Wind power generation power prediction method based on multi-scale attention mechanism

Abstract

The invention discloses a wind power generation power prediction method based on a multi-scale attention mechanism. The method comprises the steps of constructing a hybrid recurrent neural network model based on the multi-scale attention mechanism; training the hybrid recurrent neural network model by utilizing training set data, wherein the training set data comprises influence factor data and known wind power generation power data; and inputting test set data into the trained hybrid recurrent neural network model, and calculating to obtain a predicted value of the wind power generation power. According to the invention, multiple factors influencing the wind power generation power are comprehensively considered in multiple aspects, the attention mechanism is introduced into the convolution cycle GRU network, historical meteorological data information is collected for multiple times in different scales for convolution, and the prediction for the wind power generation power obtains a good effect.

Description

technical field [0001] The invention belongs to the technical field of wind power generation, and in particular relates to a wind power generation power prediction method based on a multi-scale attention mechanism. Background technique [0002] With the maturity of wind power generation technology, the capacity of wind power units and the scale of grid-connected wind farms continue to expand, and the proportion of wind power in the total power generation of the power system is also increasing year by year. The penetrating power of wind farms continues to increase, which brings a series of problems to the power system that are increasingly prominent, seriously threatening, and the power system is safe, stable, economical and reliable. Timely and accurate prediction of wind power can significantly enhance the safety, stability, economy and controllability of the power system. In recent years, many scholars have conducted extensive research on wind power forecasting methods. S...

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Problems solved by technology

[0004] In view of this, in order to solve the problem of accurate prediction of wind power generation power, the purpose of the present invention is to provide a wind power generation power prediction method based on a multi-scale attention mechanism, which comprehensively considers multiple factors affecting wind power generation power in various aspects, The attention mechanism is introduced into the convolutional cycle GRU network, and the historical meteorological data information is collected multiple times at different scales for convolution, so that the prediction of wind power generation power has achieved better results

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