Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/101011
Title: Applied lightweight parallel multi-appliance recognition on smart meter
Authors: Ma, Yi-Wei
Lai, Chin-Feng
Lin, Man
Wen, Yonggang
Chen, Jiann-Liang
Keywords: DRNTU::Engineering::Computer science and engineering
Issue Date: 2012
Source: Lai, C.-F., Lin, M., Wen, Y., Ma, Y.-W., & Chen, J.-L. (2012). Applied Lightweight Parallel Multi-Appliance Recognition on Smart Meter. 2012 IEEE 15th International Conference on Computational Science and Engineering (CSE), 361-366.
Abstract: With the crisis of uprising energy, smart meter development has gained a lot of attention. Along with the popularization of Internet of Things (IoT) and home energy management system, users can identify the electronic device being used with the help of electronic appliance recognition technology in order to improve power usage habits. However, there is a difficulty in multiple electronic appliance recognition which poses as a problem since multiple appliances switching on and off is common in everyday life. Hence this study will discuss simultaneous multi-electronic appliance recognition. Another issue in smart meter development is the difficulty in installation. This study solves this problem by proposing a non-invasive smart meter device that also studies the user power usage habits in cases where users are unfamiliar with electronic devices. The system also solves the large data volume processing problem of the current appliance recognition system using a database mechanism, electronic appliance recognition classification, as well as waveform recognition. Other electronic appliance recognition may be power consuming, while this system uses low power low order embedded system chip with high expandability and convenience. Different from past studies, this research considers simultaneous multi-electronic appliance recognition and power usage habits of normal users. The experimental results showed that the total system recognition rate can reach 86.14% with the general daily power usage habits, and the total recognition rate of a single electronic appliance can reach 96.14%, thus proving the feasibility of the proposed system.
URI: https://hdl.handle.net/10356/101011
http://hdl.handle.net/10220/16722
DOI: 10.1109/ICCSE.2012.57
Rights: © 2012 IEEE
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Conference Papers

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