Deploying Reinforcement Learning based Economizer Optimization at Scale (Papers Track)

Ivan Cui (Amazon); Wei Yih Yap (Amazon); Charles Prosper (Independant); Bharathan Balaji (Amazon); Jake Chen (Amazon)

Paper PDF NeurIPS 2023 Poster Cite
Buildings Reinforcement Learning


Building operations account for a significant portion of global emissions, contributing approximately 28\% of global greenhouse gas emissions. With anticipated increase in cooling demand due to rising global temperatures, the optimization of rooftop units (RTUs) in buildings becomes crucial for reducing emissions. We focus on the optimization of the economizer logic within RTUs, which balances the mix of indoor and outdoor air. By effectively utilizing free outside air, economizers can significantly decrease mechanical energy usage, leading to reduced energy costs and emissions. We introduce a reinforcement learning (RL) approach that adaptively controls the economizer based on the unique characteristics of individual facilities. We have trained and deployed our solution in the real-world across a distributed building stock. We address the scaling challenges with our cloud-based RL deployment on 10K+ RTUs across 200+ sites.