Short-Term Power Load Forecasting: An Integrated Approach Utilizing Variational Mode Decomposition and TCN–BiGRU

Zhuoqun Zou, Jing Wang, Ning E, Can Zhang, Zhaocai Wang and Enyu Jiang ()
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Zhuoqun Zou: College of Information Technology, Shanghai Ocean University, Shanghai 201306, China
Jing Wang: College of Information Technology, Shanghai Ocean University, Shanghai 201306, China
Ning E: College of Information Technology, Shanghai Ocean University, Shanghai 201306, China
Can Zhang: College of Information Technology, Shanghai Ocean University, Shanghai 201306, China
Zhaocai Wang: College of Information Technology, Shanghai Ocean University, Shanghai 201306, China
Enyu Jiang: Electric Power Engineering, Shanghai University of Electric Power, Shanghai 200090, China

Energies, 2023, vol. 16, issue 18, 1-17

Abstract: Accurate short-term power load forecasting is crucial to maintaining a balance between energy supply and demand, thus minimizing operational costs. However, the intrinsic uncertainty and non-linearity of load data substantially impact the accuracy of forecasting results. To mitigate the influence of these uncertainties and non-linearity in electric load data on the forecasting results, we propose a hybrid network that integrates variational mode decomposition with a temporal convolutional network (TCN) and a bidirectional gated recurrent unit (BiGRU). This integrated approach aims to enhance the accuracy of short-term power load forecasting. The method was validated on load datasets from Singapore and Australia. The MAPE of this paper’s model on the two datasets reached 0.42% and 1.79%, far less than other models, and the R 2 reached 98.27% and 97.98, higher than other models. The experimental results show that the proposed network exhibits a better performance compared to other methods, and could improve the accuracy of short-term electricity load forecasting.

Keywords: short-term load forecasting; power systems; variational mode decomposition; TCN; BiGRU (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: 2023
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