Research on an hourly heat load forecasting model for district heating systems based on heterogeneous model fusion
Accurate load forecasting for district heating systems (DHS) is crucial for ensuring efficient production, distribution, and energy utilization. Although artificial neural networks have been widely used for DHS thermal load forecasting, their capacity to capture long-term dependencies remains limite...
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Elsevier
2025-09-01
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| Series: | Case Studies in Thermal Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214157X25007609 |
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| author | Tieliu Jiang Wenyue Liu Jianqing Lin Xu Jin Zhongyan Liu |
| author_facet | Tieliu Jiang Wenyue Liu Jianqing Lin Xu Jin Zhongyan Liu |
| author_sort | Tieliu Jiang |
| collection | DOAJ |
| description | Accurate load forecasting for district heating systems (DHS) is crucial for ensuring efficient production, distribution, and energy utilization. Although artificial neural networks have been widely used for DHS thermal load forecasting, their capacity to capture long-term dependencies remains limited. This study proposes a prediction model called TBMK (TCN-BiMamba2-KAN) based on an improved bidirectional selective state-space model (Bi-Mamba2). By integrating the local feature extraction capabilities of temporal convolutional networks (TCN), the global temporal modeling advantages of Bi-Mamba2, and the nonlinear fitting characteristics of Kolmogorov-Arnold networks (KAN), a multi-module collaborative prediction framework is constructed. Unlike conventional SSM-based models that struggle with long-term thermal inertia modeling, the proposed TBMK achieves dual-dimensional feature capture through bidirectional state-space scanning and spline-activated nonlinear mapping. The model is validated using historical operational data from a DHS in Changchun, China, which includes multidimensional features such as secondary heating/return water temperatures, solar radiation, humidity, and outdoor temperature. A comparison was conducted with benchmark models, including LSTM, TCN, Bi-Mamba2, TCN-LSTM, and BiMamba2-TCN. The experimental results indicate that the R2 value of the TBMK model reaches 0.987, with the MAE, RMSE, and MAPE decreasing by 25 %, 30.49 %, and 1.17 %, respectively, compared to the second-best model. Diebold-Mariano test confirms that its predictive performance is significantly superior to the comparative models (p < 0.05). This model can provide high-precision guidance for thermal scheduling, facilitating optimization of energy losses and control of operational costs. |
| format | Article |
| id | doaj-art-57e2b940b0bb496f9510f6e63c2b871a |
| institution | Kabale University |
| issn | 2214-157X |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Thermal Engineering |
| spelling | doaj-art-57e2b940b0bb496f9510f6e63c2b871a2025-08-20T03:32:50ZengElsevierCase Studies in Thermal Engineering2214-157X2025-09-017310650010.1016/j.csite.2025.106500Research on an hourly heat load forecasting model for district heating systems based on heterogeneous model fusionTieliu Jiang0Wenyue Liu1Jianqing Lin2Xu Jin3Zhongyan Liu4School of Energy and Power Engineering, Northeast Electric Power University, Jilin, 132012, ChinaSchool of Energy and Power Engineering, Northeast Electric Power University, Jilin, 132012, ChinaSchool of Energy and Power Engineering, Northeast Electric Power University, Jilin, 132012, ChinaSchool of Energy and Power Engineering, Northeast Electric Power University, Jilin, 132012, ChinaCorresponding author.; School of Energy and Power Engineering, Northeast Electric Power University, Jilin, 132012, ChinaAccurate load forecasting for district heating systems (DHS) is crucial for ensuring efficient production, distribution, and energy utilization. Although artificial neural networks have been widely used for DHS thermal load forecasting, their capacity to capture long-term dependencies remains limited. This study proposes a prediction model called TBMK (TCN-BiMamba2-KAN) based on an improved bidirectional selective state-space model (Bi-Mamba2). By integrating the local feature extraction capabilities of temporal convolutional networks (TCN), the global temporal modeling advantages of Bi-Mamba2, and the nonlinear fitting characteristics of Kolmogorov-Arnold networks (KAN), a multi-module collaborative prediction framework is constructed. Unlike conventional SSM-based models that struggle with long-term thermal inertia modeling, the proposed TBMK achieves dual-dimensional feature capture through bidirectional state-space scanning and spline-activated nonlinear mapping. The model is validated using historical operational data from a DHS in Changchun, China, which includes multidimensional features such as secondary heating/return water temperatures, solar radiation, humidity, and outdoor temperature. A comparison was conducted with benchmark models, including LSTM, TCN, Bi-Mamba2, TCN-LSTM, and BiMamba2-TCN. The experimental results indicate that the R2 value of the TBMK model reaches 0.987, with the MAE, RMSE, and MAPE decreasing by 25 %, 30.49 %, and 1.17 %, respectively, compared to the second-best model. Diebold-Mariano test confirms that its predictive performance is significantly superior to the comparative models (p < 0.05). This model can provide high-precision guidance for thermal scheduling, facilitating optimization of energy losses and control of operational costs.http://www.sciencedirect.com/science/article/pii/S2214157X25007609District heating systemsThermal load forecastingBi-Mamba2State space modelTemporal convolutional networkKolmogorov-arnold network |
| spellingShingle | Tieliu Jiang Wenyue Liu Jianqing Lin Xu Jin Zhongyan Liu Research on an hourly heat load forecasting model for district heating systems based on heterogeneous model fusion Case Studies in Thermal Engineering District heating systems Thermal load forecasting Bi-Mamba2 State space model Temporal convolutional network Kolmogorov-arnold network |
| title | Research on an hourly heat load forecasting model for district heating systems based on heterogeneous model fusion |
| title_full | Research on an hourly heat load forecasting model for district heating systems based on heterogeneous model fusion |
| title_fullStr | Research on an hourly heat load forecasting model for district heating systems based on heterogeneous model fusion |
| title_full_unstemmed | Research on an hourly heat load forecasting model for district heating systems based on heterogeneous model fusion |
| title_short | Research on an hourly heat load forecasting model for district heating systems based on heterogeneous model fusion |
| title_sort | research on an hourly heat load forecasting model for district heating systems based on heterogeneous model fusion |
| topic | District heating systems Thermal load forecasting Bi-Mamba2 State space model Temporal convolutional network Kolmogorov-arnold network |
| url | http://www.sciencedirect.com/science/article/pii/S2214157X25007609 |
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