DeepDFML-NILM: A New CNN-Based Architecture for Detection, Feature Extraction and Multi-Label Classification in NILM Signals

Nolasco, Lucas da Silva; Lazzaretti, André E.; Mulinari, Bruna Machado

doi:10.1109/jsen.2021.3127322

Cited by 36 publications

(17 citation statements)

References 29 publications

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“…Nolasco et al [5] proposed the DeepDFML architecture that integrated disaggregation, event detection, and multi-label classification. The DeepDFML had a shared DCNN stage, with three fully connected sub-networks, each one for one specific task.…”

Section: Related Workmentioning

confidence: 99%

“…In the last five years, the scientific community has presented many feature extractors based on Deep Convolutional Networks (DCNNs), which we number as a fourth category of feature extractors (iv), in addition to CPD, TFA and VI Trajectories. DCNN methods are highly discriminative and overcame state-of-the-art performances for NILM in the last five years, both in terms of classification and disaggregation [5,6]. However, the performance of the DCNN methods depends on the network architecture, and mostly, on the amount of data available for training.…”

Section: Introductionmentioning

confidence: 99%

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Features Extraction and Selection with the Scattering Transform for Electrical Load Classification

Aguiar¹,

Lazzaretti²,

Pipa³

2023

Learn. Nonlin. Mod.

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The Scattering Transform (ST) presents itself as an alternative approach to the classic methods that involve neural networks and deep learning techniques for the feature extraction and classification of electrical signals. Among its main advantages, one can emphasize that the coefficients of the ST are determined analytically and do not need to be learned, as typically performed in Convolutional Neural Networks (CNNs). Additionally, ST has time-shifting and small time-warping invariance, which reduces the need for precise temporal localization (detection) for subsequent classification. This paper originally proposes six feature extraction and selection methods applied to classification of Non-intrusive Load Monitoring (NILM) high-frequency signals. We visually analyze the separability among classes for the proposed Feature Extractors and validate the performance of the proposed methods varying several parameters for ST calculation, such as signal length, number of examples, and sampling frequency. The results outperform other state-of-the-art feature extraction techniques, reaching up to 100% of FScore for a publicly available dataset, demonstrating the feasibility and promising aspects of the ST for NILM problems.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Features Extraction and Selection with the Scattering Transform for Electrical Load Classification

Aguiar¹,

Lazzaretti²,

Pipa³

2023

Learn. Nonlin. Mod.

View full text Add to dashboard Cite

show abstract

“…Because of the powerful ability of CNN in image classification and pattern recognition [8], the application of CNN in image segmentation has been explored by many researchers. Multiple information sources in the form of two- dimensional images are transmitted to the CNN input layer of different image channels, and it is studied whether using multimodal images as input can improve the segmentation results.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Semantic segmentation of remote sensing image based on U-NET

Yao,

Jia,

Dai

2023

Third International Conference on Image Processing and Intelligent Control (IPIC 2023)

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At present, the image processing of remote sensing technology mainly depends on the transcendental ability of human beings, and it needs to spend a lot of artificial resources to mark. Therefore, this paper proposes a research and application of semantic segmentation method for remote sensing images based on convolutional neural network. Normalize the data, subtract the mean value and divide it by the standard deviation to standardize, divide the data, introduce data enhancement to further enhance the training data, and create a convolutional neural network and a training network. Each layer of U-NET is composed of three layers of convolution, and features are extracted and integrated by pooling or up-sampling. At the last layer, all the previously extracted features are classified into two categories to realize the semantic segmentation of the image. The experimental results show that the F1 score, Recall score and Precision score of this method are 84.31%, 89.59% and 79.62%, respectively. By introducing U-NET, the semantic segmentation accuracy of remote sensing images is improved. Compared with the traditional full convolution neural network, U-NET has been improved. Through the stronger connection between layers, plus up-sampling and down-convolution, features can be fully extracted and accurate segmentation can be achieved with fewer training samples.

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“…Due to the availability of a large quantity of low-frequency electrical load measurements from smart meters, deep learning (DL) approaches have recently become popular, representing the current state of the art in NILM both for regression and classification tasks [5], [6], [7], [8], [9], [10], [11], [12], [13].…”

Section: Introductionmentioning

confidence: 99%

Knowledge Distillation for Scalable Nonintrusive Load Monitoring

Tanoni,

Stankovic,

Stankovic

et al. 2024

IEEE Trans. Ind. Inf.

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Smart meters allow the grid to interface with individual buildings and extract detailed consumption information using nonintrusive load monitoring (NILM) algorithms applied to the acquired data. Deep neural networks, which represent the state of the art for NILM, are affected by scalability issues since they require high computational and memory resources, and by reduced performance when training and target domains mismatched. This article proposes a knowledge distillation approach for NILM, in particular for multilabel appliance classification, to reduce model complexity and improve generalization on unseen data domains. The approach uses weak supervision to reduce labeling effort, which is useful in practical scenarios. Experiments, conducted on U.K.-DALE and RE-FIT datasets, demonstrated that a low-complexity network can be obtained for deployment on edge devices while maintaining high performance on unseen data domains. The proposed approach outperformed benchmark methods in unseen target domains achieving a F 1 -score 0.14 higher than a benchmark model 78 times more complex.

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DeepDFML-NILM: A New CNN-Based Architecture for Detection, Feature Extraction and Multi-Label Classification in NILM Signals

Cited by 36 publications

References 29 publications

Features Extraction and Selection with the Scattering Transform for Electrical Load Classification

Features Extraction and Selection with the Scattering Transform for Electrical Load Classification

Semantic segmentation of remote sensing image based on U-NET

Knowledge Distillation for Scalable Nonintrusive Load Monitoring

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