Integrating AI and IoT in Advanced Optical Systems for Sustainable Energy and Environment Monitoring

The increasing demand for sustainable energy solutions and environmental monitoring necessitates advanced technologies. This work combines the capabilities of AI, in the form of a GRU-Auto encoder, with IoT-connected Advanced Optical Systems to create a comprehensive monitoring system. Current monitoring systems often face limitations in real-time analysis and adaptability. Conventional methods struggle to provide timely insights for sustainable energy and environmental management due to the complexity of data patterns and the lack of dynamic adaptability. Our proposed methodology introduces an optimized GRU-Auto encoder, which excels in learning complex temporal patterns, making it well-suited for dynamic environmental and energy data. The integration with Advanced Optical Systems ensures a continuous influx of high-quality real-time data through IoT, enabling more accurate and adaptive analysis. The study involves optimizing the GRU-Auto encoder through hyper parameter tuning and gradient clipping. The model is integrated into an IoT platform that connects with Advanced Optical Systems for seamless data flow. Real-time data from environmental and energy sensors are processed through the AI model, providing immediate insights. Performance is evaluated based on the system's ability to accurately predict environmental trends, optimize energy consumption, and adapt to dynamic changes. Comparative analyses with traditional methods show advantages of the suggested strategy in terms of efficiency and accuracy. This research presents a significant development in the field of study of sustainable energy and environment monitoring, offering a robust solution for real-time data analysis and adaptive decision-making. The integration of an optimized GRU-Auto encoder with IoT-connected Advanced Optical Systems showcases promising results in improving overall system performance and sustainability.