Federated Reinforcement Learning for Energy Management of Multiple Smart Homes with Distributed Energy Resources
- Authors
- Lee, S.; Choi, D.-H.
- Issue Date
- Jan-2022
- Publisher
- IEEE Computer Society
- Keywords
- Deep reinforcement learning (DRL); distributed energy resource; federated reinforcement learning (FRL); home appliance; home energy management system; smart home
- Citation
- IEEE Transactions on Industrial Informatics, v.18, no.1, pp 488 - 497
- Pages
- 10
- Journal Title
- IEEE Transactions on Industrial Informatics
- Volume
- 18
- Number
- 1
- Start Page
- 488
- End Page
- 497
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/54478
- DOI
- 10.1109/TII.2020.3035451
- ISSN
- 1551-3203
1941-0050
- Abstract
- This article proposesa novel federated reinforcement learning (FRL) approach for the energy management of multiple smart homes with home appliances, a solar photovoltaic system, and an energy storage system. The novelty of the proposed FRL approach lies in the development of a distributed deep reinforcement learning (DRL) model that consists of local home energy management systems (LHEMSs) and a global server (GS). Using energy consumption data, DRL agents for LHEMSs construct and upload their local models to the GS. Then, the GS aggregates the local models to update a global model for LHEMSs and broadcasts it to the DRL agents. Finally, the DRL agents replace the previous local models with the global model and iteratively reconstruct their local models. Simulation results obtained under heterogeneous home environments indicate the advantage of the proposed approach in terms of convergence speed, appliance energy consumption, and number of agents.
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Collections - College of ICT Engineering > School of Electrical and Electronics Engineering > 1. Journal Articles
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