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Building Energy and HVAC&R Research Group

Buildings consumed 40% of the energy and represented 40% of the carbon emissions in the United States. This is more than any other sector of the U.S. economy, including transportation and industry. Enhancing building efficiency represents one of the easiest, most immediate, and most cost-effective ways to reduce energy consumption while keeping buildings sustainable and healthy. Dr. Zheng O’Neill and her research group are doing something about that by exploring fundamental challenges and emerging technologies for smart and healthy buildings.

More information about the research group can be found at here.

For New Members

This group is a highly collabrative environment, which requires efficient cooperation among memebers especially those who are working on the same research project. Here we briefly introduce our workflow for collobration and some guidelines for efficient project management and software development.

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  1. FMU-DRL-DOCKER FMU-DRL-DOCKER Public

    This is a set of environments that support FMU based deep reinforcement learning (DRL) environment.

    Python 9 2

  2. modelica-ice-tank modelica-ice-tank Public

    An ice tank model implemented in Modelica.

    Modelica 2

  3. Energyplus-EnhancedPCM Energyplus-EnhancedPCM Public

    This is a repository based on NREL's Energyplus with enhanced PCM hysteresis model

    C++ 1 1

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