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Alexnet-Adaboost-ABC Based Hybrid Neural Network for Electricity Theft Detection in Smart Grids

Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 278)

Abstract

In this paper, a hybrid deep learning model is presented to detect electricity theft in the power grids, which happens due to the Non-Technical Losses (NTLs). The NTLs emerge due to meter malfunctioning, meter bypassing, meter tampering, etc. The main focus of this study is to detect the NTLs. However, the detection of NTLs faces three major challenges: the problem of severe class imbalance, the problem of overfitting due to the highly dynamic data and poor generalization due to the usage of synthetic data. To overcome the aforementioned problems, a hybrid deep neural network is designed, which is the combination of Alexnet, Adaptive Boosting (AdaBoost) and Ant Bee Colony (ABC), termed as Alexnet-Adaboost-ABC. The Alexnet is exploited for the features’ extraction while Adaboost and ABC are used for the classification and parameters’ tuning, respectively. Moreover, the class imbalance issue is resolved using the Near Miss (NM) undersampling technique. The NM effectively reduces the majority class samples and standardize the proportion of both majority and minority classes. The model is evaluated on the real time inspected dataset released by the State Grid Corporation of China (SGCC). The performance of the proposed model is validated through the F1-score, precision, recall, Area Under Curve (AUC) and Matthew Correlation Coefficient (MCC). The simulation results depict that the proposed model outperform the existing techniques. The simulation results depict that the proposed model obtains 3%, 2% and 4% higher values of F1-score, AUC and MCC, respectively.

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Asif, M. et al. (2021). Alexnet-Adaboost-ABC Based Hybrid Neural Network for Electricity Theft Detection in Smart Grids. In: Barolli, L., Yim, K., Enokido, T. (eds) Complex, Intelligent and Software Intensive Systems. CISIS 2021. Lecture Notes in Networks and Systems, vol 278. Springer, Cham. https://doi.org/10.1007/978-3-030-79725-6_24

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