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Intelligent Environments Laboratory

The University of Texas at Austin
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    • Prof. Zoltan Nagy, PhD
    • June Young Park
    • José Ramón Vázquez-Canteli
    • Megan K. McHugh, MSE
    • Ayşegül Demir Dilsiz
    • Hagen Fritz
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June 28, 2018, Filed Under: News, Publication

ASHRAE 2018—Annual Meeting Houston

Photo: @healthyheating

Dr Nagy spoke at the 2018 edition of the annual ASHRAE conference in Houston. He discussed our Bluetooth methodology for occupancy detection and showcased an 8% reduction of the chilled water supply based on the data from a $10 device.

Z. Nagy, J. Vazquez-Canteli, J.Y. Park, “Using Bluetooth Based Occupancy Estimation for HVAC Set-back to Reduce Energy Consumption in Buildings” in Proc. ASHRAE Annual Conference 2018, Houston, TX, USA

Presentation slides are here and available until July 28th, 2018

Abstract: Buildings account for approximately 40% of the national energy consumption in the U.S., mainly due to heating, ventilation and air-conditioning (HVAC). Occupancy based building control can save up to 20-50% of energy compared to conventional building strategies. In this work, we present an occupancy detection method based on the Bluetooth (BT) signal of mobile devices to infer the occupancy schedule in a university building. The advantages of BT are robustness, low power consumption and low cost. Recent work has relied on the iBeacon technology, which requires the installation of specific smart phone applications. In our work, we rely on a less intrusive approach by searching the environment for BT devices. Compared to traditional occupancy sensors in buildings, such as passive infrared sensors (PIR), which provide a motion detection signal, BT has the advantage of providing a true occupancy signal, provided that the BT functionality is activated. Furthermore, the integration of BT with other communication protocols, such as WiFi, allows to monitor and save the occupancy data to the building automation system (BAS) for further analysis using data mining techniques. In this work, we explore the application of BT detection on a building level, using off-the shelf, low-cost BT sensors. While the exact number of detected people can only be estimated, we hypothesize that this approach is sufficient to detect the major occupancy hours of the building, when the sensors are placed in the vicinity of the major entrances of the building. In addition, we monitor the consumption of chilled water used for cooling, and the outdoor temperature as provided by the BAS. This allows us to estimate potential energy savings from temperature set-backs based on the average occupancy rates for every day of the week, without seriously compromising thermal comfort. We propose customized daily set-back strategies for this particular building, and estimate the resulting energy savings. Given that the number of BT devices is increasing significantly, and that BT is increasingly activated on the devices for other services (communication, headphones, car interface, etc), our approach provides a fast to deploy (< 15min), low-cost and scalable solution to retrofit a building with occupancy detection and potential savings.

Research Highlight

Reinforcement Learning in the Built Environment

We develop reinforcement learning techniques for energy efficient operation of buildings and systems without the need for mathematical models. Despite Read more 

About Us

The Intelligent Environments Laboratory (IEL), led by Prof. Zoltán Nagy, is an interdisciplinary research group within the Building Energy & Environments (BEE) and Sustainable Systems (SuS) Programs of the Department of Civil, Architectural and Environmental Engineering (CAEE) in the Cockrell School of Engineering of the University of Texas at Austin.

The aim of our research is to rethink the built environment and define Smart Buildings and Cities as spaces that adapt to their occupants and reduce their energy consumption.

We combine data science with building science and apply machine learning to the building and urban scale

Take a look at our projects !

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air handling unit Annex 79 architecture artificial neural network Bluetooth city learn Community engaged research earthquakes environmental monitoring fault detection and diagnostics HVAC integrated design intelligent energy management Lighting Control machine learning Megan McHugh multi-agent systems Occupancy Occupant Centered Control Reinforcement Learning Review Smart Building smart city teaching Thermal Comfort
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UT Energy App – Privacy Policy

Fault detection and diagnostics of air handling units using machine learning and expert rule-sets

Reinforcement Learning in the Built Environment

Reinforcement learning for urban energy systems & demand response

Multi-Agent Reinforcement Learning for demand response & building coordination

IEA-EBC Annex 79: Occupant Centric Design and Operation of Buildings

People

  • Prof. Zoltan Nagy, PhD
  • June Young Park
  • José Ramón Vázquez-Canteli
  • Megan K. McHugh, MSE

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air handling unit Annex 79 architecture artificial neural network Bluetooth city learn Community engaged research earthquakes environmental monitoring fault detection and diagnostics HVAC integrated design intelligent energy management Lighting Control machine learning Megan McHugh multi-agent systems Occupancy Occupant Centered Control Reinforcement Learning Review Smart Building smart city teaching Thermal Comfort
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