99-110 Mining appliance usage patterns in smart home environment

Yi Cheng Chen, Yu Lun Ko, Wen Chih Peng, Wang Chien Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

19 Scopus citations


Nowadays, due to the great advent of sensor technology, the data of all appliances in a house can be collected easily. However, with a huge amount of appliance usage log data, it is not an easy task for residents to visualize how the appliances are used. Mining algorithms is necessary to discover appliance usage patterns that capture representative usage behavior of appliances. If some of our representative patterns of appliance electricity usages are available, we may be able to adapt our usage behaviors to conserve the energy easily. In this paper, we introduce (i) two types of usage patterns which capture the representative usage behaviors of appliances in a smart home environment and (ii) the corresponding algorithms for discovering usage patterns efficiently. Finally, we apply our algorithms on a real-world dataset to show the practicability of usage pattern mining.

Original languageEnglish (US)
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 17th Pacific-Asia Conference, PAKDD 2013, Proceedings
Number of pages12
EditionPART 1
StatePublished - 2013
Event17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2013 - Gold Coast, QLD, Australia
Duration: Apr 14 2013Apr 17 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume7818 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2013
CityGold Coast, QLD

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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