Judah Goldfeder
Judah Goldfeder is a Student Researcher at Google, as well as a PhD. Student at the Creative Machines Lab at Columbia University. At Google, his work focuses on applying Reinforcement Learning to the HVAC systems of large commercial buildings. At Columbia, he is focused on several projects in the Deep Learning space, including reconstructing neutral network weights from only query access, Auxiliary Learning for Computer Vision, Machine Crystallography, Machine Learning for Biometrics, and Robotics.
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Modern commercial Heating, Ventilation, and Air Conditioning (HVAC) devices form a complex and interconnected thermodynamic system with the building and outside weather conditions,
and current setpoint control policies are not fully optimized for minimizing energy use and carbon emission. Given a suitable training environment, a Reinforcement Learning (RL) model is able to improve upon these policies, but training such a model, especially in a way that scales to thousands of buildings, presents many real-world challenges. We propose a novel simulation-based approach,where a customized simulator is used to train the agent for each building. Our open-source simulator is lightweight and calibrated via telemetry from the building to reach a higher level of fidelity. On a two-story, 68,000 square foot building, with 127 devices, we were able to calibrate our simulator to have just over half a degree of drift from the real world over a six-hour interval. This approach is an important step toward having a real-world RL control system that can be scaled to many buildings, allowing for greater efficiency and resulting in reduced energy consumption and carbon emissions.
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