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A Frequency Decomposition-Based Hybrid Forecasting Algorithm for Short-Term Reactive Power

Author

Listed:
  • Jiabao Du

    (School of Automation, Wuhan University of Technology, Wuhan 430070, China)

  • Changxi Yue

    (China Electric Power Research Institute, Wuhan 430070, China)

  • Ying Shi

    (School of Automation, Wuhan University of Technology, Wuhan 430070, China)

  • Jicheng Yu

    (China Electric Power Research Institute, Wuhan 430070, China)

  • Fan Sun

    (Xinjiang Electric Power Research Institute of State Gird, Urumqi 830000, China)

  • Changjun Xie

    (School of Automation, Wuhan University of Technology, Wuhan 430070, China)

  • Tao Su

    (School of Automation, Wuhan University of Technology, Wuhan 430070, China)

Abstract

This paper proposes a new frequency decomposition-based hybrid reactive power forecasting algorithm, EEMD-LSTM-RFR (ELR), which adopts a strategy of frequency decomposition prediction after ensemble empirical mode decomposition and then data reconstruction to improve the prediction ability of reactive power. This decomposition process can compress the high frequency of reactive power and benefits the following separate forecasting. Long short-term memory is proposed for the high-frequency feature of reactive power to deal with the forecasting difficulty caused by strong signal disturbance and randomness. In contrast, random forest regression is applied to the low-frequency part in order to speed up the forecasting. Four classical algorithms and four hybrid algorithms based on different signal decompositions are compared with the proposed algorithm, and the results show that the proposed algorithm outperforms those algorithms. The predicting index RMSE decreases to 0.687, while the fitting degree R 2 gradually approaches 1 with a step-by-step superposition of high-frequency signals, indicating that the proposed decomposition-predicting reconstruction strategy is effective.

Suggested Citation

  • Jiabao Du & Changxi Yue & Ying Shi & Jicheng Yu & Fan Sun & Changjun Xie & Tao Su, 2021. "A Frequency Decomposition-Based Hybrid Forecasting Algorithm for Short-Term Reactive Power," Energies, MDPI, vol. 14(20), pages 1-18, October.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:20:p:6606-:d:655512
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    References listed on IDEAS

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    1. Nantian Huang & Guobo Lu & Dianguo Xu, 2016. "A Permutation Importance-Based Feature Selection Method for Short-Term Electricity Load Forecasting Using Random Forest," Energies, MDPI, vol. 9(10), pages 1-24, September.
    2. 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.
    3. Wu, Yu-Xi & Wu, Qing-Biao & Zhu, Jia-Qi, 2019. "Improved EEMD-based crude oil price forecasting using LSTM networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 114-124.
    4. Yu Jin & Honggang Guo & Jianzhou Wang & Aiyi Song, 2020. "A Hybrid System Based on LSTM for Short-Term Power Load Forecasting," Energies, MDPI, vol. 13(23), pages 1-32, November.
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    1. Can Ding & Yiyuan Zhou & Qingchang Ding & Kaiming Li, 2022. "Integrated Carbon-Capture-Based Low-Carbon Economic Dispatch of Power Systems Based on EEMD-LSTM-SVR Wind Power Forecasting," Energies, MDPI, vol. 15(5), pages 1-27, February.

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