Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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A Deep Belief Network for Electricity Utilisation Feature Analysis of Air Conditioners Using a Smart IoT Platform

  • Song, Wei (School of Computer Science, North China University of Technology) ;
  • Feng, Ning (School of Computer Science, North China University of Technology) ;
  • Tian, Yifei (School of Computer Science, North China University of Technology) ;
  • Fong, Simon (Dept. of Computer and Information Science, University of Macau) ;
  • Cho, Kyungeun (Dept. of Multimedia Engineering, Dongguk University)
  • Received : 2017.05.08
  • Accepted : 2017.09.08
  • Published : 2018.02.28
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Abstract

Currently, electricity consumption and feedback mechanisms are being widely researched in Internet of Things (IoT) areas to realise power consumption monitoring and management through the remote control of appliances. This paper aims to develop a smart electricity utilisation IoT platform with a deep belief network for electricity utilisation feature modelling. In the end node of electricity utilisation, a smart monitoring and control module is developed for automatically operating air conditioners with a gateway, which connects and controls the appliances through an embedded ZigBee solution. To collect electricity consumption data, a programmable smart IoT gateway is developed to connect an IoT cloud server of smart electricity utilisation via the Internet and report the operational parameters and working states. The cloud platform manages the behaviour planning functions of the energy-saving strategies based on the power consumption features analysed by a deep belief network algorithm, which enables the automatic classification of the electricity utilisation situation. Besides increasing the user's comfort and improving the user's experience, the established feature models provide reliable information and effective control suggestions for power reduction by refining the air conditioner operation habits of each house. In addition, several data visualisation technologies are utilised to present the power consumption datasets intuitively.

Keywords

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Fig. 1. The proposed smart electricity utilisation IoT platform.

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Fig. 2. The multi-layer DBN mechanism.

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Fig. 3. An illustration of the RBM.

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Fig. 4. The developed smart meter for air conditioner control. (a) Electricity measurement module and(b) ZigBee communication module.

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Fig. 5. The applied router and the smart gateway for connecting the air conditioners to the Internet.

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Fig. 6. The visualisation results of the power consumption. (a) Visualisation on a mobile device and(b) visualisation on a PC.

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Fig. 7. The average electricity consumption distributions of the training datasets. (a) Hotel rooms, (b)family rooms, and (c) office rooms.

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Fig. 8. The DBN testing results of the classification accuracies of electricity utilisation types.

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Fig. 9. The prediction results using the ELM.

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