Driven by the growing energy demand and severe challenges posed by climate change, reducing the high energy consumption of district heating systems while enhancing their flexibility and operational reliability has become an urgent priority. This study focuses on the heating system of a residential community in Zhengzhou, China, by developing a joint source-network-load simulation model and proposing a model predictive control (MPC) strategy tailored to the dynamic characteristics of the system. A white-box model of the building complex and heating system was established by coupling EnergyPlus and Modelica. Subsequently, the model was automatically calibrated using actual operational data and the GenOpt optimization tool, which further improved the simulation accuracy and optimal control performance of the model. The results show that the root mean square errors (RMSEs) of the calibrated secondary network supply water temperature, return water temperature, and indoor temperature decreased by 34.6% and 15.7%, respectively, verifying the effectiveness of the proposed calibration method. Furthermore, the proposed MPC strategy demonstrates significant advantages over conventional control baselines, greatly improving the temperature regulation accuracy and system stability. Compared to the baseline operation without MPC, the proposed strategy increases the user-side thermal comfort index from 56% to 100%, thereby significantly enhancing overall heating quality.
Ma et al. (Mon,) studied this question.
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