Enhancing solar power generation through AC power prediction optimization in solar plants

Abstract

As the world embraces sustainable energy solutions, the accurate prediction of AC power generation in solar power plants becomes imperative for efficient energy management. This research endeavors to address this critical need through a meticulous exploration of five distinctive predictive algorithms: linear regression, gradient boosting, neural networks, support vector regression (SVR), and ensemble techniques. Leveraging a merged dataset comprising environmental parameters like ambient and module temperatures, irradiation, and historical yield, our study embarks on a comprehensive evaluation journey. The essence of this endeavor lies in the recognition that renewable energy sources, particularly solar power, are instrumental in mitigating environmental concerns associated with traditional energy generation. To unleash the full potential of solar power, a nuanced understanding of predictive methodologies is indispensable. Linear regression serves as a cornerstone, validating its foundational role. However, the crux of innovation lies in the advanced algorithms – gradient boosting, neural networks, SVR, and ensemble methods – each striving to optimize prediction accuracy. A novelty of this research stems from its holistic approach to predictive modelling. By meticulously comparing the performance of multiple algorithms, we uncover insights that transcend mere theoretical applications. Our findings assume significance in the context of renewable energy's societal impact.