Applied lightweight parallel multi-appliance recognition on smart meter
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...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
| Published: |
2013
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| Online Access: | https://hdl.handle.net/10356/101011 http://hdl.handle.net/10220/16722 |
| _version_ | 1811687301338628096 |
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| author | Ma, Yi-Wei Lai, Chin-Feng Lin, Man Wen, Yonggang Chen, Jiann-Liang |
| author2 | School of Computer Engineering |
| author_facet | School of Computer Engineering Ma, Yi-Wei Lai, Chin-Feng Lin, Man Wen, Yonggang Chen, Jiann-Liang |
| author_sort | Ma, Yi-Wei |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T05:14:08Z |
| format | Conference Paper |
| id | ntu-10356/101011 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:14:08Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/1010112020-05-28T07:17:17Z Applied lightweight parallel multi-appliance recognition on smart meter Ma, Yi-Wei Lai, Chin-Feng Lin, Man Wen, Yonggang Chen, Jiann-Liang School of Computer Engineering IEEE International Conference on Computational Science and Engineering (15th : 2012 : Nicosia, Cyprus) DRNTU::Engineering::Computer science and engineering 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. 2013-10-23T06:47:04Z 2019-12-06T20:31:59Z 2013-10-23T06:47:04Z 2019-12-06T20:31:59Z 2012 2012 Conference Paper 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. https://hdl.handle.net/10356/101011 http://hdl.handle.net/10220/16722 10.1109/ICCSE.2012.57 en © 2012 IEEE |
| spellingShingle | DRNTU::Engineering::Computer science and engineering Ma, Yi-Wei Lai, Chin-Feng Lin, Man Wen, Yonggang Chen, Jiann-Liang Applied lightweight parallel multi-appliance recognition on smart meter |
| title | Applied lightweight parallel multi-appliance recognition on smart meter |
| title_full | Applied lightweight parallel multi-appliance recognition on smart meter |
| title_fullStr | Applied lightweight parallel multi-appliance recognition on smart meter |
| title_full_unstemmed | Applied lightweight parallel multi-appliance recognition on smart meter |
| title_short | Applied lightweight parallel multi-appliance recognition on smart meter |
| title_sort | applied lightweight parallel multi appliance recognition on smart meter |
| topic | DRNTU::Engineering::Computer science and engineering |
| url | https://hdl.handle.net/10356/101011 http://hdl.handle.net/10220/16722 |
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