Wind speed forecasting method and device based on G-L mixed noise characteristic kernel ridge regression technology

Abstract

The invention relates to a wind speed forecasting method and device based on G-L mixed noise characteristic kernel ridge regression technology. The method comprises the following steps: 1) obtaining wind speed data set D1; using the Bayesian principle for the loss function of Gaussian-Laplace mixed noise characteristic; 2) through the use of the theories of statistical learning and optimization and in combination with the loss function obtained in step 1), establishing the original problem of the kernel ridge regression model based on the Gauss-Laplace mixed noise; deducing and solving the dual problem of the kernel ridge regression model; 3) determining the optimal parameters of the dual problem of the kernel ridge regression model; selecting the kernel function; constructing the decision function of the kernel ridge regression model; and 4) constructing the wind speed forecasting model of the kernel ridge regression model; and using this forecasting mode to forecast and analyze the wind speed value. The device of the invention includes a loss function obtaining module, a dual problem solving module, a decision function constructing module and a wind speed forecasting module. The method and invention meet practical application in wind power generation, agricultural production, and etc. which are demanding in terms of wind speed forecasting accuracy.

Description

technical field [0001] The invention relates to the technical field of short-term wind speed forecasting, in particular to a short-term wind speed forecasting method and device based on G-L mixed noise characteristic kernel ridge regression technology. Background technique [0002] For linear systems, since the Gauss era, the least squares technique has been used to fit the points on the plane to a straight line, and to fit the points in the high-dimensional space to a hyperplane. After more than 200 years of development, the classical least squares technique has become the most widely used technique for data processing in many fields. However, for the ill-posed problems in linear regression or nonlinear regression, the performance of least squares regression technology will become very bad. In view of this situation, many scholars have studied the improved model of least squares regression and proposed many new ones. regression algorithm. Ridge regression (RR for short) i...

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Problems solved by technology

[0010] The present invention provides a wind speed forecasting method and device based on G-L mixed noise characteristic kernel ridge regression technology to solve the problem that the existing single noise characteristic kernel ridge regression technology cannot meet the requirements for wind speed forecast accuracy in practical applications

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