Short-Term Electricity Price Forecasting Based on the Two-Layer VMD Decomposition Technique and SSA-LSTM

Fang Guo, Shangyun Deng, Weijia Zheng (), An Wen, Jinfeng Du, Guangshan Huang and Ruiyang Wang
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Fang Guo: School of Mechatronical Engineering and Automation, Foshan University, Foshan 528231, China
Shangyun Deng: School of Mechatronical Engineering and Automation, Foshan University, Foshan 528231, China
Weijia Zheng: School of Mechatronical Engineering and Automation, Foshan University, Foshan 528231, China
An Wen: Yangtze Delta Region Institute, University of Electronic Science and Technology of China, Huzhou 313098, China
Jinfeng Du: School of Information, Xuzhou Vocational College of Industrial Technology, Xuzhou 221140, China
Guangshan Huang: School of Mechatronical Engineering and Automation, Foshan University, Foshan 528231, China
Ruiyang Wang: School of Mechatronical Engineering and Automation, Foshan University, Foshan 528231, China

Energies, 2022, vol. 15, issue 22, 1-20

Abstract: Accurate electricity price forecasting (EPF) can provide a necessary basis for market decision making by power market participants to reduce the operating cost of the power system and ensure the system’s stable operation. To address the characteristics of high frequency, strong nonlinearity, and high volatility of electricity prices, this paper proposes a short-term electricity price forecasting model based on a two-layer variational modal decomposition (VMD) technique, using the sparrow search algorithm (SSA) to optimize the long and short-term memory network (LSTM). The original electricity price sequence is decomposed into multiple modal components using VMD. Then, each piece is predicted separately using an SSA-optimized LSTM. For the element with the worst prediction accuracy, IMF-worst is decomposed for a second time using VMD to explore the price characteristics further. Finally, the prediction results of each modal component are reconstructed to obtain the final prediction results. To verify the validity and accuracy of the proposed model, this paper uses data from three electricity markets, Australia, Spain, and France, for validation analysis. The experimental results show that the proposed model has MAPE of 0.39%, 1.58%, and 0.95%, RMSE of 0.25, 0.9, and 0.3, and MAE of 0.19, 0.68, and 0.31 in three different cases, indicating that the proposed model can well handle the nonlinear and non-stationarity characteristics of the electricity price series and has superior forecasting performance.

Keywords: electricity price forecasting; two-layer variational modal decomposition; sparrow search algorithm; long short-term memory networks; hybrid model (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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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