The optimal design, sizing and operation of building energy systems is a complex problem due to the variety of available generation and storage devices as well as the high-resolution input data required for considering seasonal and intraday fluctuations in the thermal and electrical loads as well as renewable supply. A common measure to reduce the problem's size and complexity is to cluster the demands into representative periods. There exist many different algorithms for the clustering, but to the best of our knowledge, no comparison has been made that illustrates which algorithms are the most appropriate for such problems. Therefore, this paper compares six aggregation methods for reducing full year input data to typical demand days for energy system synthesis. We consider seasonal and monthly classification as well as sophisticated clustering methods such as k-centers, k-means, k-medians and k-medoids for aggregating the heat and electricity demand as well as solar irradiation onto the roof of a single-family house and an apartment building. The results show that all clustering methods are able to determine energy systems that are close to the optimal system, however their demand related costs are approximated best and most reliably with k-medoids. (C) 2018 Elsevier Ltd. All rights reserved.