Recurrence Plot Spacial Pyramid Pooling Network for Appliance Identification in Non-Intrusive Load Monitoring

Wenninger, Marc; Bayerl, Sebastian P.; Maier, Andreas; Schmidt, Jochen

doi:10.1109/icmla52953.2021.00025

Cited by 6 publications

(4 citation statements)

References 39 publications

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“…In [26], the V-I trajectory and amplitudes of current and voltage were mapped as a color image, which provided richer feature information to CNN-based load recognition. Wenninger et al [27] mapped a cycle of V-I trajectories as a threshold-free recursive graphs, and subsequently designed a Spatial Pyramid Pooling (SPP) convolutional neural network for load recognition.…”

Section: Related Workmentioning

confidence: 99%

Learning-Based Non-Intrusive Electric Load Monitoring for Smart Energy Management

He,

Liu,

Zhang

et al. 2024

Preprint

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The state-of-the-art smart city has been calling for an economic but efficient energy management over large-scale network, especially for the electric power system. It is a critical issue to monitor, analyze and control electric loads of all users in system. In this paper, we employ the popular computer vision techniques of AI to design a non-intrusive load monitoring method for smart electric energy management. First of all, we utilize both signal transforms (including wavelet transform and discrete Fourier transform) and Gramian Angular Field (GAF) methods to map one-dimensional current signals onto two-dimensional color feature images. Second, we propose to recognize all electric loads from color feature images using a deep neural network with multi-scale feature extraction and attention mechanism. Third, we design our method as a cloud-based, non-intrusive monitoring of all users, thereby saving energy cost during electric power system control. Experimental results on both public and our private datasets have demonstrated our method achieves superior performances than its peers, and thus supports efficient energy management over large-scale Internet of Things.

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Section: Related Workmentioning

confidence: 99%

Learning-Based Non-Intrusive Electric Load Monitoring for Smart Energy Management

He,

Liu,

Zhang

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…In [26], the V-I trajectory and amplitudes of current and voltage were mapped as a color image, which provided richer feature information for CNN-based load recognition. Wenninger et al [27] mapped a cycle of V-I trajectories as threshold-free recursive graphs and subsequently designed a Spatial Pyramid Pooling (SPP) convolutional neural network for load recognition. Despite these great efforts, there still exists a need to further improve the accuracy of the LRA.…”

Section: Introductionmentioning

confidence: 99%

Learning-Based Non-Intrusive Electric Load Monitoring for Smart Energy Management

He,

Liu,

Zhang

et al. 2024

Sensors

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State-of-the-art smart cities have been calling for economic but efficient energy management over a large-scale network, especially for the electric power system. It is a critical issue to monitor, analyze, and control electric loads of all users in the system. In this study, a non-intrusive load monitoring method was designed for smart power management using computer vision techniques popular in artificial intelligence. First of all, one-dimensional current signals are mapped onto two-dimensional color feature images using signal transforms (including the wavelet transform and discrete Fourier transform) and Gramian Angular Field (GAF) methods. Second, a deep neural network with multi-scale feature extraction and attention mechanism is proposed to recognize all electrical loads from the color feature images. Third, a cloud-based approach was designed for the non-intrusive monitoring of all users, thereby saving energy costs during power system control. Experimental results on both public and private datasets demonstrate that the method achieves superior performances compared to its peers, and thus supports efficient energy management over a large-scale Internet of Things network.

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“…Thus, for every 10 network cycles fed to the proposed CNN architecture, the output is the appliance event type classification. In another example in [18], the instantaneous steady-state current and voltage measurements are extracted and a search process for the zero crossing of the voltage follows. When the zero-crossing voltage is detected, a steady-state cycle of the instantaneous current and voltage measurements is extracted.…”

Section: Introductionmentioning

confidence: 99%

A Time-Driven Deep Learning NILM Framework Based on Novel Current Harmonic Distortion Images

Papageorgiou,

Mylona,

Stergiou

et al. 2023

Sustainability

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Non-intrusive load monitoring (NILM) has been on the rise for more than three decades. Its main objective is non-intrusive load disaggregation into individual operating appliances. Recent studies have shown that a higher sampling rate in the aggregated measurements allows better performance regarding load disaggregation. In addition, recent developments in deep learning and, in particular, convolutional neural networks (CNNs) have facilitated load disaggregation using CNN models. Several methods have been described in the literature that combine both a higher sampling rate and a CNN-based NILM framework. However, these methods use only a small number of cycles of the aggregated signal, which complicates the practical application of real-time NILM. In this work, a high sampling rate time-driven CNN-based NILM framework is also proposed. However, a novel current harmonic distortion image extracted from 60 cycles of the aggregated signal is proposed, resulting in 1 s appliance classification with low computational complexity. Appliance classification performance is evaluated using the PLAID3 dataset for both single and combined appliance operation. In addition, a comparison is made with a method from the literature. The results highlight the robustness of the novel feature and confirm the real-time applicability of the proposed NILM framework.

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Recurrence Plot Spacial Pyramid Pooling Network for Appliance Identification in Non-Intrusive Load Monitoring

Cited by 6 publications

References 39 publications

Learning-Based Non-Intrusive Electric Load Monitoring for Smart Energy Management

Learning-Based Non-Intrusive Electric Load Monitoring for Smart Energy Management

Learning-Based Non-Intrusive Electric Load Monitoring for Smart Energy Management

A Time-Driven Deep Learning NILM Framework Based on Novel Current Harmonic Distortion Images

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