Low Complexity Event Detection Algorithm for Non- Intrusive Load Monitoring Systems

Rehman, Attique Ur; Lie, Tek Tjing; Vallès, Brice; Tito, Shafiqur Rahman

doi:10.1109/isgt-asia.2018.8467919

Cited by 14 publications

(9 citation statements)

References 12 publications

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“…e electric car charging in the smart home is now a prominent feature requiring consideration in the NILM recognition system design. e authors in [9] showed that the electric car charging can successfully be implemented into the NILM system using data from the Pecan Street Inc. Dataport. ere are a number of challenges facing NILM recognition systems for achieving high recognition performance and they include the follows: (1) the system includes some equal or very close power specification electronic appliances (EVPSAs) during steady state operation and having basically identical signature characteristics, (2) the system has low power appliances that are difficult to recognize and are often interpreted as noise when the aggregate is composed of low and high power appliances (LHPAs), (3) the system includes continuously variable operating states' (CVOS) appliances, and (4) the same power appliances are switched on/off at the same time [5][6][7]10].…”

Section: Background and Motivationsmentioning

confidence: 99%

A Machine-Learning Based Nonintrusive Smart Home Appliance Status Recognition

Matindife

Sun

Wang

2020

Mathematical Problems in Engineering

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In a smart home, the nonintrusive load monitoring recognition scheme normally achieves high appliance recognition performance in the case where the appliance signals have widely varying power levels and signature characteristics. However, it becomes more difficult to recognize appliances with equal or very close power specifications, often with almost identical signature characteristics. In literature, complex methods based on transient event detection and multiple classifiers that operate on different hand crafted features of the signal have been proposed to tackle this issue. In this paper, we propose a deep learning approach that dispenses with the complex transient event detection and hand crafting of signal features to provide high performance recognition of close tolerance appliances. The appliance classification is premised on the deep multilayer perceptron having three appliance signal parameters as input to increase the number of trainable samples and hence accuracy. In the case where we have limited data, we implement a transfer learning-based appliance classification strategy. With the view of obtaining an appropriate high performing disaggregation deep learning network for the said problem, we explore individually three deep learning disaggregation algorithms based on the multiple parallel structure convolutional neural networks, the recurrent neural network with parallel dense layers for a shared input, and the hybrid convolutional recurrent neural network. We disaggregate a total of three signal parameters per appliance in each case. To evaluate the performance of the proposed method, some simulations and comparisons have been carried out, and the results show that the proposed method can achieve promising performance.

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Section: Background and Motivationsmentioning

confidence: 99%

A Machine-Learning Based Nonintrusive Smart Home Appliance Status Recognition

Matindife

Sun

Wang

2020

Mathematical Problems in Engineering

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“…The aggregated signal can be very noisy, and only a few electrical appliances could be detected, depending on the sampling frequency. Even with advanced artificial intelligence (AI) algorithms, it could be possible to monitor only a few major appliances: e.g., oven, washing machine, air-conditioner, and EV [7], [10], [11]. When facing these kinds of scenarios in terms of the type of appliance used, performance remains inconclusive on different datasets [12].…”

Section: Introductionmentioning

confidence: 99%

“…One refers to a model in which energy consumption profiles are obtained from the device level using submeasurement sensors attached to appliances. The second is smart appliances (SA), which are devices with built-in capabilities to monitor and report their energy usage [11], [13]. The feature extraction consists of computing unique vectors using different procedures (e.g., sliding window) to be set as input of a ML-based classifier [14], [15].…”

Section: Introductionmentioning

confidence: 99%

A Framework for IoT Based Appliance Recognition in Smart Homes

et al. 2021

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Internet of Things (IoT) technologies will play an important role in enabling the smart grid achieving its goals in monitoring, protecting, and controlling by incorporating sensors, actuators, and metering devices while supporting various network functions and system automation. In this regard, home energy management systems (HEMS) enable households to better manage energy consumption by providing information about energy usage and allowing more precise control of major appliances. This work proposes a novel framework for IoT based appliance recognition in smart homes. It consists of two parts: training framework and inference framework. The proposed framework allows incorporating different loads in the monitoring system and enables selecting and testing specific parameters related to dataset configuration, feature extraction, and classifier model setting. The work contributes by developing an easy-to-use tool that allows customization of the training/prediction parameters according to the user criterion. Once the data and all its parameters are loaded, a novel feature extraction algorithm is used to obtain a total of ten statistical features. For the classification task, three machine learning models are included: a feed-forward neural network (FFNN), a long short-term memory (LSTM) and a support vector machine (SVM). In addition, the user can apply a set of techniques to overcome the class imbalance, and also measure the influence of the selected features in the classifiers' prediction by performing a feature importance analysis.INDEX TERMS Appliance recognition, frameworks, intrusive load monitoring, internet of things, smart grids, smart homes.

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“…Some examples of NILM power series based recognition systems include: (1) the "sequence-to-point learning" where the output is made up of one point of the target appliance and input is made up of a window of the aggregate signal as raw data, (2) onedimensional convolutional differential input systems, and 3) stacked denoising autoencoders (sdAEs) with the ability to reconstruct a good signal from a composite of noise and signal [4][5][6][7][8][9]. The NILM method has traditionally been based on the power series format of the equipment signal [6,7,10] in labeled or unlabeled form, often with a detailed incorporation of event detection mechanism [11,12]. The appliance features that are used in NILM systems broadly fall in the following categories of steady state (power change, time and frequency domain voltage-current (V-I), V-I trajectory), transient state (transient power, start-up current waveforms, voltage noise), combined steady and transient states features, and features obtained or inferred from the behavior of the appliance [13].…”

Section: Introductionmentioning

confidence: 99%

Image-based mains signal disaggregation and load recognition

Matindife

Sun

Wang

2021

Complex Intell. Syst.

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The mains signal is a complex fusion of various electrical equipment load signals in a building. In the non-intrusive load monitoring recognition, our main aim is to be able to extract as much load features as possible from the complex aggregate mains signal in a simpler way through a computer vision-based approach as opposed to the powers series signal approach. Power series methods, which are one dimensional in nature, suffer from poor aggregate and load signal feature localization necessitating a larger training dataset spanning very long time periods and normally require signal formatting and pre-processing. We use Gramian angular summation fields to transform the power series into a reduced image dataset that contains a rich set of localized signal features. A computer vision approach allows us to capture as much information as possible, and then propose an image-based mains load recognition system with high performance. In this paper for the entire recognition system, we use convolutional neural networks that very well adapted to vision recognition. The load signal image disaggregation is achieved through the powerful stacked denoising autoencoder noise extraction network. To test the proposed system, some simulations and comparisons are carried out and the results show that our easier to handle method can achieve acceptable performance.

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Low Complexity Event Detection Algorithm for Non- Intrusive Load Monitoring Systems

Cited by 14 publications

References 12 publications

A Machine-Learning Based Nonintrusive Smart Home Appliance Status Recognition

A Machine-Learning Based Nonintrusive Smart Home Appliance Status Recognition

A Framework for IoT Based Appliance Recognition in Smart Homes

Image-based mains signal disaggregation and load recognition

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