Calibrating the Dynamic Energy Simulation Model for an Existing Building: Lessons Learned from a Collective Exercise

Calibration of the existing building simulation model is key to correctly evaluating the energy savings that are achievable through retrofit. However, calibration is a non-standard phase where different approaches can possibly lead to different models. In this study, an existing residential building is simulated in parallel by four research groups with different dynamic simulation tools. Manual/automatic methodologies and basic/detailed measurement data sets are used. The calibration is followed by a validation on two evaluation periods. Monitoring data concerning the windows opening by the occupants are used to analyze the calibration outcomes. It is found that for a good calibration of a model of a well-insulated building, the absence of data regarding the users' behavior is more critical than uncertainty on the envelope properties. The automatic approach is more effective in managing the model complexity and reaching a better performing calibration, as the RMSE relative to indoor temperature reaches 0.3 degrees C compared to 0.4-0.5 degrees C. Yet, a calibrated model's performance is often poor outside the calibration period (RMSE increases up to 10.8 times), and thus, the validation is crucial to discriminate among multiple solutions and to refine them, by improving the users' behavior modeling.

Automated summary

Calibrating the existing building simulation model correctly is crucial for evaluating the potential energy savings through retrofit. In this study, four research groups used different dynamic simulation tools to simultaneously simulate an existing residential building. Different methodologies and measurement data sets were employed for calibration, followed by validation on two evaluation periods. The study finds that the absence of user behavior data is more critical than uncertainty in envelope properties for calibrating a well-insulated building model. The automatic approach is more effective in managing model complexity and achieving better calibration performance, with a lower RMSE relative to indoor temperature compared to the manual approach.