Open Access

REVIEW

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

by Daixuan Zhou¹, Yujin Liu¹, Xu Wang², Fuxing Wang¹, Yan Jia^2,*

1 College of Energy and Power Engineering, Inner Mongolia University of Technology, Hohhot, 010051, China
2 College of Electric Power, Inner Mongolia University of Technology, Hohhot, 010080, China

* Corresponding Author: Yan Jia. Email:

Energy Engineering 2024, 121(12), 3573-3616. https://doi.org/10.32604/ee.2024.055853

Received 08 July 2024; Accepted 12 September 2024; Issue published 22 November 2024

Abstract

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.

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