Artificial intelligence for energy fraud detection: a review

Sushmita Poudel, Udaya Raj Dhungana


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.

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International Journal of Applied Power Engineering (IJAPE)
p-ISSN 2252-8792, e-ISSN 2722-2624

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