A Survey of Artificial Intelligence Integrating Image Processing Techniques for Predicting Solar Energy Production: Challenges and Innovations

Solar energy production prediction is an essential prerequisite for the successful implementation of renewable energy sources and the elaboration of sustainable strategies. The paper provides a detailed overview of artificial intelligence methods used to predict the performance of solar energy facilities. It examines traditional prediction methods, such as meteorological reports, and their disadvantages, such as numerous factors' influence on the report, changes on the current data availability, and atmospheric conditions. This probes the exploitation of satellite pictures, earth-based cameras, and drone monitoring, depicting how these graphic evidentiary sources are handled using sophisticated machine gaining formulas for example Convolutional Neural Systems and Creative Opposing Networks. The potential of AI-based prediction models that apply machine learning and neural networks to analyze numerous data and identify complicated dependencies is underlined. As conclusive remarks, we have reviewed various techniques and frameworks so far, organized AI algorithms based on their respective advantages and limitations, and compared their performance by presenting examples. Furthermore, we have indicated future work directions, particularly the incorporation of advanced data sources, enhanced model interpretability, scalability optimization, adaptive learning algorithm development, multidisciplinary cooperation, strengthened validation processes, and widened spread in practice. By combining these findings in a comprehensive manner, we aspire to propel knowledge on AI-driven solar energy prediction and inspire increased reliance on it for a cleaner world.