Review of uncertainties in building characterization for urban-scale energy modeling

Abstract

Bottom-up modeling appears to be a suitable approach for the urban-scale building energy performance assessment with providing valuable inferences on the complicated building energy patterns and helping authorities monitor/predict the energy demand for urban planning and retrofitting. Archetype characterization is the utmost challenging process when developing bottom-up models since there is a large diversity in characteristic features of building stocks. This gap induces practitioners to seek stochastic methods even though the deterministic approaches are solid guides in archetype characterization. Hence, the research objective of this study is to provide insights into the motivation, challenges, and methods of the studies conducted to assess the buildings' energy demand at the urban scale. The original value of this research is to analyze/question different archetype characterization methods and their practicability over wide-ranging studies, identify the most crucial characterization parameters and assess the validation techniques to enhance the demand estimations of urban building energy models (UBEMs). To that end, this study performs a literature review and mainly provides the following findings: (1) The required characterization method is highly dependent on the purpose and scope of the study. (2) The Bayesian calibration makes ground in UBEM practices as it consolidates the models' estimation power through the probabilistic archetype characterization. (3) Considering the notable fluctuations in buildings' energy demand induced by occupancy patterns, detailed occupancy profiles could improve the archetype characterization. Finally, the major setback is the lack of available data to characterize energy models with building-specific information. (4) Building information models (BIMs) could soon play a pivotal role in supplying such data for UBEM practices. This study contributes to the literature by fulfilling the lack of perspective that concentrates on the archetype characterization methods in UBEM. The findings could help practitioners (e.g., policymakers and city planners) and academics to comprehend the potential of the UBEM that improves energy management strategies at the urban scale.