Urban-scale building energy modeling under future climate scenarios: a scalable workflow and insights from Nassau County, New York

Abstract

Urban- or large-scale building energy modeling is essential to understand how climate change may affect energy demand in urban or populated areas. Past research, however, is generally small-scale, of low spatial resolution, or not highly transferable to future climate conditions. In this research, an automated high-performance computing (HPC) workflow is created and implemented to simulate energy use in 346,827 buildings in Nassau County, New York, under present and future 2099 climate scenarios (RCP8.5). The simulation platform combines large-scale physics-based modeling with morphed Typical Meteorological Year data in order to compute site and source energy use, heating and cooling loads, and correlated carbon emissions. Results for Nassau County show that while mean site energy use decreases by 20.1% due to lower heating loads during milder winters, source energy use increases by 5.36% due to increased electrical demand for cooling. Non-residential buildings, although constituting only 10% of the building stock, are the source for over 50% of future total source energy consumption in the scenario considered. These outcomes underscore the importance of grid decarbonization as well as such focused efficiency measures, particularly for electricity-reliant types of buildings. The proposed workflow is applicable in other fields, scalable, and can potentially inform long-term urban energy and climate resilience planning.