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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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| Publicat a: | Sensors (Basel) |
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| Autors principals: | , , |
| Format: | Artigo |
| Idioma: | Inglês |
| Publicat: |
MDPI
2021
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| Matèries: | |
| Accés en línia: | https://ncbi.nlm.nih.gov/pmc/articles/PMC8309780/ https://ncbi.nlm.nih.gov/pubmed/34300637 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3390/s21144898 |
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