Open AccessExploratory study on clustering methods to identify electricity use patterns in building sector
Author(s) -
Selin Yılmaz,
Jonathan Chambers,
Stefano Cozza,
Martin K. Patel
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1343/1/012044
Subject(s) - cluster analysis , silhouette , electricity , partition (number theory) , exploratory analysis , curse of dimensionality , computer science , cluster (spacecraft) , quality (philosophy) , data mining , mathematics , engineering , artificial intelligence , data science , philosophy , epistemology , combinatorics , electrical engineering , programming language
In this paper, we perform a cluster analysis using smart meter electricity demand data from 656 households in Switzerland, collected during one year. First, we present the silhouette analysis to determine the optimum number of clusters for a k-means clustering approach. Secondly, we try different distance functions used in the k-means clustering to partition the samples into different categories. We find that the choice of distance function has no effect on the clustering performance. Finally, we investigate the “dimensionality curse” and find that low dimensions should be preferred to increase the quality of the clustering outcome.