Sensitivity of High Spatiotemporal Meteorological Data in AutoBEM Building Energy Modeling

Buildings account for over 30% of US greenhouse gas emissions (GHG), making them a significant driver of human-induced climate change. Simultaneously, the increasing frequency and intensity of extreme heat events is responsible for greater cooling demand due to increased air conditioning (AC) use. While these impacts are significant for all urban areas, they are particularly critical for cities located in already hot climates, such as the Phoenix Metropolitan Area (PMA), where summertime maximum temperatures routinely exceed 40C and summertime minimum temperatures rarely drop below 27C, further driving AC usage at night. Hence, there is a need for improved fidelity of models that can capture these conditions at the building scale. To enhance the simulation of building-level energy consumption, we combine the Automatic Building Energy Modeling (AutoBEM) framework, which generates energy consumption profiles for building stocks, with outputs from the community Weather Research and Forecasting (WRF) model. This allows us to assess AutoBEM’s sensitivity to utility of simulated, gridded, meteorological data compared to the standard method, which relies on weather data from a single location for an entire county. As part of the Department of Energy funded Southwest-Integrated Field Laboratory, we performed AutoBEM simulations for the May 15 through May 31, 2020 period using both the standard approach and WRF-simulated output (with a 1km grid spacing and 1 hour output frequency) for the same time period. Our initial results indicate that, with the finer resolution data, the areas of greater density within the PMA use more total electricity while rural areas are estimated to use less. This trend continues for cooling electricity. The aggregated overall usage of electricity within these higher-density areas is predicted to increase roughly 2-3%, while in lower-density areas it is expected to decrease roughly 2-3%. Our findings underscore the importance of using detailed spatiotemporal meteorological data to generate locally representative energy consumption profiles for buildings across the PMA. Given the anticipated increase in temperatures across the southwestern U.S., it is crucial to develop greater precision in building-level energy consumption profiles as the anticipated rise in AC demand is likely to intensify heat stress, further burdening energy resources, and exacerbate health risks.