Application of Artificial Intelligence in Solar Power Generation Forecasting

With the world going through the energy paradigm shift to clean and low-carbon energy sources, the development of solar power, as one of the cornerstone renewable energy sources, shows grid stability and energy optimization challenges that involve the intermittency and unpredictability of solar power output. The specifics of solar power generation prediction have turned out to be the key to the solution of this problem. This paper will discuss the use of artificial intelligence in solar power generation prediction, covering the core technologies and the viable solutions in the given field. The studies involve deep learning applications, machine learning, and hybrid model applications. The problems of the current research include the lack of data quality and standardization, the challenge of balancing model complexity and real-time performance, and the lack of interpretability. It is desirable to advance in the future in the direction of multimodal data fusion and such directions. This paper represents a systematic review of the implementation results of artificial intelligence technology in solar energy forecasting, and it has tremendous implications in the optimization of energy distribution and the stability of the grid.