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

Authors

• Hassan Ghaedi ¹
• Seyed Reza Kamel Tabbakh ²
• Reza Ghaemi ³

1. Department of Computer, Neyshabur Branch, Islamic Azad University, Neyshabur, Iran
2. Department of Computer, Mashhad Branch, Islamic Azad University, Mashhad, Iran
3. Department of Computer, Quchan Branch, Islamic Azad University, Quchan, Iran

Abstract

Today, electricity theft is one of the main challenges for energy distribution and transmission companies around the world. Early detection of abnormal consumers can prevent security and financial losses. Extensive research studies have been done to detect electricity theft by analyzing customer consumption patterns. Today, one of the most widely used methods is convolutional neural networks (CNNs). These networks contain a large number of hyper-parameters.  The accuracy of these networks is low in most studies due to the lack of attention to the adjustment of these hyper-parameters.  Network accuracy and achieving a robust learning model are influenced by the optimal adjusting of these hyper-parameters, which requires exploring a complex and large search space. Meta-heuristic-based search methods are suitable for solving these problems. Therefore, the main contribution of this paper is to use the high ability of the cheetah optimization algorithm (CHOA) to optimally extract CNN hyper-parameters. In this paper, in order to balance the dataset, abnormal samples are created using artificial attacks and added to the dataset. Also, in order to increase the accuracy of the network, abnormal data are clustered using the CHOA algorithm. ISSDA dataset is used to test and evaluate the results. Based on the results obtained and comparing them with the other works, it was proved that the proposed framework with high accuracy identifies abnormal consumers.

Keywords

• convolutional neural network (CNN)
• Prediction
• PV panel
• Solar output
• GUI
• ANN
• Data mining
• Classification
• Electricity Theft Detection