Exploring dimensional distinctions of residential heat load profiles using an unsupervised machine learning clustering framework

Decarbonizing the heating sector is central to achieving the energy transition, as heating systems provide essential space heating and hot water in residential and industrial environments. A major challenge lies in effectively profiling large clusters of buildings to improve demand estimation and enable efficient Demand Response schemes. This paper addresses this challenge by introducing a novel unsupervised machine learning framework for clustering residential heating load profiles, focusing on natural gas space heating and hot water preparation boilers, while analyzing five different dimensions: boiler usage, heating demand, weather conditions, building characteristics, and user behavior. Three distance metrics, Euclidean Distance, Dynamic Time Warping, and Derivative Dynamic Time Warping, are applied and evaluated using established clustering indices. The proposed method is assessed considering 29 residential buildings in Greece equipped with smart meters throughout a calendar heating season (i.e. 210 days). This study demonstrates that Dynamic Time Warping is demonstrably the most suitable metric. A subsequent correlation analysis of the clustering results from each dimension reveals strong, time-dependent relationships between boiler usage, heat demand and temperature, identifying them as the most important and correlated directions. These findings shed light into heating load behavior, establishing a solid foundation for developing more targeted and effective demand response programs.