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Abstract
Solar energy is a promising renewable energy source that can play a crucial role in addressing global energy challenges and mitigating climate change impacts. This research focuses on assessing the impact of climate change on solar energy potential, specifically in regions vulnerable to environmental shifts. The study employs a multi-faceted approach combining data analysis, modeling techniques, and machine learning algorithms to analyze solar radiation data under varying atmospheric conditions. The methodology involves collecting historical climate data, satellite-based solar radiation data, and ground-based measurements to create comprehensive datasets. Clear sky and all-sky solar radiation parameters such as Global Horizontal Irradiance (GHI), Direct Normal Irradiance (DNI), and Diffuse Horizontal Irradiance (DHI) are analyzed using established models and algorithms. Machine learning techniques are utilized to develop predictive models for solar energy forecasting, considering factors like cloud cover variations, aerosol content, and long-term climate trends. The research aims to provide insights into how climate change trends impact solar energy resources, enabling better decision-making for solar energy infrastructure development and energy policy formulation. By understanding the complex interactions between climate dynamics and solar radiation, this study contributes to the advancement of sustainable energy practices and adaptation strategies in a changing climate scenario
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