تقرير
Controlling Commercial Cooling Systems Using Reinforcement Learning
| العنوان: | Controlling Commercial Cooling Systems Using Reinforcement Learning |
|---|---|
| المؤلفون: | Luo, Jerry, Paduraru, Cosmin, Voicu, Octavian, Chervonyi, Yuri, Munns, Scott, Li, Jerry, Qian, Crystal, Dutta, Praneet, Davis, Jared Quincy, Wu, Ningjia, Yang, Xingwei, Chang, Chu-Ming, Li, Ted, Rose, Rob, Fan, Mingyan, Nakhost, Hootan, Liu, Tinglin, Kirkman, Brian, Altamura, Frank, Cline, Lee, Tonker, Patrick, Gouker, Joel, Uden, Dave, Bryan, Warren Buddy, Law, Jason, Fatiha, Deeni, Satra, Neil, Rothenberg, Juliet, Waraich, Mandeep, Carlin, Molly, Tallapaka, Satish, Witherspoon, Sims, Parish, David, Dolan, Peter, Zhao, Chenyu, Mankowitz, Daniel J. |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Electrical Engineering and Systems Science - Systems and Control |
| الوصف: | This paper is a technical overview of DeepMind and Google's recent work on reinforcement learning for controlling commercial cooling systems. Building on expertise that began with cooling Google's data centers more efficiently, we recently conducted live experiments on two real-world facilities in partnership with Trane Technologies, 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. Comment: 27 pages, 11 figures |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2211.07357 |
| رقم الأكسشن: | edsarx.2211.07357 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |