Summary of The Power Prediction Studies of Photovoltaic Systems

This paper summarizes the research status and technical progress of photovoltaic system power prediction. With the acceleration of the global energy transition, solar energy, as a clean and sustainable energy source, has attracted wide attention in its utilization efficiency and stability. Power prediction of photovoltaic system is of great significance for optimizing power grid dispatching, reducing energy waste and improving system reliability. This paper classifies the photovoltaic power prediction from four aspects of prediction process, prediction form, prediction space and prediction time range, and analyzes in detail the application of current artificial intelligence technology in photovoltaic power prediction, including machine learning, deep learning, digital twinning and combined algorithm. through comparative analysis, evaluates the strengths and limitations of various photovoltaic power prediction methods, summarizes their merits and demerits, explores the future development trends in this technology, identifies key directions for advancement, and proposes an innovative scheme to enhance prediction accuracy and reliability, contributing to the sustainable development of photovoltaic power systems. The results show that the hybrid model combined with a variety of technologies has significant advantages in improving the prediction accuracy and adaptability, while emerging technologies such as digital twin technology and transfer learning provide a new idea for solving to the problem of data shortage of new photovoltaic power stations.