Non-Intrusive Load Disaggregation by Convolutional Neural Network and Multilabel Classification

Luca Massidda,Simone Manca,Marino Marrocu

doi:10.3390/app10041454

Abstract

Non-intrusive load monitoring (NILM) is the main method used to monitor the energy footprint of a residential building and disaggregate total electrical usage into appliance-related signals. The most common disaggregation algorithms are based on the Hidden Markov Model, while solutions based on deep neural networks have recently caught the attention of researchers. In this work we address the problem through the recognition of the state of activation of the appliances using a fully convolutional deep neural network, borrowing some techniques used in the semantic segmentation of images and multilabel classification. This approach has allowed obtaining high performances not only in the recognition of the activation state of the domestic appliances but also in the estimation of their consumptions, improving the state of the art for a reference dataset.

Highlights

Non-Intrusive Load Monitoring (NILM) is the technique used to estimate the consumption of individual household appliances based on the aggregate consumption of a home
To obtain the Fa we will use a convolutional neural network, which has as input a time interval of the consumption of a house and provides an estimate of the state of activation of the equipment for each instant considered
In this paper we have presented a new methodology for Non Intrusive Load Monitoring and Disaggregation, based on the recognition of the activation states of household appliances

Summary

Introduction

Non-Intrusive Load Monitoring (NILM) is the technique used to estimate the consumption of individual household appliances based on the aggregate consumption of a home. This allows the monitoring of the consumption of household appliances without the need to install dedicated sensors for the individual appliances, avoiding electrical system complications and related costs. Several studies have shown that knowledge of the consumption of individual devices can have a positive effect on user behavior allowing savings of up to 12% on annual consumption [2]. The savings result from a more conscious behaviour of the user of the electrical service, who can identify the appliances with the highest consumption and limit their use or replace them with more efficient ones. The NILM technique can be used in association with a home management system [3], or with a price-sensitive demand side management system [4]

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