Trittenbach, HolgerBach, JakobBöhm, Klemens2021-06-212021-06-212019https://dl.gi.de/handle/20.500.12116/36648Energy data often is available at high temporal resolution, which challenges the scalability of data-analysis methods. A common way to cope with this is to aggregate data to, say, 15-minute-interval summaries. But it often is not known how much information is lost with this, i. e., how good analysis results on aggregated data actually are. In this article, we study the effects of aggregating energy data on clustering. We propose an experimental design to compare a wide range of clustering methods found in literature. We then introduce different ways to compare clustering results obtained with different aggregation schemes. Our evaluation shows that aggregation affects the clustering quality significantly. Finally, we propose guidelines to select an aggregation scheme.enData miningclusteringenergy dataaggregationbenchmarkText/Journal Article10.1515/itit-2019-00142196-7032