A Comprehensive Study on Electricity Theft Detection Using Data Analysis

Electricity theft is the crux of all problems in the power sector. The world has been facing this issue and wants to upgrade the system to overcome this problem. Directly or indirectly, it has been causing enormous financial losses worldwide. In the present scenario, a lot of advancements are made in information technology. Therefore, electrical communication networks can be adopted artificial intelligence methods to increase oversight of each and every blind spot. However, the electrical network system still uses an antiquated manner of theft detection. Nevertheless, private utilities have worked to gather information and examine various elements of electricity consumption. Therefore, the utilities have collected the data in real time. Further, data mining and sample arrangement have been addressed with some neural networking approaches such as fuzzy networking, recurrent neural network (RNN), Fourier Convolution Neural Network FCNN, and time-series recurrent neural network TSRNN. Therefore, this paper has illustrated the shortcomings of the fundamental method and elaborated to advanced and fast algorithms escorted by intelligent decision-making networks to control the electricity theft.