The Intelligent Environments Laboratory (IEL), led by Prof. Zoltán Nagy, is an interdisciplinary research group in the Department of Civil, Architectural and Environmental Engineering (CAEE) at The University of Texas at Austin.

IEL advances science, engineering and education towards an intelligent and human-responsive energy infrastructure in the built environment. We develop methods in Machine Learning (Supervised, Unsupervised and Reinforcement), Internet of Things, Data Analytics and System Integration/Deployment, with applications in Occupant-Centric Building Design and Operation and Grid-Interactive Smart Communities.

Latest News

EPRI AI Grand Challenge Webinar
This month’s webinar was focused on reinforcement learning as we had presentations from Dr Henze and his research team at CU Boulder. They discussed the advanced control test bed (ACTB) for buildings as well as opportunities, challenges and cases studies for reinforcement learning control in buildings.
EPRI AI Grand Challenge Webinar
Dr Nagy speaks at Amazon
Dr Nagy was the guest speaker in Amazon’s SSI speaker series on Sustainability. He talked about our research on CityLearn. Amazon is sponsoring AWS compute credits for the CityLearn Challenge 2022
Dr Nagy joins scientific and program committee of CISBAT
Focused on the built environment’s transition to carbon neutrality and sustainability, CISBAT 2023 offers a scientific platform for the presentation of research that pushes the boundaries of energy efficiency and renewable energy technologies.
Dr Nagy receives the 2022 IBPSA-USA Outstanding Researcher Award
This award recognizes one or more individuals who have provided outstanding leadership in the promotion of building simulation through research in the course of their career. The award has be announced at the 2022 ASHRAE/IBPSA-USA BPAC/SimBuild Conference .
Dr Nagy receives the 2022 IBPSA-USA Outstanding Researcher Award
New Paper: Real-world challenges for multi-agent reinforcement learning in grid-interactive buildings
Led by IEL’s awesome PhD Student Kingsley, we have a new paper in Energy and AI. Open Access: https://www.sciencedirect.com/science/article/pii/S2666546822000489 Title: Real-world challenges for multi-agent reinforcement learning in grid-interactive buildings Abstract:
New Paper: Real-world challenges for multi-agent reinforcement learning in grid-interactive buildings
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Meet the Team

Principal Investigators

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Zoltan Nagy

Assistant Professor

Grad Students

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Kingsley Nweye

PhD Student

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Praveen Radhakrishnan

MSc Student

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Ting-Yu Dai

PhD Student

Alumni

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Dr Hagen Fritz

Engineer

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Dr. Aysha Demir

Assistant Instructional Professor

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Dr. Jose Vázquez-Canteli

Machine Learning Engineer

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Dr. June Young Park

Assistant Professor

Research Projects

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All Reinforcement Learning Other
CityLearn

CityLearn

CityLearn is an open source OpenAI Gym environment for the implementation of Multi-Agent Reinforcement Learning (RL) for building energy coordination and demand response in cities. Its objective is to facilitiate and standardize the evaluation of RL agents such that different algorithms can be easily compared with each other. Try it out using our example in Google Colab! More details and installation on GitHub: https://github.com/intelligent-environments-lab/CityLearn

Publications

Code

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