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    Researchers in the Building & Industrial Energy Systems Division (BIES) at Lawrence Berkeley National Laboratory work closely with industry, government and key decision makers to inform and develop building and industrial energy systems that increase energy efficiency, save money and improve health and safety for building occupants.

    The BIES Division engages in innovative and creative research to advance energy efficiency in the built environment, one of the world's most critical energy and environmental challenges because buildings are the world's largest energy-users.

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  • Research +

    We are at the forefront of cutting-edge research that redefines building technology and explores all areas of urban systems.

    We have been leaders for decades in developing energy-efficient windows, improving indoor air quality, coming up with new ideas to fix the nation's electricity grid, and so much more.

    Visit our research areas at the right to find out more.

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    • Windows & Daylighting
    • FLEXLAB® & Systems Integration
    • Electronics, Lighting & Networks
    • Modeling & Simulation
    • Indoor Air Quality
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    Colum 2 +
    • Decision Science
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  • Tools & Guides +

    Explore our tools, guidebooks and software and download for free.

    We offer a variety of technologies designed to simulate and model real-world circumstances to assist in energy-saving programs and help building owners build better buildings. These tools can help calculate performance of building systems like windows and shades, help consumers and builders pick the best windows for a variety of applications and much more.

    • Whole Building
    • Occupant Behavior
    • Lighting
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Publications

Publications By Research Area

  • Cool Roofs & Walls
  • Decision Science
  • EMIS
  • Electronics, Lighting & Networks
  • Energy & Financing
  • Energy Analytics
  • FLEXLAB® and Systems Integration
  • High Tech & Industrial
  • Indoor Air Quality
  • Modeling & Simulation
  • The Grid & Demand Response
  • Windows & Daylighting
X Author: Mariam Kiran

2021

Touzani, Samir, Anand Prakash, Zhe Wang, Shreya Agarwal, Marco Pritoni, Mariam Kiran, Richard E Brown, and Jessica Granderson."Controlling distributed energy resources via deep reinforcement learning for load flexibility and energy efficiency."Applied Energy (2021). DOI

2020

Prakash, Anand, Samir Touzani, Mariam Kiran, Shreya Agarwal, Marco Pritoni, and Jessica Granderson."Deep Reinforcement Learning in Buildings: Implicit Assumptions and their Impact."RLEM'20: Proceedings of the 1st International Workshop on Reinforcement Learning for Energy Management in Buildings & Cities (2020). DOI

U.S Department of Energy   UC Berkeley

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