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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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July 9, 2020, Filed Under: News

CFP: RLEM’20 @ BuildSys’20

Together with Prof. Mario Bergés (CMU), we are organizing the first Workshop on Reinforcement Learning for Energy Management in Buildings and Cities (RLEM’20) co-located with ACM BuildSys’20, and held virtually.

We invite original submissions that explore the use of reinforcement learning algorithms for intelligent infrastructure and energy management. Topics of interest include, but are not limited to:

  • Challenges and Opportunities for RL in Building and Cities
  • Explorations of model vs model-free RL algorithms and hybrids
  • Comparisons of RL algorithms to other control solutions, e.g., model-predictive control
  • Frameworks and datasets for benchmarking algorithms
  • Theoretical contributions to the RL field brought about by constraints/challenges in the buildings/cities domains
  • Applications (demand response, HVAC, distributed energy systems, renewable energy integration, occupant integration, traffic scheduling, EV charging)

Workshop Website: http://rlem-workshop.net
Submission portal: http://rlem20.hotcrp.com

Important Dates:
Abstract registration (required): August 3rd, 2020
Submission deadline: August 10th, 2020
Paper Notification: September 21nd, 2020
Camera ready: September 28th, 2020

We look forward to having you participate by submitting a paper and engaging in discussions during the event!

Research Highlight

Thermal Comfort & Smart Buildings

This is an excerpt from our review paper Comprehensive analysis of the relationship between thermal comfort and building control research - A 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

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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
512-555-5555
nagy@utexas.edu

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