Non-technical losses detection with Gramian angular field and deep residual network

Chen, Yuhui; Li, Jian; Huang, Qi Zhe; Li, Ké; Zhao, Zixu; Ren, Xibi

doi:10.1016/j.egyr.2023.05.183

Cited by 1 publication

(1 citation statement)

References 13 publications

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“…Gramian angular fields: GAFs have been one of the first approaches used to encode time series for multivariate image analysis [5] and remains the most popular approach for this purpose. Generally, GAF has been used in diverse fields ranging from tool wear classification with CNNs [6] to identification of nontechnical losses in power systems [7], recognition of wearable sensor-based human activity [8], fault detection in transmission lines [9], time series classification with vision transformers [10], and so on.…”

Section: Imaging Of Time Series Datamentioning

confidence: 99%

A Comparative Analysis of Image Encoding of Time Series for Anomaly Detection

Aldrich

2023

Time Series Analysis - Recent Advances, New Perspectives and Applications

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A novel approach to anomaly detection in time series data is based on the use of multivariate image analysis techniques. With this approach, time series are encoded as images that make them amenable to analysis by pretrained deep neural networks. Few studies have evaluated the merits of the different image encoding algorithms, and in this investigation, encoding of time series data with Euclidean distance plots or unthresholded recurrence plots, Gramian angular fields, Morlet wavelet scalograms, and an ad hoc approach based on the presentation of the raw time series data in a stacked format are compared. This is done based on three case studies where features are extracted from the images with gray level co-occurrence matrices, local binary patterns and the use of a pretrained convolutional neural network, GoogleNet. Although no method consistently outperformed all the other methods, the Euclidean distance plots and GoogleNet features yielded the best results.

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Section: Imaging Of Time Series Datamentioning

confidence: 99%

A Comparative Analysis of Image Encoding of Time Series for Anomaly Detection

Aldrich

2023

Time Series Analysis - Recent Advances, New Perspectives and Applications

View full text Add to dashboard Cite

show abstract

Non-technical losses detection with Gramian angular field and deep residual network

Cited by 1 publication

References 13 publications

A Comparative Analysis of Image Encoding of Time Series for Anomaly Detection

A Comparative Analysis of Image Encoding of Time Series for Anomaly Detection

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