DocumentCode :
2728108
Title :
An Intelligent System for Mining Usage Patterns from Appliance Data in Smart Home Environment
Author :
Yi-Cheng Chen ; Yu-Lun Ko ; Wen-Chih Peng
Author_Institution :
Dept. of Comput. Sci., Nat. Chiao Tung Univ., Hsinchu, Taiwan
fYear :
2012
fDate :
16-18 Nov. 2012
Firstpage :
319
Lastpage :
322
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.
Keywords :
air pollution control; behavioural sciences; building management systems; data mining; domestic appliances; energy conservation; home automation; power consumption; appliance data; appliance recognition; appliance usage; electricity consumption; electricity cost; electricity saving; energy saving; greenhouse gas reduction; intelligent system; power disaggregation; smart home environment; usage pattern mining; usage pattern utilization; user behavior pattern extraction; Clustering algorithms; Data mining; Electricity; Energy conservation; Feature extraction; Home appliances; Smart homes; abnormal detection; energy saving; smart home; usage pattern;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Technologies and Applications of Artificial Intelligence (TAAI), 2012 Conference on
Conference_Location :
Tainan
Print_ISBN :
978-1-4673-4976-5
Type :
conf
DOI :
10.1109/TAAI.2012.54
Filename :
6395048
Link To Document :
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