Implementing machine learning to characterize solar cells and optimize their efficiency

Artificial intelligence (AI) has evolved into an indispensable component across various sectors, with a particular emphasis on employing machine learning techniques. This integration of AI technologies holds significant importance, especially in the realm of renewable energy, given its positive ecological impact and its role as a sustainable energy source to fulfill our energy needs. The field of solar energy is undergoing continual research and development, with a dedicated focus on applying various machine learning techniques to optimize energy production. One such area of investigation pertains to Maximum Power Point Tracking (MPPT). In this study, we have implemented a machine learning algorithm, specifically a regression tree, using a novel dataset derived from experimental measurements. Our approach yielded a commendable accuracy score of 96%. Our primary objective revolves around the meticulous construction of a database through precise experimental measurements, which serves as the foundational cornerstone of the machine learning process. This dataset is then utilized to develop and train a machine learning model that can predict values pertinent to our research. Finally, we deploy this model within a dedicated platform to facilitate the attainment of the Maximum Power Point, a crucial component in the optimization of solar energy systems.