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


== 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.


== Original document ==

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

* [http://arxiv.org/abs/1907.06299 http://arxiv.org/abs/1907.06299]

* [http://arxiv.org/pdf/1907.06299 http://arxiv.org/pdf/1907.06299]

* [http://xplorestaging.ieee.org/ielx7/8964549/8994324/08994618.pdf?arnumber=8994618 http://xplorestaging.ieee.org/ielx7/8964549/8994324/08994618.pdf?arnumber=8994618],
: [http://dx.doi.org/10.1109/appeec45492.2019.8994618 http://dx.doi.org/10.1109/appeec45492.2019.8994618]

* [http://xplorestaging.ieee.org/ielx7/8964549/8994324/08994618.pdf?arnumber=8994618 http://xplorestaging.ieee.org/ielx7/8964549/8994324/08994618.pdf?arnumber=8994618],
: [http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=8994618 http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=8994618],
: [https://academic.microsoft.com/#/detail/3006347272 https://academic.microsoft.com/#/detail/3006347272]