PMID- 34300637
OWN - NLM
STAT- MEDLINE
DCOM- 20210727
LR  - 20210729
IS  - 1424-8220 (Electronic)
IS  - 1424-8220 (Linking)
VI  - 21
IP  - 14
DP  - 2021 Jul 19
TI  - Privacy-Preserving Energy Management of a Shared Energy Storage System for Smart 
      Buildings: A Federated Deep Reinforcement Learning Approach.
LID - 10.3390/s21144898 [doi]
LID - 4898
AB  - 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 learning (DRL) 
      framework using the FRL method, which consists of a global server (GS) and local 
      building energy management systems (LBEMSs). In the framework, the LBEMS DRL 
      agents share only a randomly selected part of their trained neural network for 
      energy consumption models with the GS without consumer's energy consumption data. 
      Using the shared models, the GS executes two processes: (i) construction and 
      broadcast of a global model of energy consumption to the LBEMS agents for 
      retraining their local models and (ii) training of the SESS DRL agent's energy 
      charging and discharging from and to the utility and buildings. Simulation 
      studies are conducted using one SESS and three smart buildings with solar 
      photovoltaic systems. The results demonstrate that the proposed approach can 
      schedule the charging and discharging of the SESS and an optimal energy 
      consumption of heating, ventilation, and air conditioning systems in smart 
      buildings under heterogeneous building environments while preserving the privacy 
      of buildings' energy consumption.
FAU - Lee, Sangyoon
AU  - Lee S
AUID- ORCID: 0000-0002-0736-2325
AD  - School of Electrical and Electronics Engineering, Chung-Ang University, 84 
      Heukseok-ro, Dongjak-gu, Seoul 156-756, Korea.
FAU - Xie, Le
AU  - Xie L
AUID- ORCID: 0000-0002-9810-948X
AD  - Department of Electrical and Computer Engineering, Texas A&M University, College 
      Station, TX 77843, USA.
FAU - Choi, Dae-Hyun
AU  - Choi DH
AUID- ORCID: 0000-0002-9248-9522
AD  - School of Electrical and Electronics Engineering, Chung-Ang University, 84 
      Heukseok-ro, Dongjak-gu, Seoul 156-756, Korea.
LA  - eng
GR  - 2020R1F1A1049314/National Research Foundation of Korea/
GR  - 2020/Chung-Ang University Graduate Research Scholarship/
PT  - Journal Article
DEP - 20210719
PL  - Switzerland
TA  - Sensors (Basel)
JT  - Sensors (Basel, Switzerland)
JID - 101204366
SB  - IM
MH  - Computer Simulation
MH  - *Heating
MH  - Neural Networks, Computer
MH  - *Privacy
MH  - Ventilation
PMC - PMC8309780
OTO - NOTNLM
OT  - building energy management system
OT  - deep reinforcement learning
OT  - federated reinforcement learning
OT  - shared energy storage system
OT  - smart buildings
COIS- The authors declare no conflict of interest.
EDAT- 2021/07/25 06:00
MHDA- 2021/07/28 06:00
PMCR- 2021/07/19
CRDT- 2021/07/24 01:08
PHST- 2021/06/15 00:00 [received]
PHST- 2021/07/12 00:00 [revised]
PHST- 2021/07/15 00:00 [accepted]
PHST- 2021/07/24 01:08 [entrez]
PHST- 2021/07/25 06:00 [pubmed]
PHST- 2021/07/28 06:00 [medline]
PHST- 2021/07/19 00:00 [pmc-release]
AID - s21144898 [pii]
AID - sensors-21-04898 [pii]
AID - 10.3390/s21144898 [doi]
PST - epublish
SO  - Sensors (Basel). 2021 Jul 19;21(14):4898. doi: 10.3390/s21144898.