Wind power generation power determining method and device

Abstract

The embodiments of the invention provide a wind power generation power determining method and device. The method includes the following steps that: a Gauss regression training model is constructed, and the hyper-parameters of the Gauss regression training model are determined; the hyper-parameters of the Gauss regression training model are screened by adopting a memetic algorithm, and screened-out hyper-parameters are utilized to construct a new Gauss regression training regression model; the new Gauss regression training regression model is utilized to perform computation processing on a first sample data set, so that wind speed data at a wind power generation power prediction time point is obtained; and the new Gauss regression training regression model is utilized to perform computation on the wind speed data at the wind power generation power prediction time point and a second sample data set, so as to obtain predictive wind electricity power. According to the method provided by the technical schemes of the invention, the memetic algorithm is adopt to screen the hyper-parameters of the Gauss regression training model; the screened-out hyper-parameters are utilized to construct the new Gauss regression training regression model; the new Gauss regression training regression model is utilized to predict the wind electricity power; and therefore, the efficiency of the prediction of the wind electricity power can be improved, and the accuracy of prediction results can be improved.

Description

technical field [0001] The invention relates to the technical field of wind power generation, in particular to a method and device for determining wind power generation power. Background technique [0002] Wind farms need to participate in market bidding for grid-connected power generation, so the dependence on and demand for wind power forecasting is increasing. Predicting the output power of wind farms is a very useful method to alleviate the pressure of frequency regulation and peak regulation of the power system and improve the ability to accept wind power. In addition, forecasting the power of wind power generation can provide an important reference for planning the maintenance of wind farms, thereby improving the utilization rate of wind energy and the economic benefits of wind farms. After years of exploration, research and technological innovation, the wind power forecasting system with my country's independent intellectual property rights has been widely used in th...

AI Technical Summary

Problems solved by technology

In the process of applying Gaussian process to predict wind power generation power, since the hyperparameters in Gaussian process are obtained by the conjugate gradient method, it takes a long calculation time and the accuracy is not high to find hyperparameters, which makes the application Gaussian process wind power prediction efficiency is low, and the accuracy of prediction results is low

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