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Short-Term Wind Power Prediction for Wind Farm Clusters Based on SFFS Feature Selection and BLSTM Deep Learning

Author

Listed:
  • Xiaosheng Peng

    (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Kai Cheng

    (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Jianxun Lang

    (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Zuowei Zhang

    (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Tao Cai

    (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Shanxu Duan

    (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

Abstract

Wind power prediction (WPP) of wind farm clusters is important to the safe operation and economic dispatch of the power system, but it faces two challenges: (1) The dimensions of the input parameters for WPP of wind farm clusters are very high so that the input parameters contain irrelevant or redundant features; (2) it is difficult to build a holistic WPP model with high-dimensional input parameters for wind farm clusters. To overcome these challenges, a novel short-term WPP model for wind farm clusters, based on sequential floating forward selection (SFFS) feature selection and bidirectional long short-term memory (BLSTM) deep learning, is proposed in this paper. First, more than 300,000 input features of the wind farm cluster are constructed. Second, the SFFS method is applied to sort the high-dimensional features and analyze the rule that the forecasting accuracy changes with the number of features to obtain the optimal number of features and feature sets. Finally, based on the results of feature selection, BLSTM is applied to build a WPP model for wind farm clusters with a combination of feature selection and deep learning. This case study shows that (1) SFFS is an effective method for selecting the core features for WPP of wind farm clusters; (2) BLSTM shows not only higher WPP accuracy than long short-term memory and backpropagation neural network but also outstanding performance in terms of reducing the phase errors of WPP.

Suggested Citation

  • Xiaosheng Peng & Kai Cheng & Jianxun Lang & Zuowei Zhang & Tao Cai & Shanxu Duan, 2021. "Short-Term Wind Power Prediction for Wind Farm Clusters Based on SFFS Feature Selection and BLSTM Deep Learning," Energies, MDPI, vol. 14(7), pages 1-18, March.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:7:p:1894-:d:526272
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    References listed on IDEAS

    as
    1. Chengdong Li & Zixiang Ding & Jianqiang Yi & Yisheng Lv & Guiqing Zhang, 2018. "Deep Belief Network Based Hybrid Model for Building Energy Consumption Prediction," Energies, MDPI, vol. 11(1), pages 1-26, January.
    2. Zhi Wu & Xiao Du & Wei Gu & Ping Ling & Jinsong Liu & Chen Fang, 2018. "Optimal Micro-PMU Placement Using Mutual Information Theory in Distribution Networks," Energies, MDPI, vol. 11(7), pages 1-19, July.
    3. Llorenç Burgas & Joan Colomer & Joaquim Melendez & Francisco Ignacio Gamero & Sergio Herraiz, 2021. "Integrated Unfold-PCA Monitoring Application for Smart Buildings: An AHU Application Example," Energies, MDPI, vol. 14(1), pages 1-15, January.
    4. Danqi Li & Fei Mei & Chenyu Zhang & Haoyuan Sha & Jianyong Zheng, 2019. "Self-Supervised Voltage Sag Source Identification Method Based on CNN," Energies, MDPI, vol. 12(6), pages 1-14, March.
    5. Drew, Daniel R. & Cannon, Dirk J. & Barlow, Janet F. & Coker, Phil J. & Frame, Thomas H.A., 2017. "The importance of forecasting regional wind power ramping: A case study for the UK," Renewable Energy, Elsevier, vol. 114(PB), pages 1201-1208.
    6. Nedaei, Mojtaba & Assareh, Ehsanolah & Walsh, Philip R., 2018. "A comprehensive evaluation of the wind resource characteristics to investigate the short term penetration of regional wind power based on different probability statistical methods," Renewable Energy, Elsevier, vol. 128(PA), pages 362-374.
    7. Kailai Ni & Jianzhou Wang & Guangyu Tang & Danxiang Wei, 2019. "Research and Application of a Novel Hybrid Model Based on a Deep Neural Network for Electricity Load Forecasting: A Case Study in Australia," Energies, MDPI, vol. 12(13), pages 1-30, June.
    8. Imre Delgado & Muhammad Fahim, 2020. "Wind Turbine Data Analysis and LSTM-Based Prediction in SCADA System," Energies, MDPI, vol. 14(1), pages 1-21, December.
    9. Haisheng Chen & Xinjing Zhang & Jinchao Liu & Chunqing Tan, 2013. "Compressed Air Energy Storage," Chapters, in: Ahmed F. Zobaa (ed.), Energy Storage - Technologies and Applications, IntechOpen.
    10. Bayat, A. & Bagheri, A., 2019. "Optimal active and reactive power allocation in distribution networks using a novel heuristic approach," Applied Energy, Elsevier, vol. 233, pages 71-85.
    11. He, Feifei & Zhou, Jianzhong & Feng, Zhong-kai & Liu, Guangbiao & Yang, Yuqi, 2019. "A hybrid short-term load forecasting model based on variational mode decomposition and long short-term memory networks considering relevant factors with Bayesian optimization algorithm," Applied Energy, Elsevier, vol. 237(C), pages 103-116.
    12. Ekren, Orhan & Ekren, Banu Y., 2010. "Size optimization of a PV/wind hybrid energy conversion system with battery storage using simulated annealing," Applied Energy, Elsevier, vol. 87(2), pages 592-598, February.
    13. Peng Liu & Peijun Zheng & Ziyu Chen, 2019. "Deep Learning with Stacked Denoising Auto-Encoder for Short-Term Electric Load Forecasting," Energies, MDPI, vol. 12(12), pages 1-15, June.
    14. Danxiang Wei & Jianzhou Wang & Kailai Ni & Guangyu Tang, 2019. "Research and Application of a Novel Hybrid Model Based on a Deep Neural Network Combined with Fuzzy Time Series for Energy Forecasting," Energies, MDPI, vol. 12(18), pages 1-38, September.
    15. Songkai Liu & Ruoyuan Shi & Yuehua Huang & Xin Li & Zhenhua Li & Lingyun Wang & Dan Mao & Lihuang Liu & Siyang Liao & Menglin Zhang & Guanghui Yan & Lian Liu, 2021. "A Data-Driven and Data-Based Framework for Online Voltage Stability Assessment Using Partial Mutual Information and Iterated Random Forest," Energies, MDPI, vol. 14(3), pages 1-16, January.
    16. Huiting Zheng & Jiabin Yuan & Long Chen, 2017. "Short-Term Load Forecasting Using EMD-LSTM Neural Networks with a Xgboost Algorithm for Feature Importance Evaluation," Energies, MDPI, vol. 10(8), pages 1-20, August.
    17. Zhang, Yi & Xu, Yujie & Zhou, Xuezhi & Guo, Huan & Zhang, Xinjing & Chen, Haisheng, 2019. "Compressed air energy storage system with variable configuration for accommodating large-amplitude wind power fluctuation," Applied Energy, Elsevier, vol. 239(C), pages 957-968.
    18. 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.
    19. Hossein Iranmanesh & Majid Abdollahzade & Arash Miranian, 2011. "Mid-Term Energy Demand Forecasting by Hybrid Neuro-Fuzzy Models," Energies, MDPI, vol. 5(1), pages 1-21, December.
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    Cited by:

    1. Bo Wang & Tiancheng Wang & Mao Yang & Chao Han & Dawei Huang & Dake Gu, 2023. "Ultra-Short-Term Prediction Method of Wind Power for Massive Wind Power Clusters Based on Feature Mining of Spatiotemporal Correlation," Energies, MDPI, vol. 16(6), pages 1-16, March.

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