Deep learning model-transformer based wind power forecasting approach
Sheng Huang, Yan Chang, Yinpeng Qu
Abstract
The uncertainty and fluctuation are the major challenges casted by the large penetration of wind power (WP). As one of the most important solutions for tackling these issues, accurate forecasting is able to enhance the wind energy consumption and improve the penetration rate of WP. In this paper, we propose a deep learning model-transformer based wind power forecasting (WPF) model. The transformer is a neural network architecture based on the attention mechanism, which is clearly different from other deep learning models such as CNN or RNN. The basic unit of the transformer network consists of residual structure, self-attention mechanism and feedforward network. The overall multilayer encoder to decoder structure enables the network to complete modeling of sequential data. By comparing the forecasting results with other four deep learning models, such as LSTM, the accuracy and efficiency of transformer have been validated. Furthermore, the migration learning experiments show that transformer can also provide good migration performance.