Deploying reinforcement learning based economizer optimization at scale

Building operations account for a significant portion of global emissions, contributing approximately 28% of global greenhouse gas emissions, according to the International Energy Agency. With the anticipated increase in cooling demand due to rising global temperatures, the optimization of rooftop units (RTUs) in buildings becomes crucial for reducing energy consumption and associated 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 when available, 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 overlook the specific conditions and 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 trained and deployed our solution in the real-world across a geographically distributed building stock. We address the scaling challenges with our cloud-based RL deployment on 10K+ RTUs across 200+ sites.