Research Progress of Photovoltaic Power Prediction Technology Based on Artificial Intelligence Methods

• Published in: Energy engineering Vol. 121; no. 12; pp. 3573 - 3616
• Main Authors: Zhou, Daixuan, Liu, Yujin, Wang, Xu, Wang, Fuxing, Jia, Yan
• Format: Journal Article
• Language: English
• Published: Atlanta Tech Science Press 01.01.2024
• Subjects: Algorithms; Alternative energy sources; Artificial intelligence; Efficiency; Electricity; Electricity distribution; Emissions; Energy resources; Growth rate; Load; Machine learning; Photovoltaic cells; Renewable resources; Solar energy; Solar power generation; Statistical methods; Supply & demand; Systems stability
• ISSN: 1546-0118; 0199-8595; 1546-0118
• DOI: 10.32604/ee.2024.055853

With the increasing proportion of renewable energy in China’s energy structure, among which photovoltaic power generation is also developing rapidly. As the photovoltaic (PV) power output is highly unstable and subject to a variety of factors, it brings great challenges to the stable operation and dispatch of the power grid. Therefore, accurate short-term PV power prediction is of great significance to ensure the safe grid connection of PV energy. Currently, the short-term prediction of PV power has received extensive attention and research, but the accuracy and precision of the prediction have to be further improved. Therefore, this paper reviews the PV power prediction methods from five aspects: influencing factors, evaluation indexes, prediction status, difficulties and future trends. Then summarizes the current difficulties in prediction based on an in-depth analysis of the current research status of physical methods based on the classification of model features, statistical methods, artificial intelligence methods, and combined methods of prediction. Finally, the development trend of PV power generation prediction technology and possible future research directions are envisioned.