Four-stage hybrid short-time wind direction prediction method

Abstract

The invention discloses a four-stage hybrid short-time wind direction prediction method, which belongs to the field of wind direction prediction, and comprises the following steps of: setting samplingtime, collecting wind direction original data, and establishing a wind direction input and output data matrix; using a mutual information method to perform feature selection on the input sequence, removing feature vectors with low relevancy, keeping feature vectors with high relevancy, and determining the dimension L of an input matrix; performing K-order decomposition on the L-dimensional feature sequence by adopting a variational mode decomposition (VMD) method, and mining feature information of wind direction input; constructing a wind direction deep learning prediction model by using a DBN network, and inputting the decomposed K * L-dimensional wind direction input sub-sequence to obtain a wind direction prediction value; correcting the prediction error of the DBN wind direction prediction model through the BP neural network, and improving the wind direction prediction precision; and finally, verifying the precision of the corrected wind direction prediction model. The method is of great significance to wind direction prediction in high-precision and complex regions.

Description

technical field [0001] The invention relates to the technical field of wind direction prediction, in particular to a four-stage mixed short-term wind direction prediction method. Background technique [0002] With the rapid growth of new energy, wind power as a green energy has developed rapidly in the past century. With the continuous growth of wind power access, the impact of intermittent and random fluctuations of wind power generation on the power grid is becoming more and more obvious. The instability, intermittent and randomness of wind direction increase the difficulty of wind direction modeling and prediction. Accurate wind direction prediction can improve the power generation efficiency of wind turbines, increase wind power generation capacity and profits, and provide the necessary basis for grid scheduling and unit combination operations. [0003] The main methods of wind direction prediction at this stage include classical statistical methods and data-driven met...

AI Technical Summary

Problems solved by technology

The accuracy of these models is very dependent on the artificial setting of parameters, such as the number of layers and nodes of the feed-forward neural network, the kernel function and penalty factor of the support vector machine, etc. Improper parameter setting may lead to insufficient accuracy or overfitting

Therefore, the data-driven approach requires a large number of repeated experiments to determine the data parameter settings

Images