Year: 2025
Type: article
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.
Source: Energies
DOI: 10.3390/en18092358
Authors: Qi Cheng, Jing Shi, Siwei Cheng
Institution: Huazhong University of Science and Technology
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W4410130693