TY - GEN
T1 - Reflecting energy use patterns and lifestyles in home using data mining techniques
AU - Kioumarsi, Niloufar
AU - Wang, Julian
N1 - Publisher Copyright:
© Copyright: PLEA 2018 Hong Kong.
PY - 2018
Y1 - 2018
N2 - Most methods to analyse and understand the residential energy use features rely on invasive measurements, such as energy monitoring systems, which eventually affects the reliability of pattern classifications. This paper, thus, adopts a non-invasive method using unsupervised data mining algorithms to analyse hourly energy consumption data in order to learn the occupant's lifestyle and energy consumption behavioral patterns. The study analyses hourly energy use of 298 households in Texas in 2015, using an online open source data set - Pecan Street Dataport. This study scientifically identified household's energy use features and associated behavioural patterns through a multi scale observation of the clusters. As the contribution, this study takes the house age and size into account as these variables may significantly affect building energy use patterns. Second, it takes dissimilarity measures into account by using TSclust R package for clustering time series. And third, introduces a method of multiscale observation of clusters in order to interpret the lifestyle patterns. Finally, the results demonstrated how data mining techniques might be utilized to help investigating energy use data from the behavioural perspective.
AB - Most methods to analyse and understand the residential energy use features rely on invasive measurements, such as energy monitoring systems, which eventually affects the reliability of pattern classifications. This paper, thus, adopts a non-invasive method using unsupervised data mining algorithms to analyse hourly energy consumption data in order to learn the occupant's lifestyle and energy consumption behavioral patterns. The study analyses hourly energy use of 298 households in Texas in 2015, using an online open source data set - Pecan Street Dataport. This study scientifically identified household's energy use features and associated behavioural patterns through a multi scale observation of the clusters. As the contribution, this study takes the house age and size into account as these variables may significantly affect building energy use patterns. Second, it takes dissimilarity measures into account by using TSclust R package for clustering time series. And third, introduces a method of multiscale observation of clusters in order to interpret the lifestyle patterns. Finally, the results demonstrated how data mining techniques might be utilized to help investigating energy use data from the behavioural perspective.
UR - http://www.scopus.com/inward/record.url?scp=85088358724&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85088358724&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85088358724
T3 - PLEA 2018 - Smart and Healthy within the Two-Degree Limit: Proceedings of the 34th International Conference on Passive and Low Energy Architecture
SP - 1071
EP - 1073
BT - PLEA 2018 - Smart and Healthy within the Two-Degree Limit
A2 - Ng, Edward
A2 - Fong, Square
A2 - Ren, Chao
PB - School of Architecture, The Chinese University of Hong Kong
T2 - 34th International Conference on Passive and Low Energy Architecture: Smart and Healthy Within the Two-Degree Limit, PLEA 2018
Y2 - 10 December 2018 through 12 December 2018
ER -