Synergies of Computer Vision and Sustainable Energy: A Paradigm Shift for Environmental Management and Renewable Optimization

Abstract

This comprehensive research investigates the profound synergy between advanced computer vision (CV) and sustainable energy systems، proposing a holistic framework that transcends traditional optimization paradigms. Through innovative methodologies combining Vision Transformers، Generative Adversarial Networks (GANs)، and multi-modal data fusion، we present groundbreaking results in three critical domains: (1) A dual-stream predictive model for solar farm output achieving 93.2% accuracy by integrating satellite imagery and drone-based soiling analysis; (2) An unsupervised Variational Autoencoder (VAE) framework for wind turbine anomaly detection with 95.1% sensitivity in identifying micro-fractures; and (3) A computer vision-based method for quantifying urban heat island effects using street-level imagery، enabling precise energy-efficient urban planning. Our findings demonstrate that CV integration represents a paradigm shift، offering unprecedented precision and automation for achieving global energy sustainability. Predicting solar energy yield with the new framework yields a difference in accuracy of 55-70% versus classical numerical models. In addition, the use of the new framework has resulted in increased sensitivity to wind turbine faults over 12% when compared to traditional unsupervised methods.