Economizer Optimization with Reinforcement Learning: An Industry Perspective

Jiaron Cui, Wei Yih Yap, Charles Prosper, Bharathan Balaji, Jake Chen

Abstract: Building operations contribute approximately 28% of global greenhouse gas emissions according to the International Energy Agency. With the 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 outside air, RTUs can significantly decrease mechanical energy usage, leading to reduced energy costs and emissions. However, the current practice of economizer optimization relies on static guidelines set by ASHRAE, which approximates the dynamics of individual facilities. We introduce a reinforcement learning (RL) approach that adaptively controls the economizer based on the unique characteristics of individual facilities. We have 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.