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Building performance simulation is being increasingly deployed beyond the building design phase to support efficient building operation. Specifically, the predictive feature of the simulation-assisted building systems control strategy provides distinct advantages in view of building systems with high latency and inertia. Such advantages can be exploited only if model predictions can be relied upon. Hence, it is important to calibrate simulation models based on monitored data. In the present paper, we report on the use of optimization-aided model calibration in the context of an existing university building. Thereby, our main objective is to deploy data obtained via the monitoring system to both populate the initial simulation model and to maintain its fidelity through an ongoing optimization-based calibration process. The results suggest that the calibration can significantly improve the predictive performance of the thermal simulation model.

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