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Leveraging fine-grained occupancy estimation patterns for effective HVAC control

09.10.26 | Research Foundation for the State University of New York

Background :

One of the biggest energy consumers is HVAC. In 2017 alone, approximately 30% of energy consumption in U.S. commercial buildings was used for HVAC. Conventional HVAC systems are inefficient and often lead to excessive energy use. Usually, building operators use a static schedule to control HVAC systems without accounting for how many people use the building at different times of the day. Many HVAC systems operate based on an assumed maximum occupancy in each room, which isn't always the case. This can lead to significant energy waste, for example, an HVAC system providing ventilation for 30 people when there are only 10 in a room. Such widely used HVAC control designs miss opportunities to perform more accurate and efficient control. Therefore, there is a need for a more efficient process for controlling HVAC systems.

Technology Overview :

This technology relies on predicted occupant counts and accounts for misprediction costs to minimize energy consumption. This process will first update the thermal state of multiple zones in the building based on a building thermal model and information from the building's temperature sensors. Then, the predicted occupant counts for the upcoming time slots for each zone in the building are updated using the actual occupancy counts for those zones. Finally, after these two variables are updated, the misprediction-type distribution will be updated for the occupancy numbers in each zone and time slot. This distribution will account for true negatives, false positives, false negatives, and true positives, yielding the expected total misprediction cost based on the predicted occupant counts and the distribution. This will determine HVAC power for each zone to optimize occupant thermal comfort (weighted by predicted occupant counts) while minimizing total expected misprediction costs.

Advantages :

Applications :

Intellectual Property Summary : 11719458

Stage of Development :

Licensing Status : Available

Licensing Potential : Development partner - Commercial partner - Licensing

About the Research Foundation for the State University of New York:

As the nation's largest research foundation supporting the nation's largest public university system, The Research Foundation for SUNY powers research and innovation to address today's most pressing problems and shape a better future for generations to come. The Research Foundations supports SUNY researchers leading the way globally in AI for the public good, quantum technologies, next-generation semiconductors, biotech and medicine, energy and climate solutions, and more. The Research Foundation for SUNY is a private, nonprofit educational corporation that is tax-exempt under Internal Revenue Code (IRC) Section 501(c)(3). To learn more, please visit us online at rfsuny.org .

About the State University of New York

The State University of New York is the largest comprehensive system of higher education in the United States, and more than 95 percent of all New Yorkers live within 30 miles of any one of SUNY’s 64 colleges and universities. Across the system, SUNY has four academic health centers, five hospitals, four medical schools, two dental schools, a law school, the country’s oldest school of maritime, the state's only college of optometry, 12 Educational Opportunity Centers, over 30 ATTAIN digital literacy labs, and manages one US Department of Energy National Laboratory. In total, SUNY serves about 1.7 million students across its portfolio of credit- and non-credit-bearing courses and programs, continuing education, and community outreach programs. SUNY oversees nearly a quarter of academic research in New York. Research expenditures system-wide are nearly $1.5 billion in fiscal year 2025, including significant contributions from students and faculty. There are more than three million SUNY alumni worldwide, and annually one in three New Yorkers who earn a college degree is a SUNY alum. To learn more about how SUNY creates opportunities, visit suny.edu .

Keywords

Contact Information

Matthew Sheiffer
Research Foundation for the State University of New York
matthew.sheiffer@rfsuny.org

Source

This article is based on a news release from Research Foundation for the State University of New York. BrightSurf curates and republishes science news from research institutions worldwide; the original release is linked below.

How to Cite This Article

APA:
Research Foundation for the State University of New York. (2026, September 10). Leveraging fine-grained occupancy estimation patterns for effective HVAC control. Brightsurf News. https://www.brightsurf.com/news/LDE2XPK8/leveraging-fine-grained-occupancy-estimation-patterns-for-effective-hvac-control.html
MLA:
"Leveraging fine-grained occupancy estimation patterns for effective HVAC control." Brightsurf News, Sep. 10 2026, https://www.brightsurf.com/news/LDE2XPK8/leveraging-fine-grained-occupancy-estimation-patterns-for-effective-hvac-control.html.