Electricity Theft Detection in Power Consumption using Superiority of Machine Learning Algorithm

Electricity Theft Detection in Power Consumption using Superiority of Machine Learning Algorithm

Prof. Smita Khot¹, Sudha Dhore², Sejal Thorat³, Priyanka Musmade⁴, Varsharani Sargar⁵

^1,2,3,4,5 Department of Computer Engineering

^1,2,3,4,5 Dr. D.Y. Patil Institute of Technology, Pune, Maharashtra, India

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Abstract - Electricity theft is the act of stealing electricity by deceptive techniques. Electricity theft represents a large chunk of NTL (Non- Technical Loss). These are the losses caused by misidentified, mis-allocated, or incorrect energy flows. Electricity theft is a major issue in India, as it is in most of the developing countries. Although the theft can be detected using machine learning techniques. In this study, the system is proposed that reads smart meter data (input) using OCR (Optimized Character Recognition). OCR is a technique for recognizing text characters in digital images that have been printed or handwritten. The smart meter data image which consists of electricity usage units is converted into machine-readable text by OCR. Furthermore, the SARIMAX (Seasonal Auto Regressive Integrated Moving Average with exogenous factors) algorithm is utilized to monitor customers electricity consumption and detect electricity theft. If theft is identified, an alert message and details of the theft are sent to an electrical board worker. The worker then manually verifies/checks and updates the status. If no theft is discovered, a bill is generated.

Key Words:  Electricity Theft, Machine Learning, Optical Character Recognition, SARIMAX