Last week I found out that a conference paper I helped co-author with my PhD student Alejandro Rodriguez-Silva was accepted for presentation and publication. Titled Universal Non-Intrusive Load Monitoring (UNILM) Using Filter Pipelines, Probabilistic Knapsack, and Labelled Partition Maps, it will be presented at the 11th IEEE PES Asia-Pacific Power and Energy Engineering Conference 2019 (APPEEC) held December 1-4, 2019 in Macau. Here is the paper abstract:
Being able to track appliances energy usage without the need of sensors can help occupants reduce their energy consumption to help save the environment all while 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 the differences in appliance mixes and operational characteristics found in various countries and regions. We propose a solution that can do this with the use of an advanced filter pipeline to preprocess the data, a Gaussian appliance model with a probabilistic knapsack algorithm to disaggregate the aggregate smart meter signal, and partition maps to label which appliances were found and how much energy they use no matter the country/region. Experimental results show that relatively complex appliance signals can be tracked accounting for 93.7% of the total aggregate energy consumed.
Keywords: unsupervised learning, disaggregation, non-intrusive load monitoring, NILM, knapsack, labelled partition maps, Gaussian models, smart meter, smart grid
