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GRENZE International Journal of Engineering and Technology Vol. 11 (2025), Issue 1

Streamlining Electricity Billing: A Smart, QR-Driven Approach with Deep Learning Integration

Authors

K. Indira, Raja Lavanya, C. Santhiya, Sakthi Kiruthika L P, Subanu K J Y

Abstract

Traditional electricity billing processes suffer from manual errors and inefficiencies. The Smart Electricity Billing System (SEBS) aims to address these challenges by utilizing QR code technology for data input and implementing an LSTM deep learning algorithm for accurate consumption prediction. SEBS integrates QR code technology for data input simplification and utilizes a secure payment gateway for transactions. An LSTM deep learning algorithm predicts electricity consumption based on historical data patterns. Developed on the MERN stack, SEBS ensures scalability, flexibility, and security. SEBS reduces manual data entry errors, enhances transaction security, and improves billing accuracy through its innovative features. The system demonstrates its capability to streamline the electricity billing process and offer a user-centric solution.SEBS offers a revolutionary approach to electricity billing, providing a seamless, technologically advanced solution. With its potential to redefine the landscape, SEBS promises to enhance efficiency and user experience in electricity billing processes.