Latest revision as of 15:34, 3 February 2021

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/pdf/1907.06299

• http://xplorestaging.ieee.org/ielx7/8964549/8994324/08994618.pdf?arnumber=8994618,

http://dx.doi.org/10.1109/appeec45492.2019.8994618

• http://xplorestaging.ieee.org/ielx7/8964549/8994324/08994618.pdf?arnumber=8994618,

http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=8994618,

https://academic.microsoft.com/#/detail/3006347272