Smart Detection of Energy Theft in Agricultural Sectors - Ashwini R,; Kaviga D,; Rohitha M,; Sivaranjani G,; Guru Deepthi V, (International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2025)
The project revolves around the comprehensive management of energy resources through real-time monitoring, theft detection, and demand prediction. It employs AC current and voltage sensors to monitor energy consumption, with data being timestamped and compiled into a dataset. The future energy demand is forecasted using Long Short-Term Memory (LSTM) neural networks, allowing for better resource planning. Additionally, theft detection is carried out through a Support Vector Machine (SVM) algorithm trained on historical data. The gathered information is securely uploaded to the ThingSpeak cloud platform, ensuring data integrity and accessibility. To enhance user engagement and control, a web application is developed for visualizing the energy usage, demand forecasts, and theft alerts. This project addresses the pressing need for efficient energy management, enabling users to make informed decisions and combat energy theft while contributing to sustainability and resource conservation.
Keywords: Energy Theft Detection; Long Short-Term Memory (LSTM); Support Vector Machine (SVM); ThingSpeak Cloud; Machine Learning