On the Use of Concentrated Time–Frequency Representations as Input to a Deep Convolutional Neural Network: Application to Non Intrusive Load Monitoring

Houidi, Sarra; Fourer, Dominique; Auger, François

doi:10.3390/e22090911

Cited by 26 publications

(13 citation statements)

References 34 publications

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“…In several classification and disaggregation methods, a 2D image is generated from the one-dimensional NILM signal, allowing the use of well-known image processing and deep learning techniques for NILM classification. In [33], the 2D image was generated by a time-frequency Short-Time Fourier Transform (STFT). The spectrogram was applied as the input of a CNN, particularly designed for that work.…”

Section: Cnn For Nilm Using High-frequency Datamentioning

confidence: 99%

Scattering Transform for Classification in Non-Intrusive Load Monitoring

Aguiar

Lazzaretti

Mulinari³

et al. 2021

Energies

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Nonintrusive Load Monitoring (NILM) uses computational methods to disaggregate and classify electrical appliances signals. The classification is usually based on the power signatures of the appliances obtained by a feature extractor. State-of-the-art results were obtained extracting NILM features with convolutional neural networks (CNN). However, it depends on the training process with large datasets or data augmentation strategies. In this paper, we propose a feature extraction strategy for NILM using the Scattering Transform (ST). The ST is a convolutional network analogous to CNN. Nevertheless, it does not need a training process in the feature extraction stage, and the filter coefficients are analytically determined (not empirically, like CNN). We perform tests with the proposed method on different publicly available datasets and compare the results with state-of-the-art deep learning-based and traditional approaches (including wavelet transform and V-I representations). The results show that ST classification accuracy is more robust in terms of waveform parameters, such as signal length, sampling frequency, and event location. Besides, ST overcame the state-of-the-art techniques for single and aggregated loads (accuracies above 99% for all evaluated datasets), in different training scenarios with single and aggregated loads, indicating its feasibility in practical NILM scenarios.

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Section: Cnn For Nilm Using High-frequency Datamentioning

confidence: 99%

Scattering Transform for Classification in Non-Intrusive Load Monitoring

Aguiar

Lazzaretti

Mulinari³

et al. 2021

Energies

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“…Here, we are interested in event-based NILM systems, where the current and voltage measurements are recorded using a single sensor connected to the house electrical panel [3][4][5][6][7]. An event detection method is used to predict the changes in the aggre-gated power signals that occur at each HEA's operating state [6].…”

Section: Introductionmentioning

confidence: 99%

Comparative Evaluation of Non-Intrusive Load Monitoring Methods Using Relevant Features and Transfer Learning

et al. 2021

Self Cite

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Non-Intrusive Load Monitoring (NILM) refers to the analysis of the aggregated current and voltage measurements of Home Electrical Appliances (HEAs) recorded by the house electrical panel. Such methods aim to identify each HEA for a better control of the energy consumption and for future smart grid applications. Here, we are interested in an event-based NILM pipeline, and particularly in the HEAs’ recognition step. This paper focuses on the selection of relevant and understandable features for efficiently discriminating distinct HEAs. Our contributions are manifold. First, we introduce a new publicly available annotated dataset of individual HEAs described by a large set of electrical features computed from current and voltage measurements in steady-state conditions. Second, we investigate through a comparative evaluation a large number of new methods resulting from the combination of different feature selection techniques with several classification algorithms. To this end, we also investigate an original feature selection method based on a deep neural network architecture. Then, through a machine learning framework, we study the benefits of these methods for improving Home Electrical Appliance (HEA) identification in a supervised classification scenario. Finally, we introduce new transfer learning results, which confirm the relevance and the robustness of the selected features learned from our proposed dataset when they are transferred to a larger dataset. As a result, the best investigated methods outperform the previous state-of-the-art results and reach a maximum recognition accuracy above 99% on the PLAID evaluation dataset.

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“…Readers who are interested in the details may refer to the latest state-of-the-art articles [ 9 , 10 , 11 , 12 ]. Researchers have devoted efforts to enhancing the ELD model from an algorithmic perspective, particularly toward deep learning approaches [ 13 , 14 , 15 ]. The advantages of deep learning compared to shallow learning have been demonstrated in large-scale datasets.…”

Section: Introductionmentioning

confidence: 99%

Handling Data Heterogeneity in Electricity Load Disaggregation via Optimized Complete Ensemble Empirical Mode Decomposition and Wavelet Packet Transform

Chui

Gupta

Liu

et al. 2021

Sensors

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Global warming is a leading world issue driving the common social objective of reducing carbon emissions. People have witnessed the melting of ice and abrupt changes in climate. Reducing electricity usage is one possible method of slowing these changes. In recent decades, there have been massive worldwide rollouts of smart meters that automatically capture the total electricity usage of houses and buildings. Electricity load disaggregation (ELD) helps to break down total electricity usage into that of individual appliances. Studies have implemented ELD models based on various artificial intelligence techniques using a single ELD dataset. In this paper, a powerline noise transformation approach based on optimized complete ensemble empirical model decomposition and wavelet packet transform (OCEEMD–WPT) is proposed to merge the ELD datasets. The practical implications are that the method increases the size of training datasets and provides mutual benefits when utilizing datasets collected from other sources (especially from different countries). To reveal the effectiveness of the proposed method, it was compared with CEEMD–WPT (fixed controlled coefficients), standalone CEEMD, standalone WPT, and other existing works. The results show that the proposed approach improves the signal-to-noise ratio (SNR) significantly.

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On the Use of Concentrated Time–Frequency Representations as Input to a Deep Convolutional Neural Network: Application to Non Intrusive Load Monitoring

Cited by 26 publications

References 34 publications

Scattering Transform for Classification in Non-Intrusive Load Monitoring

Scattering Transform for Classification in Non-Intrusive Load Monitoring

Comparative Evaluation of Non-Intrusive Load Monitoring Methods Using Relevant Features and Transfer Learning

Handling Data Heterogeneity in Electricity Load Disaggregation via Optimized Complete Ensemble Empirical Mode Decomposition and Wavelet Packet Transform

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