Wind Speed ​​Forecasting Method and Device Based on Kernel Ridge Regression Technology of G-L Mixed Noise Characteristics

Abstract

The present invention relates to the wind speed prediction method and device based on G-L mixed noise characteristic nuclear ridge regression technology, the method comprises the following steps: 1) obtain wind speed data set D1, utilize Bayesian principle, obtain the loss function of Gauss-Laplace mixed noise characteristic; 2) Utilize statistical learning theory and optimization theory, combined with the loss function in step 1), establish the original problem of the kernel ridge regression model based on Gauss-Laplace mixed noise characteristics, derive and solve the dual problem of the kernel ridge regression model; 3 ) Determine the optimal parameters of the dual problem of the kernel ridge regression model, select the kernel function, and construct the decision function of the kernel ridge regression model; 4) Construct the wind speed forecast model of the kernel ridge regression model, and use the forecast model to forecast and analyze the wind speed value. The device includes a loss function acquisition module, a dual problem solving module, a decision function construction module and a wind speed forecast 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 short-term wind speed forecasting, in particular to a short-term wind speed forecasting method and device based on the G-L mixed noise characteristic nuclear ridge regression technology. Background technique [0002] For linear systems, since the Gauss era, the least squares technique has been used to fit points on a plane to a straight line and points in a high-dimensional space to a hyperplane. After more than 200 years of development, the classic least square technology has become the most widely used technology for data processing in many fields. However, for ill-posed problems in linear regression or nonlinear regression, the performance based on least squares regression technology will become very bad. In response to this situation, many scholars have studied the improved model of least squares regression and proposed many new The regression algorithm. Ridge regression (Ridge regression, referred to as...

AI Technical Summary

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

Images