Application of Remote Sensing Technologies in Monitoring and Managing Renewable Energy Sources

Abstract

This paper develops a novel hybrid model based on Generative Adversarial Networks (GANs) and Differential Evolution (DE) to enhance remote sensing data and optimize resource assessment models for renewable energy management. GANs were employed to improve the resolution and quality of satellite imagery, addressing the challenges of low-resolution data and incomplete information. Quantitative evaluations, including Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), demonstrated significant improvements in image quality, facilitating more accurate site assessments and predictive modeling. DE was applied to optimize key parameters such as sensor configurations and image enhancement algorithms, leading to enhanced accuracy in resource maps and reduced operational costs. The hybridization of GANs and DE created a comprehensive workflow that allowed for improved decision-making and efficient deployment. The proposed hybrid framework was shown to achieve higher prediction accuracy, exemplified by performance metrics such as Mean Absolute Error and R-squared values. Simulation results on case studies highlighted successful applications in renewable energy projects, emphasizing the potential of this integrated approach to drive cost-effective and scalable solutions.