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Intelligent Environments Laboratory

The University of Texas at Austin
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  • People
    • Prof. Zoltan Nagy, PhD
    • June Young Park
    • José Ramón Vázquez-Canteli
    • Megan K. McHugh, MSE
    • Ayşegül Demir Dilsiz
    • Hagen Fritz
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Courses

“Education is the most powerful weapon which you can use to change the world
– Nelson Mandela


CourseBuilding Environmental Systems HVAC Design Sustainable Building Design Smart Buildings and Cities
NrARE 346NARE 346PCE 397CE 397
LevelUndergraduateUndergraduate/GraduateGraduateGraduate
Short Content

  • Thermal Comfort

  • Heating & Cooling Loads

  • Psychrometrics

  • Electrical Circuits


  • Psychrometric Processes

  • Refrigeration Cycles

  • Heat Exchangers

  • Duct Design


  • Thermal Comfort

  • Passive Building Design

  • Renewable Energy Integration

  • Advanced HVAC Systems


  • Data Analytics

  • Machine Learning

  • Theory & Practice

  • Bring Your Own Data

OfferedFallFallSpring (bi-annual)Spring (bi-annual)
Taught inFa17Fa16,17,18Sp18Sp17, Fa18

HVAC Design (ARE346P | CE389H)

Fundamentals of design of Heating, Ventilation, and Air Conditioning systems

Goals:

  1. Apply fundamental physical principles to HVAC design
  2. Describe and size each component in an HVAC system
  3. Design HVAC systems based on manufacturer’s datasheets
  4. Contrast residential systems with commercial systems and use appropriate design
    techniques for each type of system
  5. Solve HVAC design problems with high-quality references

Course material supply and course communication is via canvas.

Taught in: Fall 2016, Fall 2017


Building Environment Systems (ARE 346N)

Analysis and design of building air conditioning systems; heating and cooling load calculations; air side systems analysis; air distribution; building electrical requirements; electrical and lighting systems.

Goals:

  1. Describe the role of building environmental systems in building planning and design
  2. Research and critically analyze claims about building environmental systems made by salespeople, subcontractors, and building designers.
  3. Calculate building heating, ventilating, and air conditioning loads and specify HVAC equipment for residential and light commercial construction.
  4. Acquire design requirements for building electrical systems and design basic systems
  5.  List characteristics of different lamps, describe building lighting designs and their consequences and demonstrate knowledge of lighting design principles.

Course material supply and course communication is via canvas.

Taught in: Fall 2017


Sustainable Building Design (CE 397)

This graduate course will explore sustainable building design from 4 perspectives

  • Human: Thermal comfort
  • Architectural: Passive solar design
  • Energy: Renewable energy integration
  • Technology: Radiant Heating & Cooling

Approximately half of the course will be lectures, and short homework problems. The other half will be a design project, together with a similar course in the School of Architecture.

Course material and communication is via canvas.

Taught in: Spring 2018


Smart Buildings and Cities (CE 397)

This graduate course will focus on the use of data of buildings and cities for the design of a sustainable built environment.

Goal: Introduce students to machine learning algorithms as they apply to analyze data, e.g. energy consumption, on buildings and cities. In addition to the theoretical introduction, students will gain practical knowledge on the advantages and disadvantages of the different algorithms as well as their applicability.

Approximately 2/3 of the course will be lectures, in-class programming as well as homework problems using Matlab. The final 1/3 part will be an application project. Participants are encouraged to bring data from their own area of study for further analysis.

Course material supply and course communication is via canvas.

Taught in: Spring 2017

Research

  • All Projects

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
ITS

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

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