Real-time wind power prediction based on WD-GA-SVM combined model

Abstract

In recent years, the development of the wind power industry has been particularly rapid. The research of wind power forecasting technology is of great significance for ensuring the stability of the entire power system and improving the competitiveness of wind power in the power industry. In this paper, the wavelet denoising WD algorithm has good applicability in signal denoising, and a wind power prediction model based on WD-GA-SVM is established for a single wind turbine. Firstly, preprocess the related data to get the initial historical sequence, and then use the wavelet denoising method to reduce the noise of the historical data, and the revised historical data is closer to the actual data. Finally, the automatic optimization feature of the GA algorithm is used to complete the parameter optimization of the support vector machine, and the SVM model is optimized. The research results show that the method described in this paper effectively improves the accuracy of the real-time wind power prediction model.