TY - GEN
T1 - Reinforcement-learning-based Smart Water Heater Control
T2 - 2023 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2023
AU - Amasyali, Kadir
AU - Kurte, Kuldeep
AU - Zandi, Helia
AU - Munk, Jeffrey
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Demand response (DR)
KW - deep learning
KW - electric water heaters
KW - model-free control
KW - reinforcement learning (RL)
UR - http://www.scopus.com/inward/record.url?scp=85151531835&partnerID=8YFLogxK
U2 - 10.1109/ISGT51731.2023.10066373
DO - 10.1109/ISGT51731.2023.10066373
M3 - Conference contribution
AN - SCOPUS:85151531835
T3 - 2023 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2023
BT - 2023 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 16 January 2023 through 19 January 2023
ER -