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Intelligent Environments Laboratory

The University of Texas at Austin
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    • Prof. Zoltan Nagy, PhD
    • June Young Park
    • José Ramón Vázquez-Canteli
    • Megan K. McHugh, MSE
    • Ayşegül Demir Dilsiz
    • Hagen Fritz
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November 23, 2018, Filed Under: Past Projects I

Adaptive Solar Facade

The building facade greatly impacts how much heat has to be added or removed in order to retain a comfortable indoor climate. Given that these processes vary throughout the year, the A/S Research Team has developed an adaptive solar facade. The project combines recent developments in architecture, energy technology and robotics. It is the largest object showcasing soft robotics in architecture.

Publications

Z. Nagy, B. Svetozarevic, P. Jayathissa, M. Begle, J. Hofer, G. Lydon, A. Willmann, and A. Schlueter, The Adaptive Solar Facade: From Concept to Prototypes, Frontiers of Architectural Research, in Press, 2016
doi: 10.1016/j.foar.2016.03.002

P. Jayathissa, M. Jansen, N. Heeren, Z. Nagy, and A. Schlueter, Life Cycle Assessment of Dynamic Building Integrated Photovoltaics, Solar Energy Materials & Solar Cells, in Press, 2016

D. Rossi, Z. Nagy, and A. Schlueter, Soft Robotics for Architects, Soft Robotics, 2014, Vol.1, No. 2, pp. 147-153
doi: 10.1089/soro.2014.0006

D. Rossi, Z. Nagy, and A. Schlueter, Adaptive Distributed Robotics for Environmental Performance, Occupant Comfort and Architectural Expression, Int’l. Journal of Architectural Computing, vol.10, No.3, pp. 341-360, September 2012
doi: 10.1260/1478-0771.10.3.341

B. Svetozarevic, Z. Nagy, J. Hofer, D. Jacob, M. Begle, E. Chatzi and A. Schlueter, SoRo-Track: A Two-Axis Soft Robotic Platform for Solar Tracking and Building-Integrated Photovoltaic Applications, in Proc. IEEE Int. Conference on Robotics and Automation (ICRA), 2016, Stockholm, Sweden

P. Jayathissa, Z. Nagy, N. Offeddu, and A. Schlueter, Numerical simulation of energy performance, and construction of the adaptive solar facade., Advanced Building Skins, 2015

J. Hofer, A. Groenewolt, P. Jayathissa, Z. Nagy, and A. Schlueter, Parametric analysis and systems design of dynamic photovoltaic shading modules, EU PVSEC, 2015

B. Svetozarevic, Z. Nagy, D. Rossi, and A. Schlueter, Experimental Characterization of a 2-DOF Soft Robotic Platform for Architectural Applications, Robotics: Science and Systems, Workshop on Advances on Soft Robotics, Berkley, CA, USA, 2014

Z. Nagy, D. Rossi and A. Schlueter, Sustainable architecture and human comfort through adaptive distributed systems, IEEE Pervasive Computing and Communications Workshop (PERCOM), March, 2012, Lugano, Switzerland
doi: 10.1109/PerComW.2012.6197520

Research Highlight

Multi-Agent Reinforcement Learning for demand response & building coordination

We have introduced a new simulation environment that is the result of merging CitySim, a building energy simulator, and TensorFlow, a powerful machine Read more 

About Us

The Intelligent Environments Laboratory (IEL), led by Prof. Zoltán Nagy, is an interdisciplinary research group within the Building Energy & Environments (BEE) and Sustainable Systems (SuS) Programs of the Department of Civil, Architectural and Environmental Engineering (CAEE) in the Cockrell School of Engineering of the University of Texas at Austin.

The aim of our research is to rethink the built environment and define Smart Buildings and Cities as spaces that adapt to their occupants and reduce their energy consumption.

We combine data science with building science and apply machine learning to the building and urban scale

Take a look at our projects !

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air handling unit Annex 79 architecture artificial neural network Bluetooth city learn Community engaged research earthquakes environmental monitoring fault detection and diagnostics HVAC integrated design intelligent energy management Lighting Control machine learning Megan McHugh multi-agent systems Occupancy Occupant Centered Control Reinforcement Learning Review Smart Building smart city teaching Thermal Comfort
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Research

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UT Energy App – Privacy Policy

Fault detection and diagnostics of air handling units using machine learning and expert rule-sets

Reinforcement Learning in the Built Environment

Reinforcement learning for urban energy systems & demand response

Multi-Agent Reinforcement Learning for demand response & building coordination

IEA-EBC Annex 79: Occupant Centric Design and Operation of Buildings

People

  • Prof. Zoltan Nagy, PhD
  • June Young Park
  • José Ramón Vázquez-Canteli
  • Megan K. McHugh, MSE

Tags

air handling unit Annex 79 architecture artificial neural network Bluetooth city learn Community engaged research earthquakes environmental monitoring fault detection and diagnostics HVAC integrated design intelligent energy management Lighting Control machine learning Megan McHugh multi-agent systems Occupancy Occupant Centered Control Reinforcement Learning Review Smart Building smart city teaching Thermal Comfort
ITS

301 E Dean Keeton St
Austin, TX 78712
512-555-5555
nagy@utexas.edu

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