Improving Performance of the Convolutional Neural Networks for Electricity Theft Detection by Using Cheetah Optimization Algorithm

  • hassan ghaedi Department of Computer, Neyshabur Branch, Islamic Azad University, Neyshabur, Iran
  • Seyed Reza Kamel Tabbakh Department of Computer, Mashhad Branch, Islamic Azad University, Mashhad, Iran
  • reza ghaemi Department of Computer, Quchan Branch, Islamic Azad University, Quchan, Iran
Keywords: Data mining, Classification, Electricity Theft Detection, Convolutional Neural Network(CNN)


This paper presents an efficient approach to improve convolutional neural network(CNN) performance using cheetah optimization algorithm (CHOA). The main challenges of these networks are optimizing the parameters and finding an efficient architecture. Network performance and achieving efficient learning models based on a particular problem, depend on adjusting values of the hyper-parameters and requiring exploring in a complex and large search space. In order to solve these types of problems, heuristic-based searches are used. Therefore, the main idea of this paper is using CHOA algorithm to adjust the optimal hyper-parameters of CNN networks including number of convolutional and pooling layers, number of filters per convolutional layer and their size, stride of each filter, pool size and stride of each pooling layer and Batch size. This paper presents an optimal approach to increase the detection rate of CNN network that abnormal samples are generated and clustered by artificial attacks and CHOA algorithm, respectively.  The resulting architecture is evaluated on the ISSDA dataset. Based on the obtained results, the proposed method with high detection rate identifies unauthorized electricity customers.


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How to Cite
ghaedi, hassan, Kamel Tabbakh, S. R., & ghaemi, reza. (2022). Improving Performance of the Convolutional Neural Networks for Electricity Theft Detection by Using Cheetah Optimization Algorithm. Majlesi Journal of Electrical Engineering. Retrieved from