Crucial Electric Building Load Data for Grid Engineers
Engineers now have access to NREL’s comprehensive database of electric load data from residential and commercial buildings across the U.S.
The National Renewable Energy Laboratory (NREL) recently unveiled a treasure trove of building stock data that allows engineers, utilities, and other stakeholders to evaluate the impact of efficiency upgrades and other technologies on end-use electric loads. With 1 million “end-use load profiles” covering all types of U.S. building stock, the dataset brings a valuable toolkit to inform retrofit investments and priorities.
NREL researchers analyze building energy consumption data in the organization’s new End-Use Load Profiles dataset. Image used courtesy of NREL
The resource targets a crucial part of the U.S. emissions landscape. Residential and commercial buildings represented 40% of energy consumption nationwide last year. Electricity end-uses—via sources like heating, air conditioning, and lighting—are a key reason why buildings claim a 30% share of U.S. emissions from greenhouse gas, according to the Environmental Protection Agency (EPA). With billions of dollars in federal funds earmarked for future efficiency upgrades, knowing the scale and scope of end-use emissions is crucial for planning retrofit costs and installing new electrification technologies.
NREL’s dataset features profiles on 900,000 residential and commercial building models with end-use subcategories based on one year of energy consumption in 15-minute intervals. The residential building dataset, called “ResStock,” includes single- and multi-family homes and mobile homes, covering one in every 240 buildings across the U.S. The other dataset, “ComStock,” features 14 common types of buildings covering around two-thirds of commercial floor space nationwide, from offices to strip malls and restaurants to schools, warehouses, and hotels.
Use Cases for Grid Engineers
Several data-sharing partners contributed to the project, including utilities like Southern Company and Xcel Energy, municipal governments and universities, and private-sector service providers such as kW Engineering and Powerhouse Dynamics.
NREL is already seeing significant demand for the resource, clocking over 12 million downloads of the data files. Jared Landsman, a managing consultant at Energy and Environmental Economics, told NREL that before its data was released, the firm only had limited data on aggregate building stock loads. Now, its building studies can incorporate a more comprehensive picture of decarbonization strategies.
The dataset helps engineers interested in electrification as they weigh the upfront cost and benefits of upgrading electrical panels and other aspects of the grid. The California Public Utility Commission is using the data to simulate electrification rate design, while the New York Independent System Operator is building it into its long-term load forecasts. Rewiring America is using it to make state-based prioritizations for the Inflation Reduction Act’s efficiency rebates.
Meanwhile, California-based startup WattCarbon is using the resource to help commercial building owners identify carbon reduction strategies. The company uses NREL’s open-source data to calculate savings based on electricity and natural gas information from buildings.
Modeled end-use data on residential loads in Texas. Image used courtesy of NREL
End-Use Energy Profiles
Because most building energy data is unstandardized, the process of modeling the end-use profiles was a massive undertaking, requiring four years and dozens of team members. Elaina Present, an NREL researcher who helped lead the project, stated that before the dataset was created and made public, building energy consumption data was limited, expensive, or unavailable (partially due to nondisclosure agreements). Often, the information was decades outdated or only available in some locations.
The NREL team developed tools for modeling U.S. residential and commercial building stock to simulate baseline energy usage across heating/cooling, lighting, appliances, air ventilation, and plug and process loads. They then compared the baseline load profiles to data from 11 utilities and over 2 million power meters, along with state natural gas information.
The modeling showed that adding HVAC nighttime behavior variability affected the end-use profile of medium-sized office stock in Portland, Oregon. Image used courtesy of NREL (Page 145, Figure 137)
The dataset, which was processed using a supercomputer, can help prioritize specific electrification investments, analyze emissions by technology, and determine utility resource planning and load forecasting based on regional climate trends. For example, residential housing stock in Ohio could reduce energy use by 68% with basic insulation and air sealing upgrades alongside retrofits for more efficient appliances and electric heat pumps. Meanwhile, Texas’s climate and building data revealed how similar improvements could cut its consumption almost in half.
NREL plans to expand the models to other areas of electrification, such as electric vehicle charging systems, relevant to power system engineers, grid operators, and state/local governments.
NREL’s scenario-based models helped Ohio stakeholders pinpoint strategies to significantly reduce energy consumption in the state’s residential building stock. Image used courtesy of NREL
Federal Funding for Building Efficiency Improvements
NREL’s dataset can inform much-needed efficiency improvements across the U.S. building landscape. According to the EPA, the average commercial building wastes about one-third of the energy it consumes. By 2035, about 75% of American buildings will be new or renovated amid the ongoing energy transition.
The federal government is fueling heaps of cash into the effort. The Inflation Reduction Act earmarks over $300 billion for clean energy and weatherization investments. The latest opportunity is a $400 million formula grant program helping states advance building energy codes with increased efficiency.