Privacy-Preserving Energy Management of a Shared Energy Storage System for Smart Buildings: A Federated Deep Reinforcement Learning Approach

This paper proposes a privacy-preserving energy management of a shared energy storage system (SESS) for multiple smart buildings using federated reinforcement learning (FRL). To preserve the privacy of energy scheduling of buildings connected to the SESS, we present a distributed deep reinforcement...

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Detalles Bibliográficos
Autores principales: Lee, Sangyoon, Xie, Le, Choi, Dae-Hyun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8309780/
https://www.ncbi.nlm.nih.gov/pubmed/34300637
http://dx.doi.org/10.3390/s21144898

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8309780/
https://www.ncbi.nlm.nih.gov/pubmed/34300637
http://dx.doi.org/10.3390/s21144898

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