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

Adriana Angelotti (), Livio Mazzarella, Cristina Cornaro, Francesca Frasca, Alessandro Prada, Paolo Baggio, Ilaria Ballarini, Giovanna De Luca and Vincenzo Corrado
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Adriana Angelotti: Dipartimento di Energia, Politecnico di Milano, 20156 Milano, Italy
Livio Mazzarella: Dipartimento di Energia, Politecnico di Milano, 20156 Milano, Italy
Cristina Cornaro: Dipartimento di Ingegneria dell’Impresa, Università degli Studi di Roma Tor Vergata, 00133 Roma, Italy
Francesca Frasca: Dipartimento di Fisica, Università La Sapienza, 00185 Roma, Italy
Alessandro Prada: Dipartimento di Ingegneria Civile, Ambientale e Meccanica, Università di Trento, 38122 Trento, Italy
Paolo Baggio: Dipartimento di Ingegneria Civile, Ambientale e Meccanica, Università di Trento, 38122 Trento, Italy
Ilaria Ballarini: Dipartimento Energia, Politecnico di Torino, 10129 Torino, Italy
Giovanna De Luca: Dipartimento Energia, Politecnico di Torino, 10129 Torino, Italy
Vincenzo Corrado: Dipartimento Energia, Politecnico di Torino, 10129 Torino, Italy

Energies, 2023, vol. 16, issue 7, 1-24

Abstract: 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 °C compared to 0.4–0.5 °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.

Keywords: building energy simulation; calibration; validation; users’ behavior; automatic/manual optimization; free-floating; monitoring (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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