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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
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August 25, 2018, Filed Under: Research

Austin building stock under climate change

This project is in collaboration with the Sustainable Built Environment group in the School of Architecture.

Each year the city of Austin stays amongst the top of the list of fastest growing metro areas in the country, with an average increase of 160 residents per day. As a result, the building stock in the city is being renovated or torn down and rebuilt at an extremely aggressive pace in an attempt to keep up with the changing city and its surrounding areas.

A database with accurate information about a city’s building stock (including material property data and years of construction) can help do a sensitivity analysis on urban models using a custom archetype library or similar approach. It can also help make urban energy models more accurate and informed rather than relying on the assumption that randomly assigned data will accurately represent a city if the scale is large enough.

The following is a year-built map of the Austin building stock between highways 360, 183 and 71:

Using such a database, and fused with construction materials, allows us to investigate energy demand. For example, the following figure shows the energy demand of the West campus neighborhood.

 

We use similar maps around Austin’s neighborhoods to investigate the impact of climate change and population increase on energy demand as well as mitigation potentials of retrofitting, efficiency increase, operation control, and urban design.

References

R. Schutte, GIS Map and Building Information Database for the City of Austin Categorized by Neighborhood and Decade Built, MS Report, Dept. Civil, Architectural and Environmental Engineering, UT Austin, Spring 2018

Nicolás Castillo Castejón, 3D Physical models of Austin for energy simulations in future scenarios, MS Thesis, Dept. Civil, Architectural and Environmental Engineering, UT Austin, Spring 2018

Research Highlight

Occupancy detection using Bluetooth

  Gathering occupancy data is considered as one of the grand challenges in building information modeling. Direct occupancy detection methods, 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

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  • 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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301 E Dean Keeton St
Austin, TX 78712
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nagy@utexas.edu

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