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1 Ergebnisse
1
Demand response strategy for HVAC based on reinforcement le..:
, In:
2022 Tsinghua-IET Electrical Engineering Academic Forum
,
He, Y.
;
Zhong, H.
;
Zhang, G.
. - p. None , 2022
Link:
https://doi.org/10.1049/icp.2022.2096
RT T1 <cTitle>2022 Tsinghua-IET Electrical Engineering Academic Forum</cTitle>: T1 <Title>Demand response strategy for HVAC based on reinforcement learning and imitation learning</Title> UL https://suche.suub.uni-bremen.de/peid=ieee-9941112&Exemplar=1&LAN=DE A1 He, Y. A1 Zhong, H. A1 Zhang, G. A1 Ruan, G. YR 2022 K1 air conditioning K1 building management systems K1 HVAC K1 learning (artificial intelligence) K1 power engineering computing K1 pricing K1 demand response strategy K1 imitation learning K1 ventilation K1 complex thermal dynamics K1 realistic buildings K1 building energy optimization K1 reinforcement learning algorithm K1 indoor temperature setpoint K1 real-time price K1 HVAC load K1 occupants K1 RL agent SP None LK http://dx.doi.org/https://doi.org/10.1049/icp.2022.2096 DO https://doi.org/10.1049/icp.2022.2096 SF ELIB - SuUB Bremen
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