Bus Load Forecasting Method of Power System Based on VMD and Bi-LSTM

Jiajie Tang, Jie Zhao, Hongliang Zou, Gaoyuan Ma, Jun Wu, Xu Jiang and Huaixun Zhang
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Jiajie Tang: School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
Jie Zhao: School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
Hongliang Zou: Taizhou Power Supply Company of State Grid Zhejiang Electric Power Co., Ltd., Taizhou 318000, China
Gaoyuan Ma: School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
Jun Wu: School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
Xu Jiang: Taizhou Power Supply Company of State Grid Zhejiang Electric Power Co., Ltd., Taizhou 318000, China
Huaixun Zhang: School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China

Sustainability, 2021, vol. 13, issue 19, 1-20

Abstract: The effective prediction of bus load can provide an important basis for power system dispatching and planning and energy consumption to promote environmental sustainable development. A bus load forecasting method based on variational modal decomposition (VMD) and bidirectional long short-term memory (Bi-LSTM) network was proposed in this article. Firstly, the bus load series was decomposed into a group of relatively stable subsequence components by VMD to reduce the interaction between different trend information. Then, a time series prediction model based on Bi-LSTM was constructed for each sub sequence, and Bayesian theory was used to optimize the sub sequence-related hyperparameters and judge whether the sequence uses Bi-LSTM to improve the prediction accuracy of a single model. Finally, the bus load prediction value was obtained by superimposing the prediction results of each subsequence. The example results show that compared with the traditional prediction algorithm, the proposed method can better track the change trend of bus load, and has higher prediction accuracy and stability.

Keywords: variational mode decomposition (VMD); Bayesian optimization; bidirectional long short-term memory (Bi-LSTM); power system bus load forecasting (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (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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