Neural Network for NILM Based on Operational State Change Classification

TL;DR

This paper introduces a neural network approach for non-intrusive load monitoring that classifies operational state changes of appliances, offering a more adaptable and less complex alternative to traditional methods.

Contribution

The study proposes a neural network model focused on classifying appliance operational state changes, reducing complexity and data preprocessing compared to existing energy disaggregation techniques.

Findings

Competitive performance on REDD dataset

Lower complexity than traditional methods

Effective across various appliances

Abstract

Energy disaggregation in a non-intrusive way estimates appliance level electricity consumption from a single meter that measures the whole house electricity demand. Recently, with the ongoing increment of energy data, there are many data-driven deep learning architectures being applied to solve the non-intrusive energy disaggregation problem. However, most proposed methods try to estimate the on-off state or the power consumption of appliance, which need not only large amount of parameters, but also hyper-parameter optimization prior to training and even preprocessing of energy data for a specified appliance. In this paper, instead of estimating on-off state or power consumption, we adapt a neural network to estimate the operational state change of appliance. Our proposed solution is more feasible across various appliances and lower complexity comparing to previous methods. The simulated experiments in the low sample rate dataset REDD show the competitive performance of the designed method, with respect to other two benchmark methods, Hidden Markov Model-based and Graph Signal processing-based approaches.