Electricity Theft Detection using Empirical Mode Decomposition and K-Nearest Neighbors

Aziz, Sumair; Naqvi, Syed Zohaib Hassan; Khan, Muhammad Umar; Aslam, Taimoor

doi:10.1109/icetst49965.2020.9080727

Cited by 45 publications

(14 citation statements)

References 4 publications

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“…In de Freita Lucena et al (2021), EMD is used to investigate numerous different criminal activities with the emphasis being on predicting and forecasting future criminal activity. In Aziz et al (2020), EMD is used to investigate electrical theft. In Ahmad et al (2020), EMD is used successfully to detect criminal activity using sonic data which could be used to improve security systems which are linked to lower and more competitive premiums.…”

Section: Actuarial Setting and Contextmentioning

confidence: 99%

Package AdvEMDpy: Algorithmic variations of empirical mode decomposition in Python

Jaarsveldt

Ames²,

Peters

et al. 2023

Ann. actuar. sci.

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Abstarct This work presents a $\textsf{Python}$ EMD package named AdvEMDpy that is both more flexible and generalises existing empirical mode decomposition (EMD) packages in $\textsf{Python}$ , $\textsf{R}$ , and $\textsf{MATLAB}$ . It is aimed specifically for use by the insurance and financial risk communities, for applications such as return modelling, claims modelling, and life insurance applications with a particular focus on mortality modelling. AdvEMDpy both expands upon the EMD options and methods available, and improves their statistical robustness and efficiency, providing a robust, usable, and reliable toolbox. Unlike many EMD packages, AdvEMDpy allows customisation by the user, to ensure that a broader class of linear, non-linear, and non-stationary time series analyses can be performed. The intrinsic mode functions (IMFs) extracted using EMD contain complex multi-frequency structures which warrant maximum algorithmic customisation for effective analysis. A major contribution of this package is the intensive treatment of the EMD edge effect which is the most ubiquitous problem in EMD and time series analysis. Various EMD techniques, of varying intricacy from numerous works, have been developed, refined, and, for the first time, compiled in AdvEMDpy. In addition to the EMD edge effect, numerous pre-processing, post-processing, detrended fluctuation analysis (localised trend estimation) techniques, stopping criteria, spline methods, discrete-time Hilbert transforms (DTHT), knot point optimisations, and other algorithmic variations have been incorporated and presented to the users of AdvEMDpy. This paper and the supplementary materials provide several real-world actuarial applications of this package for the user’s benefit.

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Section: Actuarial Setting and Contextmentioning

confidence: 99%

Package AdvEMDpy: Algorithmic variations of empirical mode decomposition in Python

Jaarsveldt

Ames²,

Peters

et al. 2023

Ann. actuar. sci.

View full text Add to dashboard Cite

show abstract

“…Hence, it is inappropriate to use an algorithm based on linearity [22]. EMD, which can be used to decompose sEMG, is a suitable method for efficiently processing nonlinear signals, such as sEMG [23]. EMD is mathematically expressed as a sum of IMF and residual for given signals, as expressed in Eq.…”

Section: Semg Signal Decomposition Using Emdmentioning

confidence: 99%

Multi-Stream CNN-Based Personal Recognition Method Using Surface Electromyogram for 5G Security

Kim¹,

Kim²,

Kim³

et al. 2022

Computers, Materials &Amp; Continua

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As fifth generation technology standard (5G) technology develops, the possibility of being exposed to the risk of cyber-attacks that exploits vulnerabilities in the 5G environment is increasing. The existing personal recognition method used for granting permission is a password-based method, which causes security problems. Therefore, personal recognition studies using bio-signals are being conducted as a method to access control to devices. Among bio-signal, surface electromyogram (sEMG) can solve the existing personal recognition problem that was unable to the modification of registered information owing to the characteristic changes in its signal according to the performed operation. Furthermore, as an advantage, sEMG can be conveniently measured from arms and legs. This paper proposes a personal recognition method using sEMG, based on a multi-stream convolutional neural network (CNN). The proposed method decomposes sEMG signals into intrinsic mode functions (IMF) using empirical mode decomposition (EMD) and transforms each IMF into a spectrogram. Personal recognition is performed by analyzing time-frequency features from the spectrogram transformed into multi-stream CNN. The database (DB) adopted in this paper is the Ninapro DB, which is a benchmark EMG DB. The experimental results indicate that the personal recognition performance of the multi-stream CNN using the IMF spectrogram improved by 1.91%, compared with the singlestream CNN using the spectrogram of raw sEMG.

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“…An electricity theft detection method using interpolation for missing data, empirical mode decomposition, and K-Nearest Neighbors (K-NN) that is adequate for a time series is proposed in [20] using a labeled dataset provided by State Grid Corporation of China that consists of over 42,000 of daily time series that stretch over 1035 days. Time-series feature extraction is implemented underlying the most significant features for the classification process.…”

Section: Literature Surveymentioning

confidence: 99%

Anomaly Detection with Machine Learning Algorithms and Big Data in Electricity Consumption

et al. 2021

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When analyzing smart metering data, both reading errors and frauds can be identified. The purpose of this analysis is to alert the utility companies to suspicious consumption behavior that could be further investigated with on-site inspections or other methods. The use of Machine Learning (ML) algorithms to analyze consumption readings can lead to the identification of malfunctions, cyberattacks interrupting measurements, or physical tampering with smart meters. Fraud detection is one of the classical anomaly detection examples, as it is not easy to label consumption or transactional data. Furthermore, frauds differ in nature, and learning is not always possible. In this paper, we analyze large datasets of readings provided by smart meters installed in a trial study in Ireland by applying a hybrid approach. More precisely, we propose an unsupervised ML technique to detect anomalous values in the time series, establish a threshold for the percentage of anomalous readings from the total readings, and then label that time series as suspicious or not. Initially, we propose two types of algorithms for anomaly detection for unlabeled data: Spectral Residual-Convolutional Neural Network (SR-CNN) and an anomaly trained model based on martingales for determining variations in time-series data streams. Then, the Two-Class Boosted Decision Tree and Fisher Linear Discriminant analysis are applied on the previously processed dataset. By training the model, we obtain the required capabilities of detecting suspicious consumers proved by an accuracy of 90%, precision score of 0.875, and F1 score of 0.894.

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Electricity Theft Detection using Empirical Mode Decomposition and K-Nearest Neighbors

Cited by 45 publications

References 4 publications

Package AdvEMDpy: Algorithmic variations of empirical mode decomposition in Python

Package AdvEMDpy: Algorithmic variations of empirical mode decomposition in Python

Multi-Stream CNN-Based Personal Recognition Method Using Surface Electromyogram for 5G Security

Anomaly Detection with Machine Learning Algorithms and Big Data in Electricity Consumption

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