Buildings constitute ~40% of the total energy and ~75% of the total electricity consumed in the U.S. Understanding how building energy demand will evolve is key to planning future energy infrastructure and ensuring reliability. Demand forecasters typically project demand based on economic growth, population change, and efficiency gains, but these approaches typically assume that trends in climate are stationary. We explore two approaches to simulate how building electricity demand will change in response to multiple drivers. In our “top-down” approach, we project building energy demand using the Global Change Assessment Model [GCAM]). In our “bottom-up” approach, we use the Building ENergy Demand (BEND) model. BEND aggregates physically-based simulations of hourly electricity demand for individual residential and commercial buildings designed to be representative of regional characteristics. We compare BEND and GCAM projections of annual building electricity consumption in the western U.S. We simulated a pair of relatively low and relatively high population (SSP3 and SSP5, respectively) and climate scenarios (RCP 4.5 and RCP 8.5) using both models. Both BEND and GCAM confirm that projected population changes have a larger impact on annual building electricity consumption than either RCP 4.5 or RCP 8.5 levels of climate change. RCP 8.5 levels of climate change may increase annual building electricity consumption in certain states by 6-8% on top of potential population changes. In contrast, by year 2050 even SSP3 levels of population change can increase total building electricity consumption by >25% in all states and SSP5 levels of population change could increase total consumption by 50-150%. The two models generally agree because they both respond to changing populations (implemented in the models by expanding total floorspace) in a quasi-linear manner. Nevertheless, the models have divergent pathways of electricity consumption in a number of states, which reflects differences in how the models handle future technological change, behavioral change, and responses to variable fuel prices. This research is a novel, process-based assessment of the contribution of climate change to enhancing other stressors, with the goal of increased predictability of the energy system under multiple stressors.