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.


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The different versions of the original document can be found in:

http://dx.doi.org/10.1109/appeec45492.2019.8994618
http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=8994618,
https://academic.microsoft.com/#/detail/3006347272
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Document information

Published on 01/01/2019

Volume 2019, 2019
DOI: 10.1109/appeec45492.2019.8994618
Licence: CC BY-NC-SA license

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