| Summary: | Significant research effort has gone into developing urban building energy modeling (UBEM) tools, which allow evaluating district-wide energy demand and supply strategies. In order to characterize simulation inputs for UBEM, buildings are typically grouped into representative "archetypes". This simplification reduces the real diversity of usage patterns, potentially leading to results that misrepresent energy demands. Unfortunately, very little research has focused on identifying the impact of such process in the effectiveness of an UBEM to reliably predict savings from retrofit measures. This paper analyzes two deterministic common approaches for the definition of building archetypes in UBEM, and proposes a probabilistic third method based on the characterization of uncertain parameters related to building occupancy using measured energy data. Frequency distributions for number of occupants, lighting power and cooling set points are generated through parametric simulation of an urban sample, later used for Monte Carlo (MC) simulation of retrofit scenarios. Measured data for the yearly energy use of one hundred and forty residential buildings in Kuwait city is used as a case study for the evaluation of the three methods. Results for the proposed probabilistic method suggest a significant improvement in the fit of the model to the measured energy use distribution.
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