一种基于数字孪生的建筑能耗仿真与预测方法及系统
By monitoring the operating status and temperature changes of air conditioning equipment, identifying the step excitation moment, determining the thermal response lag time of the physical and virtual environments, and iteratively correcting the parameters of the digital twin model, the problem of thermal performance degradation in traditional building energy consumption simulation is solved, and accurate prediction of building energy consumption is achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- XIAMEN FAMILI INFORMATION TECHNOLOGY CO LTD
- Filing Date
- 2026-05-12
- Publication Date
- 2026-07-17
AI Technical Summary
Traditional building energy consumption simulation methods are unable to reflect in real time the decay of the thermal performance of the building envelope caused by long-term operation, and cannot accurately characterize the actual thermodynamic behavior of the building under dynamic operating conditions, resulting in reduced accuracy of energy consumption prediction results.
By monitoring the operating status of the air conditioning terminal equipment in the physical building, identifying the start time of the step excitation, and combining the measured temperature change rate to determine the thermal response lag time of the physical environment, the virtual model is driven to obtain the thermal response lag time of the virtual environment. The deviation between the two is calculated and the thermal inertia parameters in the digital twin model are iteratively corrected. A dynamic calibration mechanism is established to eliminate model parameter errors and ensure that the thermodynamic behavior of the virtual space is highly consistent with that of the physical building.
It enables accurate prediction of building energy consumption trends, ensures that the thermodynamic behavior of the virtual model is consistent with that of the physical entity, and improves the accuracy of energy consumption prediction.
Smart Images
Figure CN122173847B_ABST