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Hybrid model with secondary decomposition, randomforest algorithm, clustering analysis and long short memory network principal computing for short-term wind power forecasting on multiple scales

Author

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  • Sun, zexian
  • Zhao, mingyu
  • Dong, yan
  • Cao, xin
  • Sun, Hexu

Abstract

As the first prerequisite to carve out the increased exploration of the wind power generation and developments, accurate wind power prediction is sufficiently reliable to eliminate the dilemma caused by its intrinsic irregularity, intermittence and non-stationary. Therefore, the paper proposes the hybrid model composed of secondary decomposition, preliminary forecasting and error analysis, which can capture the fluctuation of the wind power series better, but also guarantee the forecasting stability simultaneously. More specifically, the secondary decomposition is developed to grasp the primary trend of a wind power series; Next, random forest algorithm, kmeans clustering and Long short term memory(LSTM) network are successfully employed to infer the latent characteristics of the decomposed modes as much as possible; For the sake of estimating the uncertainty associated with the preliminary results, the process based on LSTM network models the error sequences, of which the inherent information could be further mined. Then, the final predicted values are obtained by integrating the error sequences and preliminary results. Finally, the properties of the developed model are illustrated through wind power data from two wind farms. Besides, compared with the contrastive models, the proposed model presents 88.06%,96.35% improvements in terms of Mean Relative Error(MRE), Root Mean Square Error(RMSE) at most in the two cases, which demonstrates the superiority of the proposed model.

Suggested Citation

  • Sun, zexian & Zhao, mingyu & Dong, yan & Cao, xin & Sun, Hexu, 2021. "Hybrid model with secondary decomposition, randomforest algorithm, clustering analysis and long short memory network principal computing for short-term wind power forecasting on multiple scales," Energy, Elsevier, vol. 221(C).
    • Handle: RePEc:eee:energy:v:221:y:2021:i:c:s0360544221000979
    DOI: 10.1016/j.energy.2021.119848
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    References listed on IDEAS

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    Citations

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    7. Ai, Chunyu & He, Shan & Hu, Heng & Fan, Xiaochao & Wang, Weiqing, 2023. "Chaotic time series wind power interval prediction based on quadratic decomposition and intelligent optimization algorithm," Chaos, Solitons & Fractals, Elsevier, vol. 177(C).
    8. Wang, Fei & Chen, Peng & Zhen, Zhao & Yin, Rui & Cao, Chunmei & Zhang, Yagang & Duić, Neven, 2022. "Dynamic spatio-temporal correlation and hierarchical directed graph structure based ultra-short-term wind farm cluster power forecasting method," Applied Energy, Elsevier, vol. 323(C).
    9. Lin, Qingcheng & Cai, Huiling & Liu, Hanwei & Li, Xuefeng & Xiao, Hui, 2024. "A novel ultra-short-term wind power prediction model jointly driven by multiple algorithm optimization and adaptive selection," Energy, Elsevier, vol. 288(C).
    10. Zhang, Guowei & Zhang, Yi & Wang, Hui & Liu, Da & Cheng, Runkun & Yang, Di, 2024. "Short-term wind speed forecasting based on adaptive secondary decomposition and robust temporal convolutional network," Energy, Elsevier, vol. 288(C).
    11. Noman Khan & Fath U Min Ullah & Ijaz Ul Haq & Samee Ullah Khan & Mi Young Lee & Sung Wook Baik, 2021. "AB-Net: A Novel Deep Learning Assisted Framework for Renewable Energy Generation Forecasting," Mathematics, MDPI, vol. 9(19), pages 1-18, October.
    12. Wang, Xiaodi & Hao, Yan & Yang, Wendong, 2024. "Novel wind power ensemble forecasting system based on mixed-frequency modeling and interpretable base model selection strategy," Energy, Elsevier, vol. 297(C).
    13. Ye, Lin & Li, Yilin & Pei, Ming & Zhao, Yongning & Li, Zhuo & Lu, Peng, 2022. "A novel integrated method for short-term wind power forecasting based on fluctuation clustering and history matching," Applied Energy, Elsevier, vol. 327(C).
    14. Tang, Yugui & Zhang, Shujing & Zhang, Zhen, 2024. "A privacy-preserving framework integrating federated learning and transfer learning for wind power forecasting," Energy, Elsevier, vol. 286(C).
    15. Sharma, Abhishek & Jain, Sachin Kumar, 2022. "A novel seasonal segmentation approach for day-ahead load forecasting," Energy, Elsevier, vol. 257(C).

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