Online self-learning for smart HVAC control

Tzu Yin Chao*, Manh Hung Nguyen, Ching Chun Huang, Chien Cheng Liang, Chen Wu Chung

*此作品的通信作者

研究成果: Conference contribution同行評審

3 引文 斯高帕斯(Scopus)

摘要

In this paper, we introduce an online-learning method to model the property of an office building. Unlike conventional control methods where the building property is modeled via a simulator or through offline learning, our building model is adaptively updated according to the dynamic response of a real environment. Upon the building model for environment prediction, the proposed action agent can control the heating, ventilation, and air conditioning (HVAC) system in a smarter way by scheduling the temperature reference point. To online learn the model and improve the agent, two practical and seldom discussed issues are addressed. The first challenge is data bias where the collected initial training dataset can only partially reveal the statistical mapping between the control input and the environment response. Hence, the trained model may lack generalization. To overcome the data bias issue, a data augmentation method is proposed to embed physical logic in order to train a proper initial model. Next, an online learning process is introduced to update the model generality during the system operation phase. The second practical issue is the constraints on agent exploration for discovering unknown data samples. During the business hours, to comfort employees, a control agent is not allowed to explore the possible controlling space randomly. To balance data collection and control stability, we introduce a hybrid control strategy that considers both the human control rule and the agent action. A confidence score of the agent model is also automatically estimated to determine a suitable control strategy finally. Our experiments have realized in an office building. The results outperform conventional methods and show its superior in terms of control stability.
原文English
主出版物標題2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019
發行者Institute of Electrical and Electronics Engineers Inc.
頁面4324-4330
頁數7
ISBN(電子)9781728145693
DOIs
出版狀態Published - 10月 2019
事件2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019 - Bari, Italy
持續時間: 6 10月 20199 10月 2019

出版系列

名字Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
2019-October
ISSN(列印)1062-922X

Conference

Conference2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019
國家/地區Italy
城市Bari
期間6/10/199/10/19

Keywords

  • Buildings , HVAC , Adaptation models , Training , Temperature control , Data models , Predictive models

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