Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Chen, Yi-Cheng | en_US |
dc.contributor.author | Ko, Yu-Lun | en_US |
dc.contributor.author | Peng, Wen-Chih | en_US |
dc.date.accessioned | 2014-12-08T15:29:04Z | - |
dc.date.available | 2014-12-08T15:29:04Z | - |
dc.date.issued | 2012 | en_US |
dc.identifier.isbn | 978-0-7695-4919-4 | en_US |
dc.identifier.uri | http://hdl.handle.net/11536/20966 | - |
dc.identifier.uri | http://dx.doi.org/10.1109/TAAI.2012.54 | en_US |
dc.description.abstract | In the last decade, considerable concern has arisen over the electricity saving due to the issue of reducing greenhouse gases. Previous studies on usage pattern utilization mainly are focused on power disaggregation and appliance recognition. Little attention has been paid to utilizing pattern mining for the target of energy saving. In this paper, we develop an intelligent system which analyzes appliance usage to extract users' behavior patterns in a smart home environment. With the proposed system, users can acquire the electricity consumption of each appliance for energy saving easily. In advance, if the electricity cost is high, users can observe the abnormal usage of appliances from the proposed system. Furthermore, we also apply our system on real-world dataset to show the practicability of mining usage pattern in smart home environment. | en_US |
dc.language.iso | en_US | en_US |
dc.subject | abnormal detection | en_US |
dc.subject | energy saving | en_US |
dc.subject | usage pattern | en_US |
dc.subject | smart home | en_US |
dc.title | An Intelligent System for Mining Usage Patterns from Appliance Data in Smart Home Environment | en_US |
dc.type | Proceedings Paper | en_US |
dc.identifier.doi | 10.1109/TAAI.2012.54 | en_US |
dc.identifier.journal | 2012 CONFERENCE ON TECHNOLOGIES AND APPLICATIONS OF ARTIFICIAL INTELLIGENCE (TAAI) | en_US |
dc.citation.spage | 319 | en_US |
dc.citation.epage | 322 | en_US |
dc.contributor.department | 資訊工程學系 | zh_TW |
dc.contributor.department | Department of Computer Science | en_US |
dc.identifier.wosnumber | WOS:000313560200052 | - |
Appears in Collections: | Conferences Paper |
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