• Vemuri Vasanth Kumar
• Mrs. A.Sirisha

Electricity theft is a pervasive issue that impacts both utility providers globally and individual electricity users. This phenomenon disrupts the economic growth of utility corporations, leading to disturbances in the power grid and resulting in increased energy costs for users. The implementation of smart grids plays a crucial role in addressing electricity theft by generating large datasets that include consumer consumption information. This data can be leveraged for effective electricity theft detection using machine learning algorithms. In this project, an artificial neural network is employed to analyze features acquired from consumer data to determine potential instances of electricity theft. The use of deep learning techniques, such as Deep Artificial Neural Network, along with evaluation metrics like accuracy score, f1 score, precision score, and recall score, enhances the efficiency and accuracy of the detection model. Real-world experiments based on actual energy consumption data are conducted to validate the proposed approach. The results of the experiments demonstrate that the suggested detection model outperforms existing methods in terms of precision and efficiency. By utilizing deep learning and advanced evaluation metrics, this project presents an efficient solution for the recovery of revenue losses in electric utilities caused by electricity theft. The application of these techniques contributes to the overall improvement of electricity grid security and economic stability for utility providers.

Electricity Theft, Deep Artificial Neural Network, Deep Learning, Power Grid.

"Electricity Theft Detection in Smart Grids Based on Artificial Neural Network", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 2, page no.e308-e311, February-2024, Available :http://www.jetir.org/papers/JETIR2402444.pdf

"Electricity Theft Detection in Smart Grids Based on Artificial Neural Network", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 2, page no. ppe308-e311, February-2024, Available at : http://www.jetir.org/papers/JETIR2402444.pdf