LSTM and Bat-Based RUSBoost Approach for Electricity Theft Detection

Adil, Muhammad; Javaid, Nadeem; Qasim, Umar; Ullah, Ibrar; Shafiq, Muhammad; Choi, Jin-Ghoo

doi:10.3390/app10124378

Cited by 82 publications

(45 citation statements)

References 35 publications

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“…A classifier based on machine learning is used because the power consumption data are usually in the form of one dimension and time series. ere have been many new studies [7][8][9][10][11][12][13][14][15][16][17], and the support vector machine (SVM) classifier is the most common method. In addition, there are studies [18][19][20] that used artificial neural networks to detect energy theft.…”

Section: Related Workmentioning

confidence: 99%

Energy Theft Detection in an Edge Data Center Using Deep Learning

Cheng

Zhang

Li³

et al. 2021

Mathematical Problems in Engineering

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With the development of smart grid information physical systems, some of the data processing functions gradually approach the edge layer of end-users. To better realize the energy theft detection function at the edge, we proposed an energy theft detection method based on the power consumption information acquisition system of power enterprises. The method involves the following steps. In the centralized data center, K-means is used to decompose a large amount of data into small data and then input and train neural network parameters to realize feature extraction. We design a neural network named DWMCNN, which can extract features from the day, week, and month and can extract more accurate features. In the edge data center, the random forest (RF) algorithm is used to classify the extracted features. The experimental results show that the clustering method accords with the idea of edge computing-distributed processing and improves the operation speed and that the feature extractor has good convergence performance. In addition, compared with the methods based on various classifiers, this method has higher accuracy and lower computational complexity, which is suitable for the deployment of edge data centers.

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Section: Related Workmentioning

confidence: 99%

Energy Theft Detection in an Edge Data Center Using Deep Learning

Cheng

Zhang

Li³

et al. 2021

Mathematical Problems in Engineering

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“…The RUSBoost technique is the combination of RUS and Adaboost. In [24,25], the authors used RUSBoost to perform ETD. Table 5 demonstrates the selection of the RUSBoost's hyperparameters using the grid-search technique.…”

Section: Logistic Regression (Lr)mentioning

confidence: 99%

“…In contrast to the manual methods, supervised machine learning solutions have gained the interest of utilities and academia for performing ETD. Studies based on the supervised learning techniques [20][21][22][23][24][25] focus on ETD using the large and imbalanced datasets obtained through the smart meters. However, the performances of these techniques are still not sufficient for the practical applications in utilities.…”

Section: Introductionmentioning

confidence: 99%

A Combined Deep Learning and Ensemble Learning Methodology to Avoid Electricity Theft in Smart Grids

et al. 2020

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Electricity is widely used around 80% of the world. Electricity theft has dangerous effects on utilities in terms of power efficiency and costs billions of dollars per annum. The enhancement of the traditional grids gave rise to smart grids that enable one to resolve the dilemma of electricity theft detection (ETD) using an extensive amount of data formulated by smart meters. This data are used by power utilities to examine the consumption behaviors of consumers and to decide whether the consumer is an electricity thief or benign. However, the traditional data-driven methods for ETD have poor detection performances due to the high-dimensional imbalanced data and their limited ETD capability. In this paper, we present a new class balancing mechanism based on the interquartile minority oversampling technique and a combined ETD model to overcome the shortcomings of conventional approaches. The combined ETD model is composed of long short-term memory (LSTM), UNet and adaptive boosting (Adaboost), and termed LSTM–UNet–Adaboost. In this regard, LSTM–UNet–Adaboost combines the advantages of deep learning (LSTM-UNet) along with ensemble learning (Adaboost) for ETD. Moreover, the performance of the proposed LSTM–UNet–Adaboost scheme was simulated and evaluated over the real-time smart meter dataset given by the State Grid Corporation of China. The simulations were conducted using the most appropriate performance indicators, such as area under the curve, precision, recall and F1 measure. The proposed solution obtained the highest results as compared to the existing benchmark schemes in terms of selected performance measures. More specifically, it achieved the detection rate of 0.92, which was the highest among existing benchmark schemes, such as logistic regression, support vector machine and random under-sampling boosting technique. Therefore, the simulation outcomes validate that the proposed LSTM–UNet–Adaboost model surpasses other traditional methods in terms of ETD and is more acceptable for real-time practices.

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“…Hasan et al [17] present the combination of CNN and long short-term memory (LSTM) where CNN is practiced to derive abstract features while the LSTM is employed for theft detection. The authors in [18] design a hybrid of LSTM and random under-sampling boosting (RUSBoost) for the detection of dishonest consumers. LSTM is employed to capture long term dependencies while RUS-Boost is employed for ETD.…”

Section: ) Supervised Solutionsmentioning

confidence: 99%