Unsupervised Multi-Sequence Appliance Identification in Non-Intrusive Load Monitoring

Non-Intrusive Load Monitoring (NILM) offers a promising approach for identifying device-level energy usage from aggregate household measurements. Most existing methods rely on supervised learning or require labeled training data, which limits generalizability in practical scenarios. This paper introduces the Unsupervised Multi-Sequence Appliance Identification (UMSAI) algorithm, a modular pipeline designed to identify complex multi-phase appliances, such as washing machines and dishwashers, using only two weeks of unlabeled measurement data from a single measurement device. UMSAI performs robust event detection, feature-based clustering, and appliance identification through graph-based similarity analysis. Evaluation on real-world datasets demonstrates high detection accuracy for appliances with distinct cyclic patterns, highlighting the method’s applicability in data-scarce, label-free environments.