Tianwen Zhao1, Guoqing Chen2,3, Cong Pang4, Piyapatr Busababodhin3,5,*
CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 2883-2917, 2025, DOI:10.32604/cmes.2025.066442
- 30 June 2025
Abstract Existing power forecasting models struggle to simultaneously handle high-dimensional, noisy load data while capturing long-term dependencies. This critical limitation necessitates an integrated approach combining dimensionality reduction, temporal modeling, and robust prediction, especially for multi-day forecasting. A novel hybrid model, SLHS-TCN-XGBoost, is proposed for power demand forecasting, leveraging SLHS (dimensionality reduction), TCN (temporal feature learning), and XGBoost (ensemble prediction). Applied to the three-year electricity load dataset of Seoul, South Korea, the model’s MAE, RMSE, and MAPE reached 112.08, 148.39, and 2%, respectively, which are significantly reduced in MAE, RMSE, and MAPE by 87.37%, 87.35%, and 87.43%… More >