Modelling and calibration of a high-mass historic building for reducing the prebound effect in energy assessment

Abstract

Through a case study building equipped with an advanced monitoring system, and toward the development of a general historic-building retrofit strategy, this paper presents a contribution to the calibration of building thermal models, characterized by massive and thick walls, common features of historic Italian architecture. Model calibration is performed to assess the values of parameters which are most representative of the measured building behaviour. The approach introduces sensitivity analysis for factor fixing, harnessing hourly data and metrics that take into account surface temperatures. With the measured data and simulations, the relative importance of model parameters is inferred. Different error metrics and models configuration are presented and compared in order to determine the best approach allowing the detailed calibration of building and overcoming the model limitations within EnergyPlus to characterize the heat capacity of radiators. The procedure is verified by evaluating heating energy consumption comparing different models to measured data. The resulting “prebound” effect is lowered from 64% to 3.5%. The temperature root mean square error of indoor air is lowered to below 0.5 K in both summer and winter. New error metrics increase the overall model reliability. The methodology is implemented in an open-source software tool chain that may be used in other building types.