Short-Term Load Forecasting Based on Similar Day Theory and BWO-VMD

Qi Cheng, Jing Shi () and Siwei Cheng
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Qi Cheng: Department of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Jing Shi: Department of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Siwei Cheng: Department of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China

Energies, 2025, vol. 18, issue 9, 1-20

Abstract: Short-term power load forecasting at the regional level is essential for maintaining grid stability and optimizing power generation, consumption, and maintenance scheduling. Considering the temporal, periodic, and nonlinear characteristics of power load, a novel short-term load forecasting method is proposed in this paper. First, Random Forest importance ranking is applied to select similar days and a weighted eigenspace coordinate system is established to measure similarity. The daily load sequence is then decomposed into high-, medium-, and low-frequency components using Variational Mode Decomposition (VMD). The high-frequency component is predicted using the similar day averaging method, while neural networks are employed for the medium and low-frequency components, leveraging historical and similar-day data, respectively. This multi-faceted approach enhances the accuracy and granularity of load pattern analysis. The final forecast is obtained by summing the predictions of these components. The case study demonstrates that the proposed model outperforms LSTM, GRU, CNN, TCN and Transformer, with an RMSE of 660.54 MW and a MAPE of 7.81%, while also exhibiting fast computational speed and low CPU usage.

Keywords: load forecasting; similar day theory; VMD; artificial neural networks (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: 2025
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