Application of three Transformer neural networks for short-term photovoltaic power prediction: A case study
Jiahao Wu, Yongkai Zhao, Ruihan Zhang, Xin Li, Yuxin Wu
Abstract
In order to solve the potential safety hazards caused by the fluctuation of photovoltaic (PV) power generation, it is necessary to predict it in advance and take countermeasures as soon as possible. Based on the three models of vanilla Transformer, Informer, and Autoformer, this paper considers three prediction scenarios: zero-cost prediction, low-cost prediction, and high-cost prediction, and realizes the power prediction under two prediction horizons of 4 h and 24 h for a matrix of a centralized PV power station in Hubei Province, China. The results of some configurations meet the industry-recommended metric requirements, and the overall performance of the vanilla Transformer is better than Informer and Autoformer. After comparing the three models and different prediction intervals, and considering the practical industrial demand, this paper gives recommended configurations for both 4 h and 24 h predictions. The practical rolling prediction performance of the recommended configurations demonstrates the applicability and flexibility of the proposed methods.