Smart Metering of Variable Power Loads

Wichakool, Warit; Remscrim, Zachary; Orji, Uzoma A.; Leeb, Steven B.

doi:10.1109/tsg.2014.2352648

Cited by 64 publications

(26 citation statements)

References 14 publications

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“…This can be alleviated by performing fine-grained measurements at the cost of an increased data storage rate and more complex data analytics. 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%

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: Related Workmentioning

confidence: 99%

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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“…This can be alleviated by performing higher frequency measurements at the cost of an increased data storage rate and more complex data analytics, i.e., the voltage and current signals sampled at a frequency higher than 1 Hz are measured. From these signals, features like the harmonics [3] and other frequency components [4] from the steady-state and transient behavior can be calculated. More recently, the possibility to consider voltage-current (VI) trajectories has also been considered [5]- [7].…”

Section: Introductionmentioning

confidence: 99%

VI-Based Appliance Classification Using Aggregated Power Consumption Data

Leen

Dhaene

Deschrijver

et al. 2018

2018 IEEE International Conference on Smart Computing (SMARTCOMP)

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Non-intrusive load monitoring detects active appliances in a household (and their power consumption) from measuring the aggregated power at just one point in that household. Our previous works focused on classifying a single appliance, assuming that the voltage and current trace could be isolated from an aggregated signal by considering the difference in current before and after the event. In this paper, we show that this assumption holds and that it is a viable approach in practice. We experimentally validate this for two classification methods we proposed earlier: (1) random forests using elliptical Fourier descriptors of the appliances' VI trajectories and (2) convolutional neural networks using the appliances' VI images. We benchmark these approaches on the aggregated data from the 2018 version of PLAID. We obtain, respectively for each of these classifiers, a maximal Fmacro-measure of 85.31% and 87.95 %. We also show that using submetered data for training does not improve the performance.

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“…This can be alleviated by performing fine-grained measurements at the cost of an increased data storage rate and more complex data analytics. It is then possible to calculate features like the harmonics [3] and the frequency components [4] from the steady-state and transient behavior of the current and voltage signal. More recently, the possibility 20 to consider voltage-current (VI) trajectories has also been considered [5,6,7].…”

mentioning

confidence: 99%

Appliance classification using VI trajectories and convolutional neural networks

Baets

Ruyssinck

Develder

et al. 2018

Energy and Buildings

187

115

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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.An informative characteristic to attain this goal is the voltage-current trajectory.In this paper, a weighted pixelated image of the voltage-current trajectory is used as input data for a deep learning method: a convolutional neural network that will automatically extract key features for appliance classification. The macro-average F -measure is 77.60% for the PLAID dataset and 75.46% for the WHITED dataset.

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Smart Metering of Variable Power Loads

Cited by 64 publications

References 14 publications

Automated classification of appliances using elliptical fourier descriptors

Automated classification of appliances using elliptical fourier descriptors

VI-Based Appliance Classification Using Aggregated Power Consumption Data

Appliance classification using VI trajectories and convolutional neural networks

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