Integrating VMD and Convolutional Transformers for Optimized Solar Energy Management in IoT Monitoring Systems

The increasing demand for sustainable and real-time environmental monitoring has highlighted the need for efficient energy utilization in solar-powered systems. This research introduces a convolutional transformer-based predictive model for solar energy forecasting, representing a significant advancement in leveraging machine learning for Internet of Things (IoT) and environmental monitoring applications. Unlike prior approaches that primarily relied on conventional methods or basic predictive models, this work integrates advanced techniques such as Variational Mode Decomposition (VMD) to preprocess data and enhance forecasting accuracy. These improvements directly enhance the entire operational framework, enabling not only optimized energy utilization but also reduced system downtime and overutilization. By focusing on forecasting, this research contribution advances the state-of-the-art in predictive modeling for energy management in autonomous monitoring systems. Accurate solar energy forecasts empower dynamic adjustments in system parameters, ensuring both operational efficiency and sustainability. Comparative analysis demonstrates the substantial gains achieved by the proposed approach, highlighting its potential to transform the feasibility and performance of autonomous environmental monitoring systems. Additionally, this work integrates advanced predictive models into solar-powered frameworks, contributing to sustainable and efficient environmental monitoring practices.