According to the U.S. Dept. of Energy’s ORNL (Oak Ridge National Laboratory), there were 125,714,640 residential and commercial buildings in the U.S. consuming approximately $395 billion per year in energy bills, about 73% of the nation’s electricity (80% during peak generation) and causing 39% of the nation’s greenhouse gas emissions. Of those, 122,930,327 (97.8%) buildings have been simulated in a variation of a digital twin, giving the energy profile of those structures.
To do this, ORNL developed the Automatic Building Energy Modeling (AutoBEM) software suite to process multiple types of data, extract building-specific descriptors, generate building energy models, and simulate them on HPC (high performance computing) resources.
When the project began in 2015, there was no single resource to accurately visualize and quantify energy details for each U.S. building. Such a tool would help the buildings sector contribute to the nation’s goal of spurring an equitable clean energy economy and put America on a path to net-zero carbon emissions by 2050. This goal is critical because buildings account for 40% of America’s energy consumption.
Urban planners can use AutoBEM to look at entire blocks and neighborhoods and identify areas that have been historically overlooked in building improvement efforts. Doing so would easily identify communities that could most benefit from building upgrades.
Utilities spend billions of dollars a year based on a signature analysis of electrical profiles. Before this program, no one had the capability to perform that analysis with detailed, building-specific energy modeling at this scale. Individual utilities now have the capability to perform modeling to show the potential of reducing demand and GHG (greenhouse gas) emissions.
Data from the Sky
AutoBEM accesses satellite imagery, street views, and other publicly available data that provide insight into a building’s size and energy makeup, such as the number of windows, building envelope materials, number of floors, heating, ventilation and cooling systems, and roof type. The program gathers those inputs using high-performance computing and creates a building energy model to predict which technologies could be deployed to save energy, including solar panels, heat pumps, smart thermostats, or energy-efficient water heaters.
Although AutoBEM can estimate the building’s makeup and energy performance, it doesn’t see details inside of the building. The interior makeup of each type of building, based on its purpose, is determined by using standard building codes and prototype buildings. For example, a supermarket standard prototype has a certain number of linear feet of deep freezers, a certain number of display cases, similar refrigeration systems, etc.
By 2020, commercial partners were using the tool for tasks ranging from designing energy-efficient buildings and cities to relating energy efficiency to real estate value and risk. International companies like Google are sharing the benefits by making the resulting data publicly available.
Google is using AutoBEM to improve its free Environmental Insights Explorer tool, which launched in 2018 to help cities worldwide recognize sources of greenhouse gas and reduction opportunities. Google is combining its trove of building data with ORNL’s ability to scale up energy models and develop machine-learning algorithms. The company is one of five major firms contributing data, staff time, and equipment to AutoBEM partnerships.
Climate Change Mapping
AutoBEM also incorporates climate change scenarios identified by the Intergovernmental Panel on Climate Change, modeled by the Climate Change Science Institute at ORNL. This feature attracted the attention of partner LightBox, which offers a platform for mapping and analyzing real estate information. LightBox plans to use AutoBEM to model the long-term energy and operation costs of buildings and to support understanding and reporting greenhouse gas emissions, providing valuable information to real estate investors, brokers, lenders and banks, appraisers, engineers, and environmental consulting firms.
LightBox and some other partners will provide benefits to AutoBEM in turn, by contributing data sets like building footprints, interior details, property parcel boundaries, and financial information to improve future simulations.
Other AutoBEM partners include glass manufacturer Cardinal Glass Industries and Bentley Systems, an infrastructure engineering software company. Cardinal Glass, which supplies window manufacturers, is using the tool to understand the energy performance of various window types in different regions and climate scenarios when compared to other efficiency upgrades. Bentley Systems is researching how to leverage city-scale digital twins and building energy models to optimize building design and decarbonization.
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