An assessment of solar power generating system as a solution to deal with global warming and climate change

The increasing adoption of solar power as a sustainable energy source necessitates more efficient and reliable methods for optimising and maintaining solar power generating systems. Traditional approaches to assessing and managing these systems often rely on static models and manual interventions, which can be inefficient and fail to account for dynamic environmental conditions. In this study, we propose a novel framework for the assessment and optimisation of solar power systems using modern machine learning techniques. Our approach benifits advanced predictive maintenance, real-time energy yield optimisation, and enhanced energy forecasting models, resulting in significant improvements in system efficiency and reliability. Specifically, the predictive maintenance system, driven by machine learning algorithms, was able to reduce system downtime by 29.88% compared to traditional reactive maintenance methods. The real-time energy yield optimisation, leveraging dynamic data inputs, increased energy capture efficiency by 14.78% over standard static models. Additionally, our enhanced energy forecasting models demonstrated a 25.12% improvement in accuracy, significantly outperforming conventional forecasting techniques. These innovations enhance the operational efficiency of solar power systems, and contribute in their long-term sustainability and economic viability. The integration of machine learning into solar power management enables proactive decision-making, adaptive control strategies, and more accurate performance predictions. As a result, our proposed framework offers a practical and scalable solution to meet the growing demands of the renewable energy sector and supports the global transition toward cleaner and more resilient energy infrastructures.