Low-Complexity Non-Intrusive Load Monitoring Using Unsupervised Learning and Generalized Appliance Models

Awareness of electric energy usage has both societal and economic benefits, which include reduced energy bills and stress on non-renewable energy sources. In recent years, there has been a surge in interest in the field of load monitoring, also referred to as energy disaggregation, which involves methods and techniques for monitoring electric energy usage and providing appropriate feedback on usage patterns to homeowners. The use of unsupervised learning in Non-Intrusive Load Monitoring (NILM) is a key area of study, with practical solutions having wide implications for energy monitoring. In this paper, a low-complexity unsupervised NILM algorithm is presented, which is designed toward practical implementation. The algorithm is inspired by a fuzzy clustering algorithm called Entropy Index Constraints Competitive Agglomeration (EICCA), but facilitated and improved in a practical load monitoring environment to produce a set of generalized appliance models for the detection of appliance usage within a household. Experimental evaluation conducted using energy data from the Reference Energy Data Disaggregation Dataset (REDD) indicates that the algorithm has out-performance for event detection compared with recent state of the art work for unsupervised NILM when considering common NILM metrics such as Accuracy, Precision, Recall, F-measure, and Total Energy Correctly Assigned (TECA)