Detection of unidentified appliances in non-intrusive load monitoring using siamese neural networks

Baets, Leen De; Develder, Chris; Dhaene, Tom; Deschrijver, Dirk

doi:10.1016/j.ijepes.2018.07.026

Cited by 96 publications

(49 citation statements)

References 16 publications

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“…The photons are checked until the signal photon clusters are completely expanded or there are no photons left to check. A photon is defined as a "noise photon" if it does not belong to any classified signal cluster [21,39,40]. The clustering result and fitness value are calculated using the values of eps and MinPts, and then optimized by an iterative process.…”

Section: Methodsmentioning

confidence: 99%

“…The algorithm used two parameters, eps and MinPts, to compare the density of data objects. For each data object in each cluster, the number of objects in an eps-neighborhood with a given radius had to be higher than a threshold MinPts [21]. The DBSCAN algorithm could efficiently cluster signal photons without target clusters and was able to discover clusters of an arbitrary shape.…”

Section: Introductionmentioning

confidence: 99%

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Particle Swarm Optimization-Based Noise Filtering Algorithm for Photon Cloud Data in Forest Area

et al. 2019

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The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), which is equipped with the Advanced Topographic Laser Altimeter System (ATLAS), was launched successfully in 15 September 2018. The ATLAS represents a micro-pulse photon-counting laser system, which is expected to provide more comprehensive and scientific data for carbon storage. However, the ATLAS system is sensitive to the background noise, which poses a tremendous challenge to the photon cloud noise filtering. Moreover, the Density Based Spatial Clustering of Applications with Noise (DBSCAN) is a commonly used algorithm for noise removal from the photon cloud but there has not been an in-depth study on its parameter selection yet. This paper presents an automatic photon cloud filtering algorithm based on the Particle Swarm Optimization (PSO) algorithm, which can be used to optimize the two key parameters of the DBSCAN algorithm instead of using the manual parameter adjustment. The Particle Swarm Optimization Density Based Spatial Clustering of Applications with Noise (PSODBSCAN) algorithm was tested at different laser intensities and laser pointing types using the MATLAS dataset of the forests located in Virginia, East Coast, and the West Coast, USA. The results showed that the PSODBSCAN algorithm and the localized statistical algorithm were effective in identifying the background noise and preserving the signal photons in the raw MATLAS data. Namely, the PSODBSCAN achieved the mean F value of 0.9759, and the localized statistical algorithm achieved the mean F value of 0.6978. For both laser pointing types and laser intensities, the proposed algorithm achieved better results than the localized statistical algorithm. Therefore, the PSODBSCAN algorithm could support the MATLAS photon cloud data noise filtering applicably without manually selecting parameters.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimization-Based Noise Filtering Algorithm for Photon Cloud Data in Forest Area

et al. 2019

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“…This data acquisition is commonly related to a device or system, very close to the existing electrical facilities, where different approaches can be deployed in order to measure certain parameters, such as currents or voltages, in a certain household or building. Sometimes other parameters, actually coming from these voltage and current signals, can be determined, such as the real power, the apparent power, the power factor, or the I-V trajectory [5], and used as features. Not only these parameters, but also their variation over time, are clues to guide our approach to any further energy disaggregation and appliance identification.…”

Section: Data Collectionmentioning

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 convolutional neural network (CNN) is used as classification algorithm. In [13], the same author aims to detect several household consumers whose signatures were not included in the initial implementation of the classification/identification algorithm. In this case, the binary image of the voltage-current trajectory is considered the signature.…”

Section: Introductionmentioning

confidence: 99%

“…As a result, measurements of aggregate power (e.g., at apartment level for domestic consumers) are required to detect consumers' transitions between states over timeframes. In [13], Baets considers that, in principle, the detection of an event is a side effect of the identification algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for Obtaining the Signature of Household Consumer Pairs

Căiman

Dragomir

2020

Energies

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The management of electricity consumption by household consumers requires multiple ways of consumer monitoring. One of these is the signature i(v) determined by monitoring the consumer voltage-current trajectory. The paper proposes a novel method for obtaining signatures of 2-multiple consumers, i.e., a pair of consumers connected in parallel. Signatures are obtained from samples of the voltage at the consumers’ terminals and of the total current absorbed by the consumers, measured at a frequency of only 20 Hz. Within the method, signatures are calculated using genetic algorithms (GA) and nonlinear regression, according to a procedure developed by the authors in a previous paper. The management of the data selected for the signature assignment represents the novelty. The method proposed in this paper is applied in two case studies, one concerning household consumers within the same power level, the other for household consumers of different power levels. The results confirm the possibility of obtaining signatures of i(v) type.

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Detection of unidentified appliances in non-intrusive load monitoring using siamese neural networks

Cited by 96 publications

References 16 publications

Particle Swarm Optimization-Based Noise Filtering Algorithm for Photon Cloud Data in Forest Area

Particle Swarm Optimization-Based Noise Filtering Algorithm for Photon Cloud Data in Forest Area

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

A Novel Method for Obtaining the Signature of Household Consumer Pairs

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