An Event-Driven Convolutional Neural Architecture for Non-Intrusive Load Monitoring of Residential Appliance

Yang, Dongsheng; Gao, Xiaoting; Kong, Liang; Pang, Yanwei; Zhou, Bowen

doi:10.1109/tce.2020.2977964

Cited by 125 publications

(66 citation statements)

References 37 publications

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“…In literature, the traditional two-dimensional (2-D) convolutional neural network (CNN or ConvNet) is a common feature in most if not all NILM image-based recognition systems. To improve the performance of NILM image-based designs it is necessary to modify the basic CNN structure [17][18][19][20][21][22]. Based on a twenty-one appliance dataset, the authors of [17], proposed a 2-D CNN composed of a residual model and a Batch Normalization layer to correct the gradient disappearance issue during training.…”

Section: Introductionmentioning

confidence: 99%

“…Clearly, there is a need to improve the NILM disaggregation performance in [18]. The event-driven NILM recognition method proposed in [19] captures event-based information that includes establishing the signal's zero-crossing point, the similarity between current signals, threshold measure and point at which event starts and stops. All these event current characteristics are converted to gray-scale images as an input of a VGG-16 CNN model.…”

Section: Introductionmentioning

confidence: 99%

“…All these event current characteristics are converted to gray-scale images as an input of a VGG-16 CNN model. The method in [19] achieved high NILM image-based recognition performance for a considerably reduced signal dataset when the number of appliances is few. However, there is a need to further improve the method in [19], as the NILM recognition accuracy degrades with an increase in the number of appliances.…”

Section: Introductionmentioning

confidence: 99%

“…Hence, we aim to exploit this property of the sdAE to obtain a clean appliance signal image from the mains supply signal composite image. Furthermore, we aim to address some of the imagebased NILM deficiencies [17][18][19][20][21][22]. As the authors in [18] we proposed GASF generated images for the disaggregation strategy, however, in our case we use an in-house generated dataset and go a step further to include the image-based equipment classification part which was not done in [18] who use a standard dataport dataset.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Image-based mains signal disaggregation and load recognition

Matindife

Sun

Wang

2021

Complex Intell. Syst.

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“…4,5 To that end, the solution to this issue is via adopting a smart low-cost monitoring, called nonintrusive load monitoring (NILM) or energy disaggregation as well. 6 NILM is the process of identifying individual device consumption footprints in a given building from the aggregated power consumption taken at the overall power entry without the need to install a smart meter for each appliance. 7,8 Device-level consumption footprints inferred by NILM can (i) provide end-users statistics with personalized consumption of each appliance through exploiting the aggregated power signal, without the need to install a smart sensor for each device and (ii) help end-users to comprehend their consumption behaviors and provide them with information on how to act for promoting energy saving.…”

Section: Introductionmentioning

confidence: 99%

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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Privacy boundary determination of smart meter data using an artificial intelligence adversary

Zhang

Watkins

Took

et al. 2021

Int Trans Electr Energ Syst

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The roll-out of the new-generation smart meter with artificial intelligence (AI)-based data mining algorithms causes serious privacy issues for consumers. By detecting appliance usages, an adversary can easily monitor the behavior patterns of residents. In this paper, a privacy-preserving smart metering model is proposed; the system utilizes a data aggregator to aggregate the readings of neighboring smart meters and a data down-sampler to reduce the sensitive information in the load profiles. An AI-based adversary is introduced to simulate the adversarial process. Four state-of-the-art deep learning/machine learning algorithms (convolutional neural network-long short-term memory (CNN-LSTM), gated recurrent unit (GRU), k-nearest neighbors (KNN), and CNN are employed as data mining algorithms. By tuning the variables (aggregation size α and interval resolution σ), the detectability boundaries of particular appliances are evaluated. Based on the appliance detectability, a three-level privacy boundary (real-time surveillance, presence/absence detection, and complete protection) is obtained. The result shows that to achieve complete data protection, the aggregation size should exceed 40, and the interval resolution should exceed 8 hours.

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An Event-Driven Convolutional Neural Architecture for Non-Intrusive Load Monitoring of Residential Appliance

Cited by 125 publications

References 37 publications

Image-based mains signal disaggregation and load recognition

Image-based mains signal disaggregation and load recognition

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

Privacy boundary determination of smart meter data using an artificial intelligence adversary

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