U.S. Government Conducts Real Estate Artificial Intelligence Testing For Carbon Emission Reduction - Concludes $28.7M In Possible Annual Energy Savings
New York—October 6, 2022—The U.S. General Services Administration, otherwise known as the U.S. government’s national real estate landlord, in collaboration with the U.S. Department of Energy conducted a multi-year study analyzing Prescriptive Data’s Nantum OS as their Energy Management Information System and Automated System Optimization tool. The study found automating government real estate operations can save the federal government $4.8M to $28.7M a year.
What you need to know:
What is the GSA?
The U.S. General Services Administration (GSA) manages the U.S. government’s nearly 370 million square feet of federal, state and local real estate assets. Established in 1949 to help manage and support the basic functioning of federal agencies, the GSA provides transportation and office space to federal employees, and develops government-wide cost-minimizing policies and other management tasks.
The GSA’s Green Proving Ground (GPG) program leverages GSA's real estate portfolio to evaluate innovative building technologies. The program aims to drive down operational costs in federal buildings and help lead market transformation through the deployment of new technologies.
The GPG uses organizations like the National Renewable Energy Laboratory (NREL) and Lawrence Berkeley National Laboratory (LBNL) to test and analyze new building technologies. The study took place in parallel with an assessment of the same technology in a commercially owned private-sector facility, conducted by the LBNL through the Department of Energy’s Building Technologies Office’s Better Buildings Alliance partners.
What is Nantum OS?
Nantum OS is a building operating system, otherwise known as an Energy Management Information System (EMIS). Nantum OS connects to all building systems: BMS/HVAC, real-time people counting, meters/submeters, indoor air quality sensors, lighting, shade, smart glass, everything in the built environment for real-time situational awareness and historical reporting.
The second main functionality of Nantum OS is its machine learning and artificial intelligence capabilities, otherwise known as Automated System Optimization (ASO). Nantum OS automates building systems to use the least amount of energy and carbon emissions possible, while providing the maximum amount of indoor comfort for occupants, known as Energy Conservation Measures (ECMs). Nantum OS also uses machine learning and artificial intelligence to detect unusual behavior within building systems, known as Fault Detection & Diagnosis (FD&D).
Beyond being recently named the Business Intelligence Group’s 2022 Sustainability Product of the Year, Nantum OS was also in the news this year for becoming JPMorgan Chase’s global AI platform for real estate sustainability.
The Evaluation
The sheer size and variety of assets in the GSA portfolio create unique challenges for facility managers. The GSA chose Nantum OS from 34 other submissions because of its ability to see ‘behind the meter,’ aggregating historical and real-time data through machine learning. Nantum’s software and hardware agnostic approach allowed seamless integration with the GSA’s existing GSALink, Java Application Control Engine (JACE) hardware, and other technologies, sensors, and controls procured through multiple vendors. Nantum’s ability to converge, normalize, analyze and display data from multiple vendors and products into a single view was essential to its selection.
Nantum OS is not a blanket approach, it creates a tailor-made optimization strategy for each individual building, climate, and geography, making it perfect for the GSA’s broad portfolio. Four buildings representative of a range of GSA facilities and climates were chosen as testbeds:
A newly constructed 10-story courthouse in Austin with advanced smart building technology
A historic 5-story office in Dallas recently retrofitted
A large office in Maryland occupied by a single tenant with significant lab space and an on-site data center operational 24/7
A large Federal agency HQ in Washington D.C. that accommodates general office space, training rooms, an auditorium, and auxiliary services.
(As well as an additional 15 buildings that only had data aggregation, visualization, and reporting capabilities, ie. no automation)
Testing was heavily impacted by the COVID-19 pandemic. Nantum OS optimum start times were only briefly active before lockdowns saw daily occupancy plummet. Despite the dramatic fluctuations, Nantum OS was able to predict occupancy and peak demand with accuracy, showing signs of continued improvements as the software learned from daily building behavior. Utility cost per square foot was below GSA averages at the facilities in Texas, eating into cost-effectiveness. Still, analysis shows a payback period of fewer than five years assuming the GSA average facility electricity rate. Nantum’s success and efficacy were in spite of a highly disruptive pandemic, further proving its value as a tool to determine intelligent return-to-work strategies, offering an unforeseen benefit during testing.
The Results
The multi-year analysis performed by both the GSA operations teams and NREL analysis concluded that, “an EMIS with ASO can help us run our buildings better, reduce our energy costs, and keep demand down during peak hours,” according to Tyler Harris, Energy Manager at GSA Public Buildings Service.
A quick recap of the test results:
In terms of ease of installation, GSA staff gave high marks for ease of installation: 4.5 on a scale of 1 to 5.
Building air-handling unit (the most common equipment type for moving air through HVAC systems) automation led to 5% - 11% energy and carbon emission reduction.
Of the 19 buildings testing Nantum OS, 95% of GSA operations team focus groups would continue using Nantum OS for its single unified interface of all building systems, real-time situational awareness, and reporting capabilities.
Nantum OS’ automation capabilities would be a perfect fit for 64% of the GSA’s 504 building portfolio, specifically in markets with higher energy costs.
NREL calculated that when scaled across the GSA portfolio, Nantum OS’ automated System Optimization capabilities would save the U.S. government $4.8M to $28.7M a year.
“We are grateful to spend time with the amazing teams at the GSA, NREL, LBNL, and the U.S. Department of Energy. Nantum OS has always believed the fastest way to decarbonize our national real estate and provide significant energy cost savings is through machine learning and automated operations. The GSA’s study is testament to the future of real estate and the realization of a carbon zero world.”
About Prescriptive Data’s Nantum OS:
Nantum OS is an artificial intelligence and machine learning climate technology designed for commercial real estate operators and sustainability managers. Nantum OS allows real estate managers to visualize building HVAC systems, metering systems, people counting systems, IoT devices (air quality, lighting, shade, smart glass), distributed energy systems (battery storage, fuel cells, on-site generation equipment, solar), and third-party datasets in real time. Nantum OS uses real-time AI/ML algorithms to reduce building energy demand, suppress carbon emissions, and shrink utility costs. Each building on Nantum OS uses the least amount of energy and carbon emissions while delivering the maximum amount of health and comfort to occupied spaces. Learn more at https://www.prescriptivedata.io.
About The GSA
The GSA provides centralized procurement for the federal government, managing a nationwide real estate portfolio of nearly 370 million rentable square feet and overseeing approximately $75 billion in annual contracts. The GSA’s mission is to deliver value and savings in real estate, acquisition, technology, and other mission-support services across government, in support of the Biden-Harris Administration’s priorities. Learn more at GSA.gov and @USGSA.
About National Renewable Energy Laboratory (NREL)
NREL is the U.S. Department of Energy's primary national laboratory for renewable energy and energy efficiency research and development. NREL is operated for the Energy Department by the Alliance for Sustainable Energy, LLC. Learn more at https://www.nrel.gov/.
About Lawrence Berkeley National Laboratory (LBNL)
Lawrence Berkeley National Laboratory (Berkeley Lab) is a Department of Energy (DOE) Office of Science lab managed by University of California. Founded in 1931 on the belief that the biggest scientific challenges are best addressed by teams, the lab and its scientists have been recognized with 14 Nobel Prizes. Today, Berkeley Lab researchers develop sustainable energy and environmental solutions, playing a key role as part of the single largest supporter of basic research in the physical sciences in the United States. Learn more at https://www.lbl.gov/.
