HNS-RE2SD

Higher National School of Renewable Energies, Environment and Sustainable Development

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AIoT Based Smart Energy Monitoring System
(2024-06-15) BOUTELIS Moussaab; ABDERRAHMANI Issam
This project develops an AI-driven IoT-based Smart Energy Monitoring System to enhance energy efficiency in residential areas. Utilizing ESP32-CAM devices, it captures real-time images of utility meters and employs AI techniques like Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to analyze and forecast energy consumption patterns. The system aims to help homeowners optimize resource use, minimize waste, and reduce expenses by providing immediate data insights and predictive analytics. It also improves utility companies’ data accuracy and customer trust through transparent reporting. The user-friendly web application is built on the Laravel framework, facilitating interactive data visualization. This initiative not only pushes technological boundaries but also promotes resource efficiency and scalability. Looking ahead, the project plans to integrate advanced machine learning algorithms, expand IoT capabilities for a fully connected home, and bolster security features, all contributing significantly to global sustainability objectives.