Work

Research on an hourly heat load forecasting model for district heating systems based on heterogeneous model fusion

Year: 2025

Type: article

Abstract: Accurate load forecasting for district heating systems 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. 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, BiMamba2-Transformer. The experimental results indicate that the R2 value of the TBMK model reaches 0.982, with the MAE, RMSE, and MAPE decreasing by 49.3%, 57.3%, and 41.4%, 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.

Source: Case Studies in Thermal Engineering

DOI: 10.1016/j.csite.2025.106500

Authors: Tieliu Jiang, Wenyue Liu, Jianqing Lin, Xu Jin, Zhongyan Liu

Institution: Northeast Electric Power University

Language: English

Topic: Integrated Energy Systems Optimization

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure, Climate action

Open Access status: gold

Estimated APC paid: $1,620