Energy optimization associated with thermal comfort and indoor air control via a deep reinforcement learning algorithm

The aim of this work is to propose an artificial intelligence algorithm that maintains thermal comfort and air quality within optimal levels while consuming the least amount of energy from air-conditioning units and ventilation fans. The proposed algorithm is first trained with 10 years of simulated...

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Veröffentlicht in:	Building and environment Jg. 155; S. 105 - 117
Hauptverfasser:	Valladares, William, Galindo, Marco, Gutiérrez, Jorge, Wu, Wu-Chieh, Liao, Kuo-Kai, Liao, Jen-Chung, Lu, Kuang-Chin, Wang, Chi-Chuan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Oxford Elsevier Ltd 15.05.2019 Elsevier BV
Schlagworte:	Air conditioners Air conditioning Air conditioning equipment Air quality Algorithms Artificial intelligence Carbon dioxide Computer simulation Control methods Control systems Deep reinforcement learning Energy consumption Indoor air pollution Indoor air quality Indoor environments Machine learning Optimization Subtropical zones Thermal comfort Ventilation Ventilation fans Air conditioning Deep reinforcement learning Ventilation Thermal comfort Optimization Indoor air quality
ISSN:	0360-1323, 1873-684X
Online-Zugang:	Volltext
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