- By:
- Kurte, Kuldeep R; Amasyali, Kadir ; Munk, Jeffrey; Zandi, Helia
- Page Number:
- 479-483
- Book Title:
- BuildSys '22: Proceedings of the 9th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation
- Publication Date:
- November 9, 2023
- Publisher Location:
- Association for Computing Machinery, New York, New York, United States of America
- Conference Name:
- Third ACM SIGEnergy Workshop on Reinforcement Learning for Energy Management in Buildings & Cities (RLEM)
- Conference Location:
- Boston,, Massachusetts, United States of America
- Conference Sponsor:
- Association for Computing Machinery (ACM)
- View DOI Listing:
- https://doi.org/10.1145/3563357.3566168
Abstract
Deep Reinforcement Learning (DRL) has started showing success in real-world applications such as building energy optimization. Much of the research in this space utilized simulated environments to train RL-agent in an offline mode. Very few research have used DRL-based control in real-world systems due to two main reasons: 1) sample efficiency challenge---DRL approaches need to perform a lot of interactions with the environment to collect sufficient experiences to learn from, which is difficult in real systems, and 2) comfort or safety related constraints---user's comfort must never or at least rarely be violated. In this work, we propose a novel deep Reinforcement Learning framework with online Data Augmentation (RLDA) to address the sample efficiency challenge of real-world RL. We used a time series Generative Adversarial Network (TimeGAN) architecture as a data generator. We further evaluated the proposed RLDA framework using a case study of an intelligent HVAC control. With a ≈28% improvement in the sample efficiency, RLDA framework lays the way towards increased adoption of DRL-based intelligent control in real-world building energy management systems.
