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DearFSAC: A DRL-based Robust Design for Power Demand Foreca..:
, In:
GLOBECOM 2022 - 2022 IEEE Global Communications Conference
,
Huang, Chenghao
;
Chen, Weilong
;
Wang, Xiaoyi
... - p. 5279-5284 , 2022
Link:
https://doi.org/10.1109/GLOBECOM48099.2022.10001127
RT T1
GLOBECOM 2022 - 2022 IEEE Global Communications Conference
: T1
DearFSAC: A DRL-based Robust Design for Power Demand Forecasting in Federated Smart Grid
UL https://suche.suub.uni-bremen.de/peid=ieee-10001127&Exemplar=1&LAN=DE A1 Huang, Chenghao A1 Chen, Weilong A1 Wang, Xiaoyi A1 Hong, Feng A1 Yang, Shunji A1 Chen, Yuxi A1 Bu, Shengrong A1 Jiang, Changkun A1 Zhou, Yingjie A1 Zhang, Yanru YR 2022 K1 Deep learning K1 Resistance K1 Power demand K1 Companies K1 Reinforcement learning K1 Predictive models K1 Data models K1 Federated learning K1 deep reinforcement learning K1 long short-term memory K1 auto-encoder K1 power demand forecasting K1 smart grid SP 5279 OP 5284 LK http://dx.doi.org/https://doi.org/10.1109/GLOBECOM48099.2022.10001127 DO https://doi.org/10.1109/GLOBECOM48099.2022.10001127 SF ELIB - SuUB Bremen
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