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

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.

Multi-Label Learning for Appliance Recognition in NILM Using Fryze-Current Decomposition and Convolutional Neural Network

“…Nalmpantis and Vrakas [37] present a multi-label NILM based on the Signal2Vec algorithm that maps any time series into a vector space. A deep neural network (DNN) based multi-label NILM applying active power features at low-sampling frequency is proposed in [23,24]. In [23], the authors propose an approach that builds on Temporal Convolutional Networks (TCNN).…”

“…In [23], the authors propose an approach that builds on Temporal Convolutional Networks (TCNN). At the same time, Massidda et al [24] applied Fully Convolutional Networks (FCNN) for multi-label-learning in NILM, adopting some methods used in semantic segmentation.…”

“…Several studies have demonstrated that multi-label learning represents a viable alternative to conventional NILM approaches [23][24][25][26][27]. For example, the work by [25], investigated the possibility of applying a temporal multi-label classification approach in non-event based NILM where a novel set of meta-features was proposed.…”

“…In [26] an extensive survey for the multi-label classification and the multi-label meta-classification framework for low-sampling power measurements is presented. Recent studies have explored deep neural networks for multi-label appliance recognition in NILM [23,24], yet these approaches also rely on low-frequency data.…”

Automated Non-Intrusive Load Monitoring System Using Stacked Neural Networks and Numerical Integration

“…The cited studies show that most NILM techniques are based on a learning paradigm where the training stage is intrusive, requiring the use of plug level sensors for collecting data that are used for learning appliance specific models to be used for disaggregation during the operational phase, when the plug level sensors are removed. In contrast, some studies based on multi-label classification paradigm that do not require actual consumption data from all the appliances and can act on aggregate data have emerged [24], [43]- [45].…”