*Note: This talk will be presented remotely via Zoom and on the Energy Institute's YouTube channel. See access details after the bio.
Bio: Dr. Zoltan Nagy is an assistant professor in the Department of Civil, Architectural, and Environmental Engineering at The University of Texas at Austin, directing the Intelligent Environments Laboratory since 2016. A roboticist turned building engineer, Dr. Nagy’s research interests are in smart buildings and cities, renewable energy systems, control systems for zero emission building operation, machine learning and artificial intelligence for the built environment, complex fenestration systems and the influence of building occupants on energy performance. Prior to joining UT Austin, Dr. Nagy was a senior scientist at the Swiss Institute of Technology (ETH) in Zurich.
Abstract: Reinforcement learning (RL) has gained popularity in the research community as a model-free and adaptive control paradigm for the built environment, especially for building energy control. RL has the potential to enable inexpensive plug-and-play building controllers that can be implemented without necessitating potentially expensive control models (unlike model predictive control), and to coordinate multiple buildings for demand response, load shaping, and load shifting. In this presentation, Dr. Nagy will give an overview of the research activities in the Intelligent Environments Lab at UT Austin, and in particular discuss our CityLearn environment, which facilitates development of Multi-Agent Reinforcement Learning controllers to study interacting buildings, and building-grid interaction.
Note: This talk will be presented remotely via Zoom Webinar and on the Energy Institute's YouTube channel. If using Zoom, viewers must register for an account with Zoom and log in to Zoom using that registration in order to use the meeting link and participate. Faculty, students and staff of UT, please use your personal UT Zoom account.
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https://utexas.zoom.us/s/91742797303
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