A feasibility study of automated plug-load identification from high-frequency measurements

Gao, Jingkun; Kara, Emre; Giri, Suman; Bergés, Mario

doi:10.1109/globalsip.2015.7418189

Cited by 92 publications

(64 citation statements)

References 9 publications

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“…The generalization properties of the model are validated using leave-one-house-out cross-validation as recommended in [12]. Each training set contains data from 54 houses and the test set data from the remaining house.…”

Section: Classification Resultsmentioning

confidence: 99%

“…It is then possible to calculate features such as the harmonics [9] and frequency components [10] from the steadystate and transient behavior of the current and voltage signal. More recently, the possibility to consider voltage-current (VI) trajectories has also been considered [11], [12], [13], [14].…”

Section: Related Workmentioning

confidence: 99%

“…As an alternative, the VI trajectory can be converted into a binary VI image (n × n matrix) by meshing the VI trajectory, see Figure 2b. In [12], [13], each cell of the mesh is assigned a binary value that denotes whether or not it is traversed by the trajectory. Based on this binary VI image, several features can be extracted to classify different power loads [13].…”

Section: Related Workmentioning

confidence: 99%

“…Examples of features are the number of continuums of occupied cells, or the binary value of the left horizontal cell and central cell. In [12], the binary VI image is transformed into an input vector that can be fed directly into a classifier like random forests to classify different appliances.…”

Section: Related Workmentioning

confidence: 99%

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Automated classification of appliances using elliptical fourier descriptors

Baets

Develder

Dhaene

et al. 2017

2017 IEEE International Conference on Smart Grid Communications (SmartGridComm)

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Abstract-Non-intrusive load monitoring methods aim to disaggregate the total power consumption of a household into individual appliances by analysing changes in the voltage and current measured at the grid connection point of the household. The goal is to identify the active appliances, based on their unique fingerprint. This information can be communicated to the electricity provider and the end-user enabling the potential of smart grids. An informative characteristic to attain the appliance classification is the voltage-current trajectory. In this paper, this trajectory is represented as a binary image from which the contours are extracted. From these contours, the elliptic Fourier descriptors are calculated and used as input for several classification algorithms outputting the appliance name. Benchmarking this method on the PLAID dataset shows that the descriptors can yield a prediction accuracy up to 79%, comparable to the state-of-the-art, based on only a very compact representation (12 numbers).

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Section: Classification Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Automated classification of appliances using elliptical fourier descriptors

Baets

Develder

Dhaene

et al. 2017

2017 IEEE International Conference on Smart Grid Communications (SmartGridComm)

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“…Finally, the PLAID, HFED, WHITED, and COOLL datasets only contain data from the startup transients and spectral traces of several individual appliances. Consequently, they are only suitable to evaluate feature extraction and classification algorithms using cross-validation (Barsim, Mauch, & Yang, 2016;Gao, Kara, Giri, & Bergés, 2015). Likewise, it should also be possible to use PLAID, WHITED, and COOLL to classify power events from other datasets.…”

Section: Public Datasetsmentioning

confidence: 99%

Performance evaluation in non‐intrusive load monitoring: Datasets, metrics, and tools—A review

Pereira¹,

Nunes

2018

WIREs Data Min & Knowl

122

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Non-intrusive load monitoring (also known as NILM or energy disaggregation) is the process of estimating the energy consumption of individual appliances from electric power measurements taken at a limited number of locations in the electric distribution of a building. This approach reduces sensing infrastructure costs by relying on machine learning techniques to monitor electric loads. However, the ability to evaluate and benchmark the proposed approaches across different datasets is key for enabling the generalization of research findings and consequently contributes to the large-scale adoption of this technology. Still, only recently researchers have focused on creating and standardizing the existing datasets in order to deliver a single interface to run NILM evaluations. Furthermore, there is still no consensus regarding, which performance metrics should be used to measure and report the performance of NILM systems and their underlying algorithms. This paper provides a review of the main datasets, metrics, and tools for evaluating the performance of NILM systems and technologies. Specifically, we review three main topics: (a) publicly available datasets, (b) performance metrics, and (c) frameworks and toolkits. The review suggests future research directions in NILM systems and technologies, including cross-datasets, performance metrics for evaluation and generalizable frameworks for benchmarking NILM technology.

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An intelligent nonintrusive load monitoring scheme based on 2D phase encoding of power signals

et al. 2020

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Nonintrusive load monitoring (NILM) is the de facto technique for extracting device-level power consumption fingerprints at (almost) no cost from only aggregated mains readings. Specifically, there is no need to install an individual meter for each appliance. However, a robust NILM system should incorporate a precise appliance identification module that can effectively discriminate between various devices. In this context, this paper proposes a powerful method to extract accurate power fingerprints for electrical appliance identification. Rather than relying solely on time-domain (TD) analysis, this framework abstracts the phase encoding of the TD description of power signals using a two-dimensional (2D) representation. This allows mapping power trajectories to a novel 2D binary representation space, and then performing a histogramming process after converting binary codes to new decimal representations. This yields the final histogram of 2D phase encoding of power signals, namely, 2D-PEP. An empirical performance evaluation conducted with three realistic power consumption databases collected at distinct resolutions indicates that the proposed 2D-PEP descriptor achieves

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A feasibility study of automated plug-load identification from high-frequency measurements

Cited by 92 publications

References 9 publications

Automated classification of appliances using elliptical fourier descriptors

Automated classification of appliances using elliptical fourier descriptors

Performance evaluation in non‐intrusive load monitoring: Datasets, metrics, and tools—A review

An intelligent nonintrusive load monitoring scheme based on 2D phase encoding of power signals

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