Non-intrusive load disaggregation based on deep dilated residual network

Xia, Min; Liu, Wan’an; Wang, Ke; Zhang, Xu; Xu, Yiqing

doi:10.1016/j.epsr.2019.01.034

Cited by 82 publications

(39 citation statements)

References 24 publications

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“…The community has therefore focused on both supervised and unsupervised machine learning techniques. Among the supervised techniques, several neural network architectures have been proposed, such as Multi Layer Perceptron (MLP) [29], Convolutional Neural Network (CNN) [30][31][32][33][34][35][36], Recurrent Neural Network (RNN) [30,[37][38][39], Extreme Learning Machine [40], techniques based on Support Vector Machines (SVM) [16,41], K-Nearest Neighbors (kNN) [41,42] naive Bayes classifiers [15], Random Forest classifier [43] and Conditional Random Fields [44]. Among the unsupervised techniques, it was mainly those based on Hidden Markov Model that were used in this field [26,28,[45][46][47][48], although clustering techniques were also used [49,50].…”

Section: Introductionmentioning

confidence: 99%

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

2020

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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.

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Section: Introductionmentioning

confidence: 99%

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

2020

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“…Supervised techniques use offline training to achieve a database of information used to design the classifier (s). Some common supervised learning techniques that have been applied in NILM are (shallow) Artificial Neural Networks, mainly Multilayer Perceptron (MLP) [66,84], concatenated Convolutional Neural Networks (CNNs) [85], Deep Neural Networks [53,[86][87][88][89][90][91], Support Vector Machines (SVM) [66,92], K-Nearest Neighbours (k-NN) [92][93][94], naïve Bayes classifiers [64,94,95] and, recently, linear-chain Conditional random fields (CRFs), which takes into account how previous states influence the current state and can deal with multi-state loads [96]. In [97] the performance of three classifiers, MLPs, Radial Basis Function (RBF) networks and SVM, with different kernels, is compared by employing odd harmonics (up to the 15th) from the current waveform, measured in a proprietary experimental setup.…”

Section: Load Identificationmentioning

confidence: 99%

NILM Techniques for Intelligent Home Energy Management and Ambient Assisted Living: A Review

et al. 2019

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The ongoing deployment of smart meters and different commercial devices has made electricity disaggregation feasible in buildings and households, based on a single measure of the current and, sometimes, of the voltage. Energy disaggregation is intended to separate the total power consumption into specific appliance loads, which can be achieved by applying Non-Intrusive Load Monitoring (NILM) techniques with a minimum invasion of privacy. NILM techniques are becoming more and more widespread in recent years, as a consequence of the interest companies and consumers have in efficient energy consumption and management. This work presents a detailed review of NILM methods, focusing particularly on recent proposals and their applications, particularly in the areas of Home Energy Management Systems (HEMS) and Ambient Assisted Living (AAL), where the ability to determine the on/off status of certain devices can provide key information for making further decisions. As well as complementing previous reviews on the NILM field and providing a discussion of the applications of NILM in HEMS and AAL, this paper provides guidelines for future research in these topics.

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“…The Accuracy and F1-Measure metrics were used to assess the precision of the model's operation. In order to determine the second one, two scores called Recall and Precision, defined analogously to those applied by Kolter and Jaakkola [40], were used. Recall presents the percentage share of correctly classified cases.…”

Section: Performance Metricsmentioning

confidence: 99%

“…Precision is the percentage of correct classifications only for active appliances. If the designations of the numbers of correct and incorrect estimations according to Table 3 are adopted, the Recall and Precision scores can be determined using Formula 3and 4 [40].…”

Section: Performance Metricsmentioning

confidence: 99%

Modelling the Disaggregated Demand for Electricity in Residential Buildings Using Artificial Neural Networks (Deep Learning Approach)

Jasiński

2020

Energies

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The paper addresses the issue of modelling the demand for electricity in residential buildings with the use of artificial neural networks (ANNs). Real data for six houses in Switzerland fitted with measurement meters was used in the research. Their original frequency of 1 Hz (one-second readings) was re-sampled to a frequency of 1/600 Hz, which corresponds to a period of ten minutes. Out-of-sample forecasts verified the ability of ANNs to disaggregate electricity usage for specific applications (electricity receivers). Four categories of electricity consumption were distinguished: (i) fridge, (ii) washing machine, (iii) personal computer, and (iv) freezer. Both standard ANNs with multilayer perceptron architecture and newer types of networks based on deep learning were used. The simulations included over 10,000 ANNs with different architecture (number of neurons and structure of their connections), type and number of input variables, formulas of activation functions, training algorithms, and other parameters. The research confirmed the possibility of using ANNs to model the disaggregation of electricity consumption based on low frequency data, and suggested ways to build highly optimised models.

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Non-intrusive load disaggregation based on deep dilated residual network

Cited by 82 publications

References 24 publications

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

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

NILM Techniques for Intelligent Home Energy Management and Ambient Assisted Living: A Review

Modelling the Disaggregated Demand for Electricity in Residential Buildings Using Artificial Neural Networks (Deep Learning Approach)

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