Z
Zoltan Nagy
Researcher at University of Texas at Austin
Publications - 123
Citations - 4381
Zoltan Nagy is an academic researcher from University of Texas at Austin. The author has contributed to research in topics: Computer science & Reinforcement learning. The author has an hindex of 29, co-authored 106 publications receiving 2799 citations. Previous affiliations of Zoltan Nagy include University of Colorado Boulder & ETH Zurich.
Papers
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Reinforcement learning for demand response: A review of algorithms and modeling techniques
TL;DR: In this paper, a review of the use of reinforcement learning for demand response applications in the smart grid is presented, and the authors identify a need to further explore reinforcement learning to coordinate multi-agent systems that can participate in demand response programs under demand-dependent electricity prices.
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Robotics in the Small, Part I: Microbotics
TL;DR: An overview of the field of microrobotics, including the distinct but related topics of micromanipulation andmicrorobots, is provided, while many interesting results have been shown to date.
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Using machine learning techniques for occupancy-prediction-based cooling control in office buildings
TL;DR: A demand-driven control strategy is proposed that automatically responds to occupants’ energy-related behavior for reducing energy consumption and maintains room temperature for occupants with similar performances as a static cooling.
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Comprehensive analysis of the relationship between thermal comfort and building control research - A data-driven literature review
June Young Park,Zoltan Nagy +1 more
TL;DR: In this article, the authors review both research fields and their relationship using a data-driven approach, and identify potential research directions in terms of bridging the two fields in order to balance the two domains.
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Automated daily pattern filtering of measured building performance data
TL;DR: In this paper, a day-typing process that uses Symbolic Aggregate approXimation (SAX), motif and discord extraction, and clustering to detect the underlying structure of building performance data is presented.