Non-Intrusive Load Monitoring by Voltage–Current Trajectory Enabled Transfer Learning

Liu, Yanchi; Wang, Xue; You, Wei

doi:10.1109/tsg.2018.2888581

Cited by 168 publications

(105 citation statements)

References 184 publications

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“…In the literature, the voltage-current (V-I) trajectories were found powerful for formulating appliance signature. In [21] and [22], appliances were classified using V-I trajectory converted to grey-scale and color coded image, respectively. Though the models in [21] were able to successfully detect a large number of appliances, the washing machine, fan, fridge and air conditioner were not identified with better score.…”

Section: Related Workmentioning

confidence: 99%

“…Though the models in [21] were able to successfully detect a large number of appliances, the washing machine, fan, fridge and air conditioner were not identified with better score. Reference [22] used AlexNet transfer learning methodology. The authors in [17] used wavelet coefficients for identification of four appliances using Decision Tree (DT) and Nearest Neighbor (NN) classifiers on the setting of semi-supervised learning.…”

Section: Related Workmentioning

confidence: 99%

“…A disaggregation model can be trained either as a singletarget [5], [14] or multi-target [15] regression problem, or as a single-label [2], [16]- [18], or multi-label [3], [7], [19]- [22] classification problem. Single-and multi-label classification and multi-class classification were explored in many works in the literature.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Target Energy Disaggregation using Convolutional Neural Networks

Ayub¹,

Elsayed²

2020

IJACSA

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Non-Intrusive Load Monitoring (NILM) has become popular for smart meters in recent years due to its low cost installation and maintenance. However, it requires efficient and robust machine learning models to disaggregate the respective electrical appliance energy from the mains. This study investigated NILM in the form of direct point-to-point multiple and single target regression models using convolutional neural networks. Two model architectures have been utilized and compared using five different metrics on two benchmarking datasets (ENERTALK and REDD). The experimental results showed that multi-target disaggregation setting is more complex than single-target disaggregation. For multi-target setting of ENERTALK dataset, the highest individual F1-score is 95.37% and the overall average F1-score is 75.00%. Better results were obtained for the multi-target setting of the other dataset with higher overall average F1-score of 83.32%. Additionally, the robustness and knowledge transfer capability of the models through cross-appliance and cross-domain disaggregation was demonstrated by training for a specific appliance on a specific data, and testing for a different appliance, house and dataset. The proposed models can also disaggregate simultaneous operating appliances with higher F1-scores.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Multi-Target Energy Disaggregation using Convolutional Neural Networks

Ayub¹,

Elsayed²

2020

IJACSA

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“…It has been demonstrated that transforming the V-I trajectory into image representation and feeding it as the input to machine learning classifiers improves classification performance [11,14,[17][18][19][20][21]. However, the presented works use single-label learning, thus assuming that only one appliance is active at a time.…”

Section: Introductionmentioning

confidence: 99%

“…To improve the discriminating power of our method, we apply the Fryze power theory, which enables the decomposition of the current waveform into active and non-active components in time-domain [20,28]. Our research hypothesis is that the sum of the active and non-active components will exhibit unique and consistent characteristics based on the appliances that are running simultaneously, hence providing a distinctive feature for multi-label classification.…”

Section: Introductionmentioning

confidence: 99%

Multi-Label Learning for Appliance Recognition in NILM Using Fryze-Current Decomposition and Convolutional Neural Network

Faustine

Pereira²

2020

Energies

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The advance in energy-sensing and smart-meter technologies have motivated the use of a Non-Intrusive Load Monitoring (NILM), a data-driven technique that recognizes active end-use appliances by analyzing the data streams coming from these devices. NILM offers an electricity consumption pattern of individual loads at consumer premises, which is crucial in the design of energy efficiency and energy demand management strategies in buildings. Appliance classification, also known as load identification is an essential sub-task for identifying the type and status of an unknown load from appliance features extracted from the aggregate power signal. Most of the existing work for appliance recognition in NILM uses a single-label learning strategy which, assumes only one appliance is active at a time. This assumption ignores the fact that multiple devices can be active simultaneously and requires a perfect event detector to recognize the appliance. In this paper proposes the Convolutional Neural Network (CNN)-based multi-label learning approach, which links multiple loads to an observed aggregate current signal. Our approach applies the Fryze power theory to decompose the current features into active and non-active components and use the Euclidean distance similarity function to transform the decomposed current into an image-like representation which, is used as input to the CNN. Experimental results suggest that the proposed approach is sufficient for recognizing multiple appliances from aggregated measurements.

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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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Non-Intrusive Load Monitoring by Voltage–Current Trajectory Enabled Transfer Learning

Cited by 168 publications

References 184 publications

Multi-Target Energy Disaggregation using Convolutional Neural Networks

Multi-Target Energy Disaggregation using Convolutional Neural Networks

Multi-Label Learning for Appliance Recognition in NILM Using Fryze-Current Decomposition and Convolutional Neural Network

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

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