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

## TL;DR

This paper introduces a universal, unsupervised NILM method that uses advanced filtering, probabilistic modeling, and labeling techniques to accurately disaggregate energy data across different regions.

## Contribution

It presents a novel combination of filter pipelines, probabilistic knapsack, and partition maps for region-independent, unsupervised appliance energy disaggregation.

## Key findings

- Achieves 93.7% accuracy in energy tracking
- Works across various countries and appliance types
- Handles complex appliance signals effectively

## 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.