An Ensemble Model of Attention-Enhanced N-BEATS and XGBoost for District Heating Load Forecasting

Shaohua Yu, Xiaole Yang, Hengrui Ye, Daogui Tang (), Hamidreza Arasteh () and Josep M. Guerrero
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Shaohua Yu: School of Intelligent Manufacturing, Nanjing University of Science and Technology, Nanjing 210094, China
Xiaole Yang: School of Cyber Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
Hengrui Ye: School of Cyber Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
Daogui Tang: School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China
Hamidreza Arasteh: Center for Research on Microgrids (CROM), Huanjiang Laboratory, Shaoxing 311800, China
Josep M. Guerrero: Center for Research on Microgrids (CROM), Huanjiang Laboratory, Shaoxing 311800, China

Energies, 2025, vol. 18, issue 15, 1-22

Abstract: Accurate heat load forecasting is essential for the efficiency of District Heating Systems (DHS). Still, it is challenged by the need to model long-term temporal dependencies and nonlinear relationships with weather and other factors. This study proposes a hybrid deep learning framework combining an attention-enhanced Neural Basis Expansion Analysis for Time Series (N-BEATS) model and eXtreme Gradient Boosting (XGBoost). The N-BEATS component, with a multi-head self-attention mechanism, captures temporal dynamics, while XGBoost models non-linear impacts of external variables. Predictions are integrated using an optimized weighted averaging strategy. Evaluated on a dataset from 103 heating units, the model outperformed 13 baselines, achieving an MSE of 0.4131, MAE of 0.3732, RMSE of 0.6427, and R 2 of 0.9664. This corresponds to a reduction of 32.6% in MSE, 32.0% in MAE, and 17.9% in RMSE, and an improvement of 5.1% in R 2 over the best baseline. Ablation studies and statistical tests confirmed the effectiveness of the attention mechanism and ensemble strategy. This model provides an efficient solution for DHS load forecasting, facilitating optimized energy dispatch and enhancing system performance.

Keywords: district heating forecasting; N-BEATS; multi-head self-attention; XGBoost; model ensemble (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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