ISI Journals

Renewable energy systems are pivotal for sustainable development, and optimizing their efficiency remains a critical challenge. This study explores the application of Machine Learning (ML) and Artificial Intelligence (AI) in Data Science to enhance the performance of solar cells, a key technology in renewable energy. By analyzing large datasets of solar cell performance metrics and environmental factors, we develop predictive models that optimize energy output. This research highlights the transformative potential of integrating AI and ML in renewable energy, emphasizing their role in improving solar cell efficiency and contributing to a greener future. Machine Learning (ML) and Artificial Intelligence (AI) are revolutionizing renewable energy systems by providing innovative solutions for optimizing efficiency and performance. This study explores the integration of ML and AI within Data Science to enhance the functionality of solar cells, a crucial component of renewable energy technologies. By analyzing extensive datasets, including environmental conditions, material properties, and historical energy outputs, predictive models were developed to maximize energy production and system reliability. The findings demonstrate how AI-driven methodologies can optimize solar cell configurations, reduce operational costs, and contribute to a more sustainable energy future. This research underscores the transformative potential of AI and ML in advancing renewable energy technologies and accelerating the transition toward greener energy systems.