@Article{cmes.2025.066442,
AUTHOR = {Tianwen Zhao, Guoqing Chen, Cong Pang, Piyapatr Busababodhin},
TITLE = {Application and Performance Optimization of SLHS-TCN-XGBoost Model in Power Demand Forecasting},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {143},
YEAR = {2025},
NUMBER = {3},
PAGES = {2883--2917},
URL = {http://www.techscience.com/CMES/v143n3/62844},
ISSN = {1526-1506},
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% relative to the baseline XGBoost model. Performance validation across nine forecast days demonstrates superior accuracy, with MAPE as low as 0.35% and 0.21% on key dates. Statistical Significance tests confirm significant improvements (<i>p</i> < 0.05), with the highest MAPE reduction of 98.17% on critical days. Seasonal and temporal error analyses reveal stable performance, particularly in Quarter 3 and Quarter 4 (0.5%, 0.3%) and nighttime hours (<1%). Robustness tests, including 5-fold cross-validation and Various noise perturbations, confirm the model’s stability and resilience. The SLHS-TCN-XGBoost model offers an efficient and reliable solution for power demand forecasting, with future optimization potential in data preprocessing, algorithm integration, and interpretability.},
DOI = {10.32604/cmes.2025.066442}
}