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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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August 13, 2019, Filed Under: GitHub

Apples or Oranges? Energy Profile Clustering

In recent years, smart energy meters have been deployed to enable monitoring of energy use data with hourly or sub-hourly temporal resolution. The concurrent rise of information technologies and data analytics enabled the development of novel applications such as customer segmentation, load profiling, demand response, energy forecasting, and anomaly detection. We try to unlock the power of smart meter data to understand the building energy performance comprehensively. More information can be found in https://github.com/intelligent-environments-lab/ProfileClustering.

Publications

– J.Y. Park, X. Yang, C. Miller, P. Arjunan, & Z. Nagy, “Apples or Oranges? Identification of fundamental load shape profiles for benchmarking buildings using a large and diverse dataset”, Applied Energy, vol. 236, pp 1280-1295, 10.1016/j.apenergy.2018.12.025

– J.Y. Park, C. Miller, & Z. Nagy, “A data-driven load shape profile based building benchmarking: Comparing DOE reference buildings with a large metering dataset”, IBPSA, Rome, Italy, September 2019

 

Research Highlight

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 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

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
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nagy@utexas.edu

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