Dense cities usually experience the urban heat island (UHI) effect, resulting in higher ambient temperatures and increased cooling loads. However, the typical lack of combining climatic variables with building passive design parameters in significant evaluations hinders the consideration of the UHI effect during the building design stage. In that regard, a global sensitivity analysis was conducted to assess the significance of climatic variables and building design features in building energy simulations for an office building. Additionally, this study examines the UHI effect on building energy performance in Qatar, a hot-arid climate, using both measurement data and computational modeling. This study collects measurement data across Qatar and conducts computational fluid dynamics (CFD) simulations; the results from both methods serve as inputs in building energy simulation (BES). The results demonstrate that space cooling demand is more sensitive to ambient temperature than other climatic parameters, building thermal properties, etc. The UHI intensity is high during hot and transition seasons and reaches a maximum of 13 °C. BES results show a 10% increase in cooling energy demand for an office building due to the UHI effect on a hot day. The results of this study enable more informed decision-making during the building design process.

Many factors contribute to the inherent uncertainty of energy consumption modeling in buildings. It is essential to perform a calibration and sensitivity analysis in order to manage these uncertainties. Despite the availability of several calibration methods, they are often deterministic and lack quantified uncertainties. Moreover, the selection of parameters in building energy modeling for calibration depends on the user’s experience. Therefore, a more rigorous selection process is required. This study developed a new automated Bayesian Inference calibration platform running as an R package. A sensitivity analysis module and a Bayesian inference module determine the calibration parameters and uncertainties, respectively. The Meta-model module is developed to replace the building energy model for the Markov Chain Monte Carlo process to save computing time. The proposed platform is successfully demonstrated on a synthetic high-rise office building and a real high-rise residential building in a hot and arid climate. The relationship between the number of calibration parameters, calibration performance, and the accuracy of the Meta-model is further discussed. The developed calibration platform in this study proved to have clear advantages over the existing platforms, with the ability to reasonably estimate building energy performance in a short computing time.

The building sector accounts for nearly 40% of global energy consumption and plays a critical role in societal energy security and sustainability. A building energy model (BEM) simulates complex building physics and provides insights into various energy-saving measures’ performance. The analysis based on BEMs has become an essential approach to slowing down increasing building energy consumption. The reliability and accuracy of BEMs have a high impact on decision-making. However, how to calibrate a building energy model has remained a challenge. In this study, Bayesian inference was applied to the calibration of an office building model under the arid weather conditions of Doha, Qatar. The coefficient of variation with a root-mean-square error of calibration and validation are 1.1% and 1.5%, respectively, which is highly satisfied with the monthly calibration tolerance of 15% required by ASHRAE Guideline 14. Additionally, the calibrated parameter results are with probabilities and degrees of confidence, so they are more reasonable and comprehensive than traditional deterministic calibration methods. This study conducted a sensitivity analysis to select the model’s dominant parameters under hot/arid weather conditions. This study will be among the first studies of stochastic calibration based on Bayesian inference for building energy performances in arid weather.