TY - JOUR

T1 - Towards Sustainable Energy Efficiency with Intelligent Electricity Theft Detection in Smart Grids Emphasising Enhanced Neural Networks

AU - Aldegheishem, A.

AU - Anwar, M.

AU - Javaid, N.

AU - Alrajeh, N.

AU - Shafiq, M.

AU - Ahmed, H.

PY - 2021/2/12

Y1 - 2021/2/12

N2 - In smart grids, electricity theft is the most significant challenge. It cannot be identified easily since existing methods are dependent on specific devices. Also, the methods lack in extracting meaningful information from high-dimensional electricity consumption data and increase the false positive rate that limit their performance. Moreover, imbalanced data is a hurdle in accurate electricity theft detection (ETD) using data driven methods. To address this problem, sampling techniques are used in the literature. However, the traditional sampling techniques generate insufficient and unrealistic data that degrade the ETD rate. In this work, two novel ETD models are developed. A hybrid sampling approach, i.e., synthetic minority oversampling technique with edited nearest neighbor, is introduced in the first model. Furthermore, AlexNet is used for dimensionality reduction and extracting useful information from electricity consumption data. Finally, a light gradient boosting model is used for classification purpose. In the second model, conditional wasserstein generative adversarial network with gradient penalty is used to capture the real distribution of the electricity consumption data. It is constructed by adding auxiliary provisional information to generate more realistic data for the minority class. Moreover, GoogLeNet architecture is employed to reduce the dataset’s dimensionality. Finally, adaptive boosting is used for classification of honest and suspicious consumers. Both models are trained and tested using real power consumption data provided by state grid corporation of China. The proposed models’ performance is evaluated using different performance metrics like precision, recall, accuracy, F1-score, etc. The simulation results prove that the proposed models outperform the existing techniques, such as support vector machine, extreme gradient boosting, convolution neural network, etc., in terms of efficient ETD.

AB - In smart grids, electricity theft is the most significant challenge. It cannot be identified easily since existing methods are dependent on specific devices. Also, the methods lack in extracting meaningful information from high-dimensional electricity consumption data and increase the false positive rate that limit their performance. Moreover, imbalanced data is a hurdle in accurate electricity theft detection (ETD) using data driven methods. To address this problem, sampling techniques are used in the literature. However, the traditional sampling techniques generate insufficient and unrealistic data that degrade the ETD rate. In this work, two novel ETD models are developed. A hybrid sampling approach, i.e., synthetic minority oversampling technique with edited nearest neighbor, is introduced in the first model. Furthermore, AlexNet is used for dimensionality reduction and extracting useful information from electricity consumption data. Finally, a light gradient boosting model is used for classification purpose. In the second model, conditional wasserstein generative adversarial network with gradient penalty is used to capture the real distribution of the electricity consumption data. It is constructed by adding auxiliary provisional information to generate more realistic data for the minority class. Moreover, GoogLeNet architecture is employed to reduce the dataset’s dimensionality. Finally, adaptive boosting is used for classification of honest and suspicious consumers. Both models are trained and tested using real power consumption data provided by state grid corporation of China. The proposed models’ performance is evaluated using different performance metrics like precision, recall, accuracy, F1-score, etc. The simulation results prove that the proposed models outperform the existing techniques, such as support vector machine, extreme gradient boosting, convolution neural network, etc., in terms of efficient ETD.

KW - Adaptation models

KW - Biological system modeling

KW - Boosting

KW - Data models

KW - Electricity theft detection

KW - Gallium nitride

KW - generative adversarial network

KW - Generative adversarial networks

KW - GoogLeNet

KW - imbalanced data

KW - Meters

KW - SMOTEENN

KW - Crime

KW - Data mining

KW - Dimensionality reduction

KW - Electric power transmission networks

KW - Electric power utilization

KW - Energy efficiency

KW - Mobile telecommunication systems

KW - Neural networks

KW - Support vector machines

KW - Adversarial networks

KW - Convolution neural network

KW - Data-driven methods

KW - Electricity-consumption

KW - False positive rates

KW - Performance metrics

KW - Synthetic minority over-sampling techniques

KW - Smart power grids

U2 - 10.1109/ACCESS.2021.3056566

DO - 10.1109/ACCESS.2021.3056566

M3 - Journal article

VL - 9

SP - 25036

EP - 25061

JO - IEEE Access

JF - IEEE Access

SN - 2169-3536

ER -