A shoeboxing algorithm for urban building energy modeling: Validation for stand-alone buildings

Urban scale simulation is recognized to have a significant potential for the estimation of buildings' thermal behavior and energy performance at district or city scale. However, many modeling approaches, such as Urban Building Energy Modeling, are particularly demanding from the computational point of view. In order to reduce the time required for calculation, a novel simplification algorithm, called "shoeboxing", is presented. Thanks to a set of geometry indicators selected from a pre-liminary sensitivity screening analysis, the proposed algorithm is able to abstract every building shape into a representative shoebox, considering also urban context, buildings' self-shading and adjacencies. In this work, the performances of the proposed approach are evaluated comparing the simulation outputs of simplified and detailed building models. As a first stage of development, a validation of the algorithm is carried out for stand-alone buildings in three different climatic conditions. Good accuracy has been found for both hourly thermal loads and indoor air temperature profiles, while reducing the simulation time to one third for the simplified models respect to the detailed ones. Even though other simplification approaches in the literature are faster, the described shoeboxing demonstrated ability to provide accurate outcomes also at fine time scales, such as hourly.