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Being able to track appliances energy usage without the need of sensors canhelp occupants reduce their energy consumption to help save the environment allwhile saving money. Non-intrusive load monitoring (NILM) tries to do just that.One of the hardest problems NILM faces is the ability to run unsupervised --discovering appliances without prior knowledge -- and to run independent of thedifferences in appliance mixes and operational characteristics found in variouscountries and regions. We propose a solution that can do this with the use ofan advanced filter pipeline to preprocess the data, a Gaussian appliance modelwith a probabilistic knapsack algorithm to disaggregate the aggregate smartmeter signal, and partition maps to label which appliances were found and howmuch energy they use no matter the country/region. Experimental results showthat relatively complex appliance signals can be tracked accounting for 93.7%of the total aggregate energy consumed.

Universal Non-Intrusive Load Monitoring (UNILM) Using Filter Pipelines, Probabilistic Knapsack, and Labelled Partition Maps