Artificial intelligence for energy fraud detection: a review

Abstract

Energy fraud in the distribution sector of electric utility includes electricity theft, meter tampering, or billing error. This fraud causing non-technical loss has led to an economic loss of the company. In order to detect and minimize fraud, different technologies have been used. From conventional methods to development in the field of artificial intelligence (AI), effective and reliable fraud detection methods have been proposed. This paper first provides an overview of different proposed methods for non-technical loss detection and evaluate the advantage and limitation of using those methods. Furthermore, several supervised and unsupervised machine learning methods for detecting electricity theft are discussed in summary along with their metrics and attributes used. Finally, these methods are classified based on the overall operation and the parameters used. This paper provides comparisons of several fraud detection methods using AI along with their weak and strong points and this information is very useful for the researchers who are working in the field of AI method for detecting fraud.

Construction and Application of VS-DBN Anti-Theft Diagnosis Model Based on Neural Architecture Search

“…Scholars such as Poudel S have proposed a machine learning methods for theft information detection, while classifying theft users. This anti-theft method has achieved good practical application results [17]. In summary, domestic and foreign researchers have conducted much research on ET detection and achieved corresponding results.…”

“…17), AA stands for the stolen electricity of lowvoltage users. BB stands for the stealing power of the user's meter.…”

A novel and effective method based deep learning model for detecting non-technical electricity losses

“…Because of the technological and financial consequences, energy providers are devoting greater resources to minimizing NTL losses. All consumers, the general public, and society as a whole are negatively impacted by electricity theft and other forms of energy fraud [16], [21]- [23]. The research community has been working hard over the past decade to reduce the number of NTL incidents in the electricity sector.…”