Quantification of HVAC Energy Savings for Occupancy Sensing in Buildings Through an Innovative Testing Methodology

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Program:
SENSOR
Award:
$900,812
Location:
College Station, Texas
Status:
ALUMNI
Project Term:
09/25/2020 - 06/30/2023
Website:

Technology Description:

The Texas A&M University team will develop a testing protocol and simulation suite for assessing the performance of advanced occupancy sensors. The testing protocol and simulation suite will address eight levels of building/occupant scenario diversity: 1) occupant profile, 2) building type and floor plan, 3) sensor type, 4) HVAC controls and modes (e.g., temperature and/or ventilation setback), 5) functional testing diversity, 6) deployment diversity (e.g., sensor location), 7) software diversity (e.g., computation at local vs. hub), and 8) diagnostic diversity (e.g., interpret missing data). The simulation tools will leverage data analytics with built-in machine learning algorithms to determine energy savings. Technical results from the testing and validation work will support technology to market efforts, including codes and standards updates, and help to validate the performance of systems developed through Categories A and B of the SENSOR program.

Potential Impact:

If successful, SENSOR projects will dramatically reduce the amount of energy needed to effectively heat, cool, and ventilate buildings without sacrificing occupant comfort.

Security:

Lower electricity consumption by buildings eases strain on the grid, helping to improve resilience and reduce demand during peak hours, when the threat of blackouts is greatest.

Environment:

Using significantly less energy could help reduce emissions attributed to power generation. In addition, improved interior air quality could help prevent negative effects on human health.

Economy:

ECONOMY: Buildings will require less energy to operate, reducing heating, cooling, and ventilation costs for businesses and families. In addition, better controlled ventilation may lead to improved indoor air quality (ensured by an accurate occupant count, and validated via widespread CO2 detection) that may, in turn, lead to improved occupant productivity.

Contact

ARPA-E Program Director:
Dr. Marina Sofos
Project Contact:
Zheng O'Neill
Press and General Inquiries Email:
ARPA-E-Comms@hq.doe.gov
Project Contact Email:
zoneill@tamu.edu

Related Projects

• Boston University (BU) - Occupancy Sensing for Commercial Venues
• Cornell University - Indoor Occupant Counting Based on RF Backscattering
• Duke University - Dynamic Metasurface Antennas for Detecting Human Presence
• Endeveo - Hotspot Enabled Building Occupancy Sensor
• Iowa State University (ISU) - Simulation, Challenge Testing & Validation of CO2 Technologies
• Matrix Sensors - CO2 Sensor for Demand-controlled Ventilation
• Michigan State University (MSU) - Simulation, Challenge Testing & Validation of Occupancy Recognition & CO2 Technologies Cat A/B
• N5 Sensors - Digital System-on-chip CO2 Sensor
• Purdue University - Microscale Sensors for CO2 Level Monitoring
• Rensselaer Polytechnic Institute (RPI) - Reflected Light Field Sensing for Occupancy Detection
• Scanalytics - Floor Sensors for Building Occupancy Counting
• Syracuse University - Multi-modal Sensor Platform for Occupancy Detection
• Texas A&M University - Low-energy PIR Sensor for Occupancy Detection
• Texas A&M University - Quantification of HVAC Energy Savings for Occupancy Sensing in Buildings Through an Innovative Testing Methodology
• United Technologies Research Center (UTRC) - Platform to Estimate Building Occupancy
• University of Alabama - Quantification of HVAC Energy Savings
• University of Colorado, Boulder (CU-Boulder) - RFID Sensor Network for Human Presence Sensing

Release Date:
01/18/2017