Design and Development of IoT Prototype for Real-Time Theft Detection and Optimization of Electricity Using Machine Learning Techniques

The pervasive issue of electricity theft poses a substantial challenge to power utilities globally, resulting in significant financial losses and operational inefficiencies. This paper presents the plan and growth of an IoT-based prototype for real-time electricity theft detection and optimization of electricity distribution using advanced machine-learning practices. By integrating smart meters and IoT sensors, the system continuously monitors electricity consumption, providing accurate, real-time data. Utilizing Deep Neural Networks (DNNs), the prototype identifies anomalous usage patterns indicative of theft, ensuring swift and precise detection. Additionally, the structure influences machine-learning procedures to optimize electricity distribution, enhancing overall efficiency and reducing waste. This complete method not only mitigates the risk of theft but also improves the dependability and sustainability of electricity supply. The proposed solution demonstrates important possibilities for enhancing the operational effectiveness of power utilities, offering a scalable, robust, and efficient framework for modern energy management.