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

Jiabao Du, Changxi Yue, Ying Shi, Jicheng Yu, Fan Sun, Changjun Xie and Tao Su
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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

Energies, 2021, vol. 14, issue 20, 1-18

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.

Keywords: reactive power; forecasting algorithm; ensemble empirical mode decomposition; long short-term memory; random forest regression (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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