A wind speed forecasting method based on g-l mixed noise characteristics v-support vector regression machine

Abstract

The present invention relates to the characteristic of mixed noise based on G-L v ‑Support vector regression machine wind speed forecasting method, the method includes the following steps: 1) Acquire wind speed data set D l , using the Bayesian principle to obtain the empirical risk loss function of Gauss-Laplace mixed noise characteristics; 2) Using statistical learning theory and convex optimization technology, combined with the loss function in step 1), to establish a Gauss-Laplace mixed noise characteristic based on v ‑The original problem of the support vector regression model, using the Lagrange multiplier method to derive and solve the v ‑Support Vector Regression Model Dual Problem; 3) Determine the v ‑Support vector regression model optimal parameters for the dual problem, select the kernel function, and construct the v ‑Decision function of support vector regression model; 4) Construct the v ‑Support vector regression model for wind speed forecasting model, using this forecasting model to predict and analyze wind speed values. The method includes an empirical risk loss function acquisition module, a dual problem solving module, a decision function construction module and a wind speed forecasting module. The invention can meet the requirements of wind speed forecast accuracy in practical applications, such as wind power generation, agricultural production and the like.

Description

technical field [0001] The invention relates to the technical field of wind speed forecasting, in particular to a wind speed forecasting method based on G-L mixed noise characteristics v-support vector regression machine. Background technique [0002] For linear systems, since the Gauss era, the least squares method has been used to fit the points on the plane to a straight line, and to fit the points in a high-dimensional space to a hyperplane. After nearly 200 years of development, the classical least squares method has become the most widely used method for data processing in many fields. However, for ill-posed problems in linear regression or problems in nonlinear regression, the performance of linear regression based on the least squares method may become very bad. In view of this situation, many scholars have studied the improvement of least squares regression. Many new regression algorithms have been proposed. Support vector regression (SVR for short) is one of them...

AI Technical Summary

Problems solved by technology

[0018] The present invention provides a kind of wind speed prediction method based on G-L mixed noise characteristic v-support vector regression machine, to solve the problem that the support vector regression technology of existing single noise characteristic can not satisfy the requirement of wind speed prediction accuracy in practical application

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