Prioritization Learning for Equitable Residential Decarbonization Investments

Upgrading residential buildings with energy-efficient heating and electrical equipment, including heat pumps and electric cooktops, is a crucial component of transitioning to a more efficient, climate-resilient, and affordable energy system. The residential and commercial building sector is responsible for 31 percent of greenhouse gas emissions in the United States. Moreover, to advance energy equity, priority populations, defined by California Climate Investments (CCI) as the category of homes particularly vulnerable to energy poverty and the impacts of climate change, must be brought to the forefront of transition plans. Doing so will reduce national energy costs and reduce the average energy burden, or the percentage of income spent on utility bills. The California Energy Commission (CEC) has implemented efforts through the Federal Inflation Reduction Act (IRA) to provide affordable retrofitting programs. However, the process of manually selecting which homes to upgrade is too inefficient to meet the pace required for the rapid and robust transition needed to combat climate change. We propose leveraging the emission and savings results from the NREL's ResStock residential upgrade analysis to develop a prioritization algorithm that schedules home upgrades, using CCI priority population spatial data and smart meter data from the CEC. Together, this ranking system uses algorithmic intelligence and user input to optimize energy affordability, emission reduction, and cost savings, thus advancing residential electrification schemes.