Ultra-short-term wind power prediction method based on FTI-VACA-XGB model

In order to predict wind power quickly and accurately and reduce the negative impact of wind power instability on the grid, this study proposes an ultra-short-term wind power prediction model based on financial technical indicators and parameter optimization algorithms. Firstly, historical wind power time series data calculates the financial technical indicators. Secondly, the Monte Carlo method and rank ant colony algorithm are used to optimize the parameters of financial technical indicators calculation. Finally, the future wind power is predicted based on the XGBoost algorithm combining financial technical indicators with historical power. The proposed model is validated on several wind power datasets from different countries and compared with various comparative models, leading to several important conclusions: (1) Fintech indicators can effectively indicate the intrinsic characteristics of wind power time-series data. (2) The variational ant colony algorithm can make the financial technical indicators better fit the wind power time-series data trends. (3) The proposed model has high prediction accuracy and speed and has similar prediction accuracy to the mainstream deep learning models. (4) The proposed model is not limited by meteorological, geographical, and seasonal factors and can make predictions by relying only on historical wind power data.