learning and error complementation for wind speed forecasting in which all the components are performed on Flink platform to ensure the forecasting efficiency. More specifically, the VMD module is employed to disintegrate the wind speed series into a primary trend and several fluctuate sub-series; Next, kmeans clustering and LSTM networks are conducted to deduce the latent characteristics of the primary trend and the stacking ensemble learning consisting of two stages is applied to infer the fluctuate abstractions of the other sub-series; Furthermore, the error complement is incorporated for assessing the error sequence created by the preliminary results. Finally, the experimental results have demonstrated that the proposed model exceeds the contrastive models on forecasting accuracy and efficiency.

Suggested Citation

  • Sun, Zexian & Zhao, Mingyu & Zhao, Guohong, 2022. "Hybrid model based on VMD decomposition, clustering analysis, long short memory network, ensemble learning and error complementation for short-term wind speed forecasting assisted by Flink platform," Energy, Elsevier, vol. 261(PB).
    • Handle: RePEc:eee:energy:v:261:y:2022:i:pb:s036054422202134x
    DOI: 10.1016/j.energy.2022.125248
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    References listed on IDEAS

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    1. Liu, Zhenkun & Jiang, Ping & Zhang, Lifang & Niu, Xinsong, 2020. "A combined forecasting model for time series: Application to short-term wind speed forecasting," Applied Energy, Elsevier, vol. 259(C).
    2. Caporin, Massimiliano & Preś, Juliusz, 2012. "Modelling and forecasting wind speed intensity for weather risk management," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3459-3476.
    3. 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.
    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. Wu, Zhuochun & Zhao, Xiaochen & Ma, Yuqing & Zhao, Xinyan, 2019. "A hybrid model based on modified multi-objective cuckoo search algorithm for short-term load forecasting," Applied Energy, Elsevier, vol. 237(C), pages 896-909.
    6. Hu, Jianming & Wang, Jianzhou & Ma, Kailiang, 2015. "A hybrid technique for short-term wind speed prediction," Energy, Elsevier, vol. 81(C), pages 563-574.
    7. Ke Zhang & Xiao Li & Jie Su, 2022. "Variable Support Segment-Based Short-Term Wind Speed Forecasting," Energies, MDPI, vol. 15(11), pages 1-18, June.
    8. Jiang, Ping & Wang, Yun & Wang, Jianzhou, 2017. "Short-term wind speed forecasting using a hybrid model," Energy, Elsevier, vol. 119(C), pages 561-577.
    9. Wang, Jianzhou & Wang, Shiqi & Yang, Wendong, 2019. "A novel non-linear combination system for short-term wind speed forecast," Renewable Energy, Elsevier, vol. 143(C), pages 1172-1192.
    10. Erdem, Ergin & Shi, Jing, 2011. "ARMA based approaches for forecasting the tuple of wind speed and direction," Applied Energy, Elsevier, vol. 88(4), pages 1405-1414, April.
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    Cited by:

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    2. Lv, Sheng-Xiang & Wang, Lin, 2023. "Multivariate wind speed forecasting based on multi-objective feature selection approach and hybrid deep learning model," Energy, Elsevier, vol. 263(PE).
    3. Wu, Zheng & Zhang, Yue & Dong, Ze, 2023. "Prediction of NOx emission concentration from coal-fired power plant based on joint knowledge and data driven," Energy, Elsevier, vol. 271(C).

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