Full metadata record
DC FieldValueLanguage
dc.contributor.authorChen, Yi-Chengen_US
dc.contributor.authorKo, Yu-Lunen_US
dc.contributor.authorPeng, Wen-Chihen_US
dc.date.accessioned2014-12-08T15:29:04Z-
dc.date.available2014-12-08T15:29:04Z-
dc.date.issued2012en_US
dc.identifier.isbn978-0-7695-4919-4en_US
dc.identifier.urihttp://hdl.handle.net/11536/20966-
dc.identifier.urihttp://dx.doi.org/10.1109/TAAI.2012.54en_US
dc.description.abstractIn 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.isoen_USen_US
dc.subjectabnormal detectionen_US
dc.subjectenergy savingen_US
dc.subjectusage patternen_US
dc.subjectsmart homeen_US
dc.titleAn Intelligent System for Mining Usage Patterns from Appliance Data in Smart Home Environmenten_US
dc.typeProceedings Paperen_US
dc.identifier.doi10.1109/TAAI.2012.54en_US
dc.identifier.journal2012 CONFERENCE ON TECHNOLOGIES AND APPLICATIONS OF ARTIFICIAL INTELLIGENCE (TAAI)en_US
dc.citation.spage319en_US
dc.citation.epage322en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000313560200052-
Appears in Collections:Conferences Paper


Files in This Item:

  1. 000313560200052.pdf

If it is a zip file, please download the file and unzip it, then open index.html in a browser to view the full text content.