Raising user awareness through unsupervised clustering of energy consumption habits

Climate change mitigation requires the urgent reduction of Greenhouse Gas (GHG) emissions, with the building sector as a significant contributor. This study develops a system to identify appliance profiles from smart meter data, enhancing energy consumption awareness and management. These profiles provide valuable insights into users’ consumption patterns and habits, enabling more accurate load consumption prediction and effective appliance scheduling strategies. The proposed approach employs feature extraction techniques to characterise energy consumption profiles, followed by k-means clustering to identify distinct appliance profiles. Eleven representative features are identified, offering comprehensive insights into occupants’ energy usage habits. The evaluation with real-case data shows accurate consumption cycle approximations for each profile, with errors consistently below 10%. Performance assessment using classification metrics indicates well-characterised and representative profiles, outperforming state-of-the-art methods with average values exceeding 0.88 for all considered metrics. This system helps raise occupants’ awareness of appliance energy usage and facilitates optimised scheduling through an Energy Management System (EMS). By promoting more efficient energy consumption, the proposed approach contributes to overall energy reduction and, consequently, lower GHG emissions in the building sector. • Development of a system to monitor consumption habits for different appliances. • Design a methodology to identify key features for diverse appliance profiles. • Identification of 11 robust features representing consumption profiles effectively. • Integration of k-means clustering to group appliances by consumption and behaviour. • Validation of the proposed system using a real-case dataset.