Open Access

ARTICLE

Application and Performance Optimization of SLHS-TCN-XGBoost Model in Power Demand Forecasting

Tianwen Zhao¹, Guoqing Chen^2,3, Cong Pang⁴, Piyapatr Busababodhin^3,5,*

1 Department of Trade and Logistics, Daegu Catholic University, Gyeongsan, 38430, Republic of Korea
2 Digital Economy Research Institute, Chengdu Jincheng College, Chengdu, 611731, China
3 Department of Mathematics, Faculty of Science, Mahasarakham University, Maha Sarakham, 44150, Thailand
4 Institute of Seismology, China Earthquake Administration, Wuhan, 430071, China
5 The Digital Innovation Research Cluster for Integrated Disaster Management in the Watershed, Mahasarakham University, Maha Sarakham, 44150, Thailand

* Corresponding Author: Piyapatr Busababodhin. Email:

Computer Modeling in Engineering & Sciences 2025, 143(3), 2883-2917. https://doi.org/10.32604/cmes.2025.066442

Received 08 April 2025; Accepted 11 June 2025; Issue published 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% 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 (p < 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.

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