Data modeling tool can project energy, efficiency savings for residential, commercial buildings
Projecting the impact of a building’s transition to clean energy is a complicated process that involves complex data—one that might be difficult to translate for an audience of constituents at an annual town meeting or public forum. A new data set published by the National Renewable Energy Laboratory (NREL) could help administrators better quantify the fiscal and environmental impact of clean energy and efficiency upgrades can have, and help building managers understand where to invest their time and energy.
“Buildings are responsible for 40% of total energy use in the United States, including 75% of all electricity use and 35% of the nation’s carbon emissions,” reads a brief about the data set published by NREL. “Although today’s decarbonization efforts often focus on renewable electricity or electric vehicles, decarbonizing the building stock is also essential.”
The data set, called the End-Use Load Profiles, details current energy usage in buildings and can project future energy usage. Prior to NREL’s tool, that information wasn’t easy to come by, according to the brief.
It includes many sets of hundreds of thousands of modeled building energy-use profiles that reflect energy consumption and how usage might change under various ‘what-if’ efficiency scenarios. Specifically, it models 900,000 representative residential and commercial building—550,000 residential and 350,000 commercial. The brief notes that the energy usage of one of every 240 residential buildings is modeled. For commercial buildings, the models represent approximately 65% of commercial floor space in the United States.
“This was a significant undertaking over the course of about four years by around 40 people,” said Elaina Present, a researcher in NREL’s Residential Buildings Research Group and a member of the project. “And it was hard. But as a national lab, with the expertise we had, we were able to do it.”
NREL worked with 42 organizations to curate the profiles, which include detailed specifications about unique buildings like schools. Practically, the data sets are intended to equip households and communities with knowledge about a “series of unknowns” related to energy.
“How much will it save on energy bills? Is the emissions benefit worth the upfront cost? To what extent will electrical panels or the grid need to be upgraded?” said Cora Wyent, director of research for Rewiring America. “NREL’s data sets are specifically tailored to provide answers to all of these questions, in turn facilitating building decarbonization and all of the cost savings and emissions, workforce, and health cobenefits it brings.”
As an example of the tool’s capability and potential for application, researchers modeled the impact of energy upgrades on Ohio’s residential housing stock. If high-efficiency electric appliances and heat pumps were used along with basic insulation and air sealing, the state could reduce its total energy use by 68%. In Texas, which has a very different climate and housing stock, the same changes would reduce energy consumption by 45%.
“In this way, the profiles can be used to tease apart which energy efficiency measures will be most effective in different regions—enabling better, more informed decision-making,” the brief says. “Because efficiency and electrification retrofits represent a major opportunity to reduce U.S. energy consumption and carbon emissions, even small improvements in decision-making can have a significant impact.”
To date, the brief notes, there have been more than 12 million downloads of individual data files and nearly 3,000 unique users of the online data viewer. The California Public Utilities Commission, for example, is using the load profiles to simulate the bill impacts of a rate designed to promote electrification. And the New York Independent System Operator is incorporating the load profiles into their long-term load forecasts for millions of people.
For more information and to access and view the date, visit NREL’s website.