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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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September 20, 2019, Filed Under: GitHub

Occupancy detection and estimation

As occupants and their behaviors are responsible for a significant share of energy consumption, it is important to gather occupancy information for operating infrastructures efficiently. In fact, gathering and understanding occupancy patterns are considered as grand challenges for smart buildings and cities. Capitalizing on the pervasiveness of mobile devices with wireless functionalities, e.g., Bluetooth, WiFi, we try to discover various opportunities of using occupants’ mobile devices to infer building occupancy using advanced data analytics. More information can be found in https://github.com/intelligent-environments-lab/occupancy_BT.

Publications

– J.Y. Park, E. Mbata, & Z. Nagy, “Good to see you again: Capture and recapturemethod on mobile devices to estimate occupancy profiles”, ACM Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys), New York, NY, November 2019

– J.Y. Park, T. Dougherty, H. Fritz, & Z. Nagy, “LightLearn: An Adaptive and OccupantCentered Controller for Lighting based on Reinforcement Learning”, Building and Environment, vol. 147, pp 397-414, 10.1016/j.buildenv.2018.10.028

– J.Y. Park, T. Dougherty, & Z. Nagy, “A Bluetooth based occupancy detection for buildings”, ASHRAE/IBPSA SimBuild (BPACS), Chicago IL, September 2018

– Z. Nagy, J.R. Vazquez-Canteli, & J.Y. Park, “Using Bluetooth Based OccupancyEstimation for HVAC Set-back to Reduce Energy Consumption in Buildings”, ASHRAE, Houston, TX, June 2018

 

 

 

Research Highlight

UT Energy App – Privacy Policy

  The Intelligent Environments Laboratory is releasing the first version of the UT Energy App. This app is intended to provide a way to 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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Research

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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

Tags

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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nagy@utexas.edu

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