Policy Selection for Transfer Learning in the Building Control Domain

• Author / Creator

  Krishna Guruvayur Sasikumar, Aakash

• The application of reinforcement learning (RL) to the optimal control of building systems has gained traction in recent years as it can reduce building energy consumption and improve human comfort, without requiring the knowledge of the building model. However, existing RL solutions for building control face challenges, such as slow convergence and suboptimal (or unsafe) actions during the training phase that may lead to high energy use or excessive discomfort. Additionally, the transferability of RL policies to different buildings remains a hurdle.

  Offline policy selection is a new domain in RL that aims to efficiently select the best policies from a library of policies for a downstream task. Previous works have shown that diversity-induced RL helps generate policies that generalize to unseen environments, often surpassing baselines, even without retraining. This thesis explores various techniques to select a policy from a library of diverse policies to control the heating, ventilation, and air conditioning (HVAC) system of a commercial building. The main contribution of this thesis is an offline policy selection algorithm that can effectively identify the most suitable policy for transfer to an unseen building environment. Furthermore, an investigation into the impact of the offline dataset utilized for evaluation is also conducted, providing valuable insights into the efficacy of the proposed evaluation technique.

  The outcomes of this research hold significant implications for energy conservation in the building sector. By enabling the adoption of RL-based control strategies that overcome the limitations of traditional approaches, this work could contribute to significant reductions in energy consumption and carbon emissions. The proposed framework empowers building operators to achieve energy-efficient control of building systems while minimizing occupant discomfort and facilitating the transfer of policies across different buildings.

• Subjects / Keywords

  • Artificial Intelligence
  • Machine Learning
  • Building Control
  • Policy Selection
  • Transfer Learning
  • Offline Reinforcement Learning
  • Energy Conservation
  • HVAC Control
  • Building Energy Management Systems
  • Building Energy Optimization
  • Building Control
  • Reinforcement Learning

• Graduation date

  Fall 2023

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/r3-pax5-sw68

• License

  This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.