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Evaluation of reinforcement learning control for thermal energy storage systems.

Author(s) : HENZE G. P., SCHOENMANN J.

Type of article: Article

Summary

This article describes a simulation-based investigation of machine-learning control for the supervisory control of building energy systems. Model-free reinforcement learning control is investigated for the operation of electrically driven cool thermal energy storage systems in commercial buildings. The reinforcement learning controller learns to charge and discharge a thermal storage tank based on the feedback it receives from past control actions. The learning agent interacts with its environment by commanding the thermal energy storage system and extracts cues about the environment solely based on the reinforcement feedback it receives, which in this study is the monetary cost of each control action. Over time and by exploring the environment, the reinforcement learning controller establishes a statistical summary of plant operation, which is continuously updated as operation continues. The controller learns to account for the time-dependent cost of electricity (both time-of-use and real-time pricing), the availability of thermal storage, part-load performance of the central chilled water plant, and weather conditions.

Details

  • Original title: Evaluation of reinforcement learning control for thermal energy storage systems.
  • Record ID : 2004-2410
  • Languages: English
  • Source: HVAC&R Research - vol. 9 - n. 3
  • Publication date: 2003/07
  • Document available for consultation in the library of the IIR headquarters only.

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