Reinforcement-Learning-Based Smart Water Heater Control: An Actual Deployment

Kadir Amasyali, Kuldeep Kurte, Helia Zandi, Jeffrey Munk

Research output: Contribution to conferencePaperpeer-review

2 Scopus Citations


Utilizing smart control algorithms for electric water heaters (EWHs) is essential for fully harnessing the demand response (DR) potential of EWHs. For this reason, the use of reinforcement learning (RL) algorithms for EWHs has received increasing attention in recent years. However, existing RL approaches are either simulation-based or use pretrained RL agents. To this end, this paper presents the real-world deployment of a set of model-free RL approaches that aim to minimize the electricity cost of a EWH under a time-of-use electricity pricing policy using standard DR commands (e.g., shed, load up). The experiment results showed that the RL agents can help save electricity cost in the range of 11% to 14% compared to the baseline operation. This study demonstrated that RL-based EWH controllers can be deployed in real world without any prior training and can still save electricity cost.

Original languageAmerican English
Number of pages5
StatePublished - 2023
Event2023 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2023 - Washington, United States
Duration: 16 Jan 202319 Jan 2023


Conference2023 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2023
Country/TerritoryUnited States

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

NREL Publication Number

  • NREL/CP-5600-86283


  • deep learning
  • Demand response (DR)
  • electric water heaters
  • model-free control
  • reinforcement learning (RL)


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