Modeling the Energy Consumption of U.S. Residential Buildings: ResStock and the Datasets it Produces

Abstract:
Across the country, there's a vast diversity in the age, size, construction practices, installed equipment, appliances, and resident behavior of the housing stock, not to mention the range of climates. These variations have hindered the accuracy of predicting savings for existing homes.


With support from the U.S. Department of Energy (DOE), NREL developed ResStock. It's a versatile tool that takes a new approach to large-scale residential energy analysis by combining:

This combination achieves unprecedented granularity and, most importantly, accuracy in modeling the diversity of the housing stock and the distributional impacts of building technologies in different communities.

ResStock leverages DOE's open-source building energy modeling ecosystem of OpenStudio® and EnergyPlus™. With NREL supercomputing, the ResStock team has run more than 20 million simulations using a statistical model of housing stock characteristics. This data has helped researchers uncover $49 billion in potential annual utility bill savings through cost-effective energy efficiency improvements.


Bio:
Elaina is a researcher in the Residential Buildings Research Group at the National Renewable Energy Laboratory in Golden, CO. She conducts building stock energy modeling, data analysis, and emissions analysis in pursuit of better understanding current household energy consumption patterns, how they may change in the future, and their relationship with the evolving electric grid. She was the data acquisition lead for the “End-use load profiles for the U.S. building stock” project and is a developer on the ResStock tool.

Prior to joining NREL in 2019, Elaina was part of the Center for the Built Environment at UC Berkeley. She previously conducted technical analyses for the federal appliance standards program.