2023/10/12 by Judah Goldfeder, Goldfeder, Judah, John Sipple +1 · 1 citation
Engineering · #Artificial Intelligence (cs.AI) #Building Energy and Comfort Optimization #Computational Engineering #FOS: Computer and information sciences #FOS: Electrical engineering #Finance #Refrigeration and Air Conditioning Technologies #Signal Processing (eess.SP) #Smart Grid Energy Management #and Science (cs.CE) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2310.08569
openalex publication_date 2023/10/12 · openalex created_date 2023/10/14 · openalex updated_date 2026/07/28
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 (available online: https://github.com/google/sbsim) 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.