Low-Complexity Non-Intrusive Load Monitoring Using Unsupervised Learning and Generalized Appliance Models

Liu, Qi; Kamoto, Kondwani Michael; Liu, Xiaodong; Sun, Mingxu; Linge, Nigel

doi:10.1109/tce.2019.2891160

Cited by 114 publications

(46 citation statements)

References 32 publications

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“…Naturally, it is believed that this problem could be alleviated if the domain knowledge could be incorporated into the model. In literature, various methods have been proposed for tackling NILM, for example, signal processing approaches which use features of appliances for the purpose of disaggregations [9], [10], [11], [12], [13], clustering algorithms [14], [15], matrix factorization [16], [17], etc. There are mainly two approaches to NILM using machine learning, which are unsupervised and supervised learning.…”

Section: Introductionmentioning

confidence: 99%

Transfer Learning for Non-Intrusive Load Monitoring

D'Incecco

Squartini

Zhong

2020

IEEE Trans. Smart Grid

228

159

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Non-intrusive load monitoring (NILM) is a technique to recover source appliances from only the recorded mains in a household. NILM is unidentifiable and thus a challenge problem because the inferred power value of an appliance given only the mains could not be unique. To mitigate the unidentifiable problem, various methods incorporating domain knowledge into NILM have been proposed and shown effective experimentally. Recently, among these methods, deep neural networks are shown performing best. Arguably, the recently proposed sequenceto-point (seq2point) learning is promising for NILM. However, the results were only carried out on the same data domain. It is not clear if the method could be generalised or transferred to different domains, e.g., the test data were drawn from a different country comparing to the training data. We address this issue in the paper, and two transfer learning schemes are proposed, i.e., appliance transfer learning (ATL) and crossdomain transfer learning (CTL). For ATL, our results show that the latent features learnt by a 'complex' appliance, e.g., washing machine, can be transferred to a 'simple' appliance, e.g., kettle. For CTL, our conclusion is that the seq2point learning is transferable. Precisely, when the training and test data are in a similar domain, seq2point learning can be directly applied to the test data without fine tuning; when the training and test data are in different domains, seq2point learning needs fine tuning before applying to the test data. Interestingly, we show that only the fully connected layers need fine tuning for transfer learning. Source code can be found at https://github.com/MingjunZhong/transferNILM.

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Section: Introductionmentioning

confidence: 99%

Transfer Learning for Non-Intrusive Load Monitoring

D'Incecco

Squartini

Zhong

2020

IEEE Trans. Smart Grid

228

159

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“…More recently, Decision Trees (DT) and Long Short-Time Memory (LSTM) models are used for event detection [47], obtaining 98.6% and 92.6% detection accuracy, respectively. Furthermore, [48] presents a very simple detection algorithm used in a low-complexity NILM proposal, achieving suitable performance in six houses from the REDD dataset.…”

Section: Event Detectionmentioning

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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“…Nonlinear components exist in household and office equipment, such as TV sets, energy-saving lamps, washing machines, and medical instruments. The survey of harmonics shows [8][9][10] that common electrical equipment mainly produces odd harmonics of no more than 13 orders [27,28], and a small number of electrical appliances produce harmonics of more than 20 orders, but the content of harmonics is extremely small. The harmonics of 1-32 orders can almost reflect the complete harmonics of equipment.…”

Section: Analysis Of Load Characteristicsmentioning

confidence: 99%

K-Means Clustering-Based Electrical Equipment Identification for Smart Building Application

Zhang

Deng

2020

Information

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With the development and popular application of Building Internet of Things (BIoT) systems, numerous types of equipment are connected, and a large volume of equipment data is collected. For convenient equipment management, the equipment should be identified and labeled. Traditionally, this process is performed manually, which not only is time consuming but also causes unavoidable omissions. In this paper, we propose a k-means clustering-based electrical equipment identification toward smart building application that can automatically identify the unknown equipment connected to BIoT systems. First, load characteristics are analyzed and electrical features for equipment identification are extracted from the collected data. Second, k-means clustering is used twice to construct the identification model. Preliminary clustering adopts traditional k-means algorithm to the total harmonic current distortion data and separates equipment data into two to three clusters on the basis of their electrical characteristics. Later clustering uses an improved k-means algorithm, which weighs Euclidean distance and uses the elbow method to determine the number of clusters and analyze the results of preliminary clustering. Then, the equipment identification model is constructed by selecting the cluster centroid vector and distance threshold. Finally, identification results are obtained online on the basis of the model outputs by using the newly collected data. Successful applications to BIoT system verify the validity of the proposed identification method.

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Low-Complexity Non-Intrusive Load Monitoring Using Unsupervised Learning and Generalized Appliance Models

Cited by 114 publications

References 32 publications

Transfer Learning for Non-Intrusive Load Monitoring

Transfer Learning for Non-Intrusive Load Monitoring

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

K-Means Clustering-Based Electrical Equipment Identification for Smart Building Application

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