The improvement of energy efficiency of existing buildings is key for meeting 2030 and 2050 energy and CO2 emission targets. Thus, building simulation tools play a crucial role in evaluating the performance of energy retrofit actions, not only at present, but also under future climate scenarios.
A Bayesian calibration approach, combined with sensitivity analysis, is applied to reduce the discrepancies between measured and simulated hourly indoor air temperatures. Calibration is applied to a test cell case study developed using the EnergyPlus building simulation software. Several scenarios are evaluated to determine how different variables may impact the calibration process: orientations, activation of mechanical ventilation, different blind aperture levels, etc. Uncertainties associated with model inputs (fixed parameters in the energy model), model discrepancies due to physical limitations of the building energy model (simplifications when compared to the real performance of the building), errors in field observations and noisy measurements were also accounted for.
Even though uncalibrated models were within the uncertainty ranges specified by the ASHARE Guidelines, pre-calibration simulation outputs over-predicted measurements up to 3.2 ºC. After calibration, the average maximum temperature difference was reduced to 0.68 ºC, improving the results by almost 80%. Thus, these techniques are proven to improve the level of agreement between on-site measurements and simulated outputs. Besides, the implementation of this methodology is useful for calibrating and validating indoor hourly temperatures and, consequently, provide adequate results for thermal comfort assessment.