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When deciding on the best historic building retrofit, energy savings and thermal comfort can be quantitatively evaluated using an energy model, whereas conservation compatibility is intrinsically qualitative and reflects the perspective of the local heritage authority. We present a methodology that permits finding and comparing optimal retrofits for historic buildings in a multi-perspective and quantitative way. We use an analytic hierarchy process to quantify conservation compatibility by distilling a conservation score from the opinions of 10 experts in the field. This score, along with energy needs for heating and cooling and thermal comfort, are the three targets of a multi-objective optimization aimed at identifying optimal retrofits for a medieval building in the north of Italy, destined to become a museum. Retrofit measures considered were different kinds of external and internal envelope insulation, improvement of airtightness, replacement of windows, and ventilative cooling. The result is a portfolio of optimal retrofits that cover the whole range of conservation compatibility. We show that in the analyzed case heritage preservation is compatible with a four-fold reduction in energy needs at a high thermal comfort level. Even higher energy savings are only achievable at the cost of heritage degradation. [ABSTRACT FROM AUTHOR]

Uncalibrated building energy models, as well as models calibrated only on a single performance indicator such as energy consumption or indoor temperature, can be significantly unreliable regarding model parameters and other performance indicators. The risk of obtaining a calibrated model whose parameters are far from the actual values is particularly high in historic buildings because of the increased uncertainty about the building construction. In this paper, we propose a calibration methodology aimed at reducing this risk and apply it on a medieval building. The building was modeled in EnergyPlus based on an energy audit. A sensitivity analysis was performed to identify significant parameters affecting the errors between simulated and monitored indoor air temperatures. The model was calibrated on the hourly indoor air temperatures in summer by minimizing the root mean square error averaged over the building using a particle swarm optimization algorithm. A second calibration was performed by varying the parameters of a representative room. By comparing the results from these two calibrations, we obtained indications about the accuracy of the model parameters. Finally, the model was validated on hourly indoor air and surface temperatures in winter where temperature root mean square errors ranged from 0.4 to 0.8 K. [ABSTRACT FROM AUTHOR]