Room Temperature Prediction for District Heating Substation Control: Integrating CatBoost Solar Radiation Prediction and State-Space Modeling

In district heating systems, the large-scale deployment of room temperature collectors has accumulated massive amounts of terminal data, but these data mostly remain at the level of viewing and statistical analysis and have not been effectively connected to real-time control of district heating substations. As a direct reflection of thermal comfort, room temperature can be incorporated into predictive control of district heating substations, which can promote the transition of heating control from lagging compensation to proactive prediction. To address three engineering bottlenecks, namely, the lack of solar radiation information, significant differences in user thermal characteristics, and the accumulation of prediction errors, this paper proposes a rolling room temperature prediction method that integrates CatBoost solar radiation prediction, canonical variate analysis (CVA) subspace identification, and adaptive Kalman filtering. Based on representative users selected by clustering, a model predictive control framework with an alternating iterative solution strategy is constructed to directly serve the closed-loop control of district heating substations. The method relies only on conventional weather forecasts and existing data collected by the heating system, without additional hardware costs. Taking a residential community in Luoyang as an example, there were 490 valid users on the prediction day of 16 December 2024. Using data from the 15 days prior to the prediction day for training and hourly rolling prediction, the root mean square error (RMSE) and mean absolute error (MAE) for all users were 0.203 °C and 0.146 °C, respectively, and the RMSE under six typical weather conditions remained within 0.21 °C. Online computation only involves low-dimensional matrix operations, and the computational burden is small, demonstrating the potential for deployment on existing controllers of district heating substations and good engineering deployability.