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

CityLearn is Digital Public Good
📣 We are proud to announce that Digital Public Goods Alliance has recognized CityLearn (www.citylearn.net) as digital public good!!
CityLearn is Digital Public Good

Meet the Team

Principal Investigators

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Zoltan Nagy

Assistant Professor

Grad Students

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Allen Wu

Undergraduate Research Assistant (CS)

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Calvin Lin

MSc Student

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Kingsley Nweye

PhD Student

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Ting-Yu Dai

PhD Student

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Yara Almilaify

MSc Student

Alumni

Alessandra Guerini

Visiting MSc Student

Avinash Damania

Undergraduate Research Assistant (CS)

Cassandra Prince

GLUE Student

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Dr Hagen Fritz

Research 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

Edward Mbata

Undergraduate Research Assitant (CAEE)

Eli Ramthun

MSc Student

Ignacio Aguirre Panadero

MSc Exchange Student (Spain)

Ignacio Perez de Rojas

MSc Exchange Student (Spain)

Jacob Wright

Undergraduate Research Assistant (CAEE)

Julien Brown

Undergraduate Research Assistant (Architecture)

Kaylee Trevino

Undergraduate Research Assistant (ECE)

Lauren Chen

Undergraduate Research Assistant (ME)

Megan McHugh

MSc Student

Negar Sheikh Mohammadi Khamseh

Visiting PhD Student (Politecnico di Torino)

Nicolas Castillo-Castejon

MSc Exchange Student (Spain)

Rachel Schutte

MSc Student (CAEE)

Ritvik Annam

Undergraduate Research Assistant (CS)

Sara Kingman

Undergraduate Research Assistant (CAEE)

Sepehr Bastami

Research Engineer

Silvio Brandi

Visiting PhD student (Politecnico di Torino)

Stepan Ulyanin

Research Assistant (Applied Math)

Tejal Kulkarni

Undergraduate Research Assistant (ECE)

Tung To

Undergraduate Research Assistant (ECE)

Vianey Rueda

MSc Student

Viktor Carp

MSc Exchange Student (ETH Zurich)

Wendy Zhang

Undergraduate Research Assistant

Xiya Yang

Exchange Student (NUS Singapore)

Zhiheng Hu

Visiting MSc Student (CMU)

Research Projects

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Data-Driven Energy Savings Estimation

Data-Driven Energy Savings Estimation

MARTINI is a sMARt meTer drIveN estImation of occupant-derived Heating, Ventilation and Air Conditioning(HVAC) schedules and energy savings that leverages the ubiquity of energy smart meters and Wi-Fi infrastructure in commercial buildings.

IMPACT Pathways for Decarbonization

IMPACT Pathways for Decarbonization

Increasing urbanization puts ever-increasing pressure on cities to prioritize sustainable growth and avoid carbon lock-in, yet available modeling frameworks and tools fall acutely short of robustly guiding such pivotal decision-making at the local level.

UTwin: A digital twin for the UT campus

UTwin: A digital twin for the UT campus

In partnership with Bentley Systems, we are working on a digital twin of the UT campus. We are planning to us it to address the uncertainty in energy demand projections, develop future planning scenarios, and provide guidelines for stakeholders and decision makers to prepare the campus for the effects of climate change.

Occupant-Centric Control

Occupant-Centric Control

An ideal controller should adapt itself to the preferences of the occupant and the environmental conditions. We developed LightLearn and HVACLearn, which are occupant centric controller (OCC) for lighting and HVAC, respectively, based on Reinforcement Learning (RL).

(BEVO) Beacon: A rapidly-deployable and affordable indoor environmental quality monitor

(BEVO) Beacon: A rapidly-deployable and affordable indoor environmental quality monitor

Indoor Air Quality (IAQ) monitoring is essential to assess occupant exposure to the wide range of pollutants present in indoor environments. Accurate research-grade monitors are often used to monitor IAQ but the expense and logistics associated with these devices often limits the temporal and spatial scale of monitoring efforts.

GridLearn

GridLearn

Led by CU Boulder - Griffin Lab (Dr Kyri Baker): Increasing amounts of distributed generation in distribution networkscan provide both challenges and opportunities for voltage regulation across the network. Intelligent control of smart inverters and other smart building energy management systems can be leveraged to alleviate these issues.

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