You can find this paper on the TU Delft repository at Gnu-RL

Paper: A Precocial Reinforcement Learning Solution for Building HVAC Control – Chen et al. 1/1

Technical Aspects link copied

Software & Plug-ins

EnergyPlus simulation engine to train and evaluate the agent (OpenAI Gym wrapper for EnergyPlus), PyTorch for RL implementation, PI DataLink to access real time observations from BAS, Dark Sky API for predictive information for weather

Summary

The paper proposes a method (Gnu-RL) to allow for practical implementation of RL strategies for HVAC control. The method adopts a Differentiable Model Predictive Control (MPC) policy and leverages historical data from existing HVAC systems to pre-train the agent. When interacting with environment, the agent utilizes a policy gradient algorithm to keep enhancing its policy end-to-end.

The proposed method was implemented both to a virtual and a physical example. Gnu-RL showed improved results in both cases compared to published RL results for the same environment and data from existing controllers respectively. Lastly, probabilistic occupancy was suggested as direction for further development, since occupancy information is not usually available.

Project Information

Author(s): Bingqing Chen, Zicheng Cai, Mario Bergés

Year: 2019

Project type: Paper

Keywords: Deep Reinforcement Learning, Policy iteration

Topic tags: HVAC control, Model Predictive Control (MPC) Policy, Policy Gradient algorithm