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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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November 23, 2018, Filed Under: Past Projects I

Wireless Sensor Networks for Building Retrofit

Retrofit measures are an effective means to improve both the heating energy and carbon footprint of a building. On one hand, reducing the losses through the envelope reduces the energy consumption. On the other hand, updating the heating from a fossil-fuel based system to an emission-free one bears the potential for CO2-emission free operation. This project explores how simple measurements can provide useful insight into the energetic behavior of a building and allow to derive models to predict the effect of the retrofit measures.

aufbau06aussen retrofitResults2

Publications

Z. Nagy, D. Rossi, Ch. Hersberger, S.D. Irigoyen, C. Miller, and A. Schlueter, Balancing Envelope and Heating System Parameters for Zero Emissions Retrofit using Building Sensor Data, Applied Energy, Vol. 131, October, 2014
doi: 10.1016/j.apenergy.2014.06.024

C. Miller, Z. Nagy, and A. Schlueter et. al, BIM-Extracted EnergyPlus Model Calibration for Retrofit Analysis of a Historically Listed Building, Building Simulation Conference, ASHRAE/IBPSA-USA, Atlanta, GA, USA, 2014

Research Highlight

Whole Communities—Whole Health

CHANGING THE WAY SCIENCE HELPS SOCIETY THRIVE IS OUR GRAND CHALLENGE Traditional research studies take “snapshots” of people’s lives at different 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 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 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
ITS

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

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