Zoltan Nagy

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Assistant Professor

University of Texas at Austin

United States

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About

Dr. Zoltan Nagy is an Assistant Professor at The University of Texas, USA. His research interests include operational performance in building systems, energy management in smart grids, and urban energy modeling. He has recently published articles on topics such as energy management strategies for residential districts and the impact of climate on urban liveability.

Recent Grants

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Workshop on Atmospheric and Urban Digital Twins (AUDT); Austin, Texas

Open Date: 2023-06-15

Close Date: 2024-05-31

Articles (21)

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Collaborators (15)

Alfonso Capozzoli

Associate professor

Politecnico di Torino

ITALY

Zoltan Nagy

Research Interests

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About

Recent Grants

Articles (21)

Developing a weighting scheme for building operational performance: A case study from the Netherlands

EVLearn: extending the cityLearn framework with electric vehicle simulation

Enhancing self-consumption ratio in a smart microgrid by applying a reinforcement learning-based energy management system

A scalable demand-side energy management control strategy for large residential districts based on an attention-driven multi-agent DRL approach

CityTFT: A temporal fusion transformer-based surrogate model for urban building energy modeling

Activity hours: Assessing liveability during heatwaves

CityLearn v2

Overview of occupant-centric KPIs for building performance and their value to various building stakeholders

IMPACT pathways – a bottom-up modelling framework to guide sustainable growth and avoid carbon lock-in of cities

Strategies to Implement a Community-Based, Longitudinal Cohort Study

Effects of occupant thermostat preferences and override behavior on residential demand response in CityLearn

Ten questions concerning occupant-centric control and operations

MERLIN: Multi-agent offline and transfer learning for occupant-centric operation of grid-interactive communities

How spatio-temporal resolution impacts urban energy calibration

Ten questions concerning reinforcement learning for building energy management

Analyzing the impact of COVID-19 on the electricity demand in Austin, TX using an ensemble-model based counterfactual and 400,000 smart meters

Information fusion of stationary, mobile, and wearable consumer-grade sensors to confidently estimate bedroom ventilation rates

Erratum to: Ranking parameters in urban energy models for various building forms and climates using sensitivity analysis (Building Simulation, (2022), 10.1007/s12273-022-0961-5)

Evaluating machine learning models to classify occupants’ perceptions of their indoor environment and sleep quality from indoor air quality

GridLearn: Multiagent reinforcement learning for grid-aware building energy management

Real-world challenges for multi-agent reinforcement learning in grid-interactive buildings

Collaborators (15)

Alfonso Capozzoli

Kaya de Barbaro

Mohamed M. Ouf

Esther Borkowski

Srinivasan Keshav

June Young Park

Constance Crozier

WEI LUO

Tareq Abuimara

Kerry Kinney

Rick Kramer

Runa T. Hellwig

Hicham Johra

Frances Anne Champagne

Aysegul Demir Dilsiz

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