A multi-step predictive deep reinforcement learning algorithm for HVAC control systems in smart buildings

The development of the building energy management systems (BEMS) enable users to intelligently control Heating, Ventilation, Air-conditioning and Cooling (HVAC) systems based on digital information. In order to reduce the power consumption cost of the HVAC system while ensuring user satisfaction, a...

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Veröffentlicht in:	Energy (Oxford) Jg. 259; S. 124857
Hauptverfasser:	Liu, Xiangfei, Ren, Mifeng, Yang, Zhile, Yan, Gaowei, Guo, Yuanjun, Cheng, Lan, Wu, Chengke
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 15.11.2022
Schlagworte:	algorithms ambient temperature consumer satisfaction Deep reinforcement learning electricity energy energy use and consumption Generalized correntropy heat HVAC system issues and policy Multi-step prediction neural networks prediction Deep reinforcement learning Multi-step prediction HVAC system Generalized correntropy
ISSN:	0360-5442
Online-Zugang:	Volltext
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