Xiang, YueHong, JuhuaYang, ZhiyuWang, YangHuang, YuanZhang, XinChai, YanxinYao, Haotian2020-01-172020-01-172020-01-10Xiang Y, Hong J, Yang Z, et al., (2020) Slope-based shape cluster method for smart metering load profiles. IEEE Transactions on Smart Grid, Volume 11, Issue 2, March 2020, pp.1809-18111949-3053https:doi.org/10.1109/TSG.2020.2965801http://dspace.lib.cranfield.ac.uk/handle/1826/14940Cluster analysis is used to study the group of load profiles from smart meters to improve the operability in distribution network. The traditional K-means clustering analysis method employs Euclidean distance as similarity measurement, which is insufficient in reflecting the shape similarities of load profiles. In this work, we propose a novel shape cluster method based on the segmented slope of load profiles. Compared with traditional K-means and two improved algorithms, the proposed method can improve the clustering accuracy and efficiency by capturing the shape features of smart metering load profiles.enAttribution-NonCommercial 4.0 Internationalhttp://creativecommons.org/licenses/by-nc/4.0/Cluster analysisload profileK-meanssimilarityArticle