Exploring smart heat meter data: A co-clustering driven approach to analyse the energy use of single-family houses.

The ongoing digitalisation of the district heating sector, particularly the installation of smart heat meters (SHMs), is generating data with unprecedented extent and temporal resolution. This data offers potential insights into heat energy use at a large scale, supporting policymakers and district heating utility companies in transforming the building sector. Clustering is crucial for representing this wealth of data in human-understandable groups, necessitating consideration of seasonality. Advancing current research in clustering SHM data, this work applies an established co-clustering approach, FunLBM, considering seasonal variation without fixed season definitions. Furthermore, to enhance the understanding of differentiating factors between clusters, the possibility to understand cluster memberships based on 26 building characteristics was analysed using classification and variable selection methods. Applying FunLBM on a large-scale hourly dataset from single-family houses revealed six well-separated energy use clusters each distributed over six-temporal clusters, which are correlated with the exterior temperature, yet not following fixed seasons. Variable selection and classification showed that building characteristics describing the building with a high level of detail are insufficient to explain cluster membership (Matthew's correlation coefficient (MCC) ≈ 0.3). By merging the energy use clusters based on profile and magnitude similarities, classification performance significantly improved (MCC ≈ 0.5). In both cases, simple and readily available building characteristics yield similar insights to detailed ones, emphasising their cost-effectiveness and practicality. [Display omitted] • Co-clustering of smart heat meter data to establish season-independent clusters. • Analysis of energy use clusters based on 26 building characteristics. • Classification and variable selection to identify the minimum information needed. • Statistical data leads to the same insight as detailed building data. • Prediction of energy use clusters with building characteristics has a low accuracy. [ABSTRACT FROM AUTHOR]