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District heating has been found to be a key component of future and reliable smart energy grids comprising 100% of renewable energy sources for countries with dominant heating season. However, these systems face challenges that require a deeper understanding of the coupling between the distribution networks and the connected buildings, to enable demand-side management and balance the intermittence of renewables. In recent years, many smart energy meters have been installed on the heating systems of Danish dwellings connected to district heating, and the first yearly measurement data sets of large building clusters are now available. This article presents the methodology for the pre-processing and cluster analysis (K-means clustering) of a one-year-long smart energy meter measurement data from 1665 Danish dwellings connected to district heating. The aim is to identify typical household daily profiles of heat energy use, return temperature, and temperature difference between the supply and the return fluid. The study is performed with the free software environment “R”, which enables the rapid extraction of information to be shared with professionals of the building and energy sectors. After presenting the preliminary results of the clustering analysis, the article closes with the future work to be conducted on this study case.

Denmark aims to be independent of fossil fuels in the country's energy production by 2050. One of the initiatives to reach the decarbonization goal is the digitalization of the energy sector, specifically the roll-out of smart meters in the buildings connected to the district heating network. As a result, it allowed having better insights into the dynamics of the heating loads of the demand side. However, these meters often record the total energy usage without distinguishing between the energy use for space heating (SH) and domestic hot water (DHW). Additionally, the metered data have hourly resolution, which prevents the detection of short DHW usage. To tackle this limitation and gain valuable information on the buildings' heating patterns, this paper presents a new methodology to estimate the energy use for SH and DHW from total measurements in residential buildings. The method employs a combined smoothing algorithm with a support vector regression to estimate the energy use for SH from outdoor conditions. The energy use for DHW is calculated a posteriori by the difference between the total measurements and the estimated SH energy. The advantage of this technique is the ability to be applied in hourly-resolution data while only requiring local weather measurements, making it a tool to be utilized in different scenarios. This method is validated with three different sets of building cases. The first dataset consists of 28 apartments in Denmark, where the measurement resolution is coarse at 1 kWh. This case focuses on determining the method's accuracy in single-family dwellings when their measurements are truncated. The second dataset set of apartments is located in a 5-story building in Switzerland. In this case, the objective is to test the method's accuracy when analyzing aggregated measurements of all dwellings in the building. The third dataset includes hourly readings from customers connected to a DH network in Italy. In this case, the objective is to test the method's application to other building typologies (i.e., historical buildings). Because these three cases are located in different countries, this validation study also tests the method's robustness to the variability of users, locations, and heating system types.