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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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January 31, 2019, Filed Under: Research

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 collect information on climate/temperature and consequent comfort levels for students in UT classrooms and other campus buildings. This data will be used Read more 

January 30, 2019, Filed Under: Research

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

To reduce HVAC energy inefficiencies, fault detection and diagnostics (FDD) has become a growing field of interest. In particular, air handling units (AHU), devices that circulate air and regulate room temperature and humidity, are the primary focus of most HVAC FDD systems. A data-driven FDD for Read more 

November 29, 2018, Filed Under: Research

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 the many advantages of RL for application in the built environment, many challenges remain, and are explored in our research. (1) As RL is a Read more 

November 25, 2018, Filed Under: Research

Reinforcement learning for urban energy systems & demand response

Demand response, or demand-side management, improves grid stability by increasing demand flexibility, and shifts peak demand towards periods of peak renewable energy generation by providing consumers with economic incentives. Reinforcement learning has been utilized to control diverse energy systems Read more 

November 24, 2018, Filed Under: Research

Multi-Agent Reinforcement Learning for demand response & building coordination

We have introduced a new simulation environment that is the result of merging CitySim, a building energy simulator, and TensorFlow, a powerful machine learning library for deep learning. This new simulation environment has the potential for developing building energy scenarios in which machine Read more 

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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 !

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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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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301 E Dean Keeton St
Austin, TX 78712
512-555-5555
nagy@utexas.edu

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