Ultra-short-term wind power prediction method combining financial technology feature engineering and XGBoost algorithm
Shijie Guan, Yongsheng Wang, Limin Liu, Jing Gao, Zhiwei Xu, Sijia Kan
Abstract
The input features of existing wind power time-series data prediction models are difficult to indicate the potential relationships between data, and the prediction methods are based on deep learning, which makes the convergence of the models slow and difficult to be applied to the actual production environment. To solve the above problems, an ultra-short-term wind power prediction model based on the XGBoost algorithm combined with financial technical index feature engineering and variational ant colony algorithm is proposed. The model innovatively applies financial technical indicators from financial time series data to wind power time series data and creates a class of model input features that can highly condense the potential relationships between time series data. A bionic algorithm is used to search for the best computational parameters for financial technical indicators to reduce the reliance on financial experts' experience. Taking the German power company Tennet wind power data set as an example, the prediction model proposed in this study has an mean absolute error of 0.859 and a root mean square error of 1.329, and it takes only 244 ms to complete the prediction. Thus, this study provides a new solution for ultra-short-term wind power prediction.