Development of a high-resolution top-down model to estimate actual household-level heat pump electricity consumption

Heat pumps can play an important part in decarbonizing the residential sector due to their use of electricity instead of fossil fuels, and their high efficiency, which often exceeds 100%. However, heat pump performance and energy savings vary with climate and individual household energy usage. Recent studies have used geospatial models to estimate potential heat pump energy consumption across the United States. Yet most studies use generic and oversimplified heat pump models. We contribute to this field with a geospatial model based on manufacturer data and measured test data for 16 different R410A, high efficiency, variable speed compressor heat pumps. Using linear regression, we estimate a market average of COP with respect to ambient temperature. From this, we can identify the variation in efficiency with temperature across this technology class. We also use linear regression to estimate demand for heating and cooling as a function of ambient temperature and household characteristics. We compare the performance of both the predicted energy demand and heat pump efficiency against measured data from a heat pump-equipped house in West Lafayette, Indiana, and find that the model predicts daily heat pump electricity consumption with 27.8% relative error, comparable to other building simulation models. By incorporating high-resolution geospatial data inputs, such top-down models can still maintain a large scope across technologies and diverse climates while increasing spatial and temporal resolution.