Hybrid Energy Harvesting in IoT: A Deep Learning-Based Approach to Solar Power Management renewable energy

The integration of hybrid energy harvesting (HEH) in the Internet of Things (IoT) is crucial for achieving sustainable and autonomous operations. Solar energy, as a primary renewable source, requires efficient management to address fluctuations in power generation. This study presents a deep learning-based approach to optimize solar power utilization in IoT systems. A predictive model is developed using historical solar data, environmental parameters, and energy consumption patterns to enhance power allocation and storage efficiency. The proposed framework employs advanced neural networks to forecast energy availability and dynamically adjust resource distribution. Simulation results demonstrate improved energy efficiency, reduced power wastage, and extended device lifespan. The approach also ensures continuous IoT functionality by intelligently switching between solar and auxiliary energy sources. Compared to conventional methods, the deep learning-based strategy offers superior adaptability to changing environmental conditions. The findings highlight the potential of artificial intelligence in optimizing hybrid energy harvesting for next-generation IoT applications.