Controlling Commercial Cooling Systems Using Reinforcement Learning
Jerry Luo ⋅ Cosmin Paduraru ⋅ Octavian Voicu ⋅ Yuri Chervonyi ⋅ Scott Munns ⋅ Jerry Li ⋅ Crystal Qian ⋅ Praneet Dutta ⋅ Daniel Mankowitz ⋅ Jared Quincy Davis ⋅ Ningjia Wu ⋅ Xingwei Yang ⋅ Chu-Ming Chang ⋅ Ted Li ⋅ Rob Rose ⋅ Mingyan Fan ⋅ Hootan Nakhost ⋅ Tinglin Liu ⋅ Deeni Fatiha ⋅ Neil Satra ⋅ Juliet Rothenberg ⋅ Molly Carlin ⋅ Satish Tallapaka ⋅ Sims Witherspoon ⋅ David Parish ⋅ Peter Dolan ⋅ Chenyu Zhao
Abstract
This paper is a technical overview of our recent work on reinforcement learning for controlling commercial cooling systems. Building on previous work on cooling data centers more efficiently, we recently conducted two live experiments in partnership with a building management system provider. These live experiments had a variety of challenges in areas such as evaluation, learning from offline data, and constraint satisfaction. Our paper describes these challenges in the hope that awareness of them will benefit future applied RL work. We also describe the way we adapted our RL system to deal with these challenges, resulting in energy savings of approximately 9% and 13% respectively at the two live experiment sites.
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