A Short-Term Wind Power Forecasting Method Based on Partial Least Squares Regression

Abstract

The present invention provides a short-term wind power prediction method based on partial least squares regression, which analyzes and extracts the characteristics and influencing factors of wind farms that affect wind power prediction, forms historical wind power data vectors, and obtains training sample sets. Set for dimensionality reduction processing, use the obtained training samples to extract components, and conduct correlation analysis on the input and output, then use the least square method to calculate the regression coefficient, establish a partial least square regression model, and select the wind power point value as the test sample The input vector and the corresponding output vector are used as the real value of power output, and brought into the partial least squares regression model to obtain the ultra-short-term forecast value vector of wind power. The invention utilizes the partial least squares regression method to have good multiple regression problem analysis ability, and at the same time includes methods such as data regression model establishment, principal component analysis and typical correlation analysis, which not only simplifies the prediction model structure, but also improves the prediction accuracy and enhances the the generalization ability of the forecasting method.

Description

technical field [0001] The invention relates to a wind power system, in particular to a short-term wind power prediction method. Background technique [0002] With the rapid development of society and economy, countries around the world have increasingly strong demand for energy, which threatens the depletion of traditional fossil energy. The development of renewable energy is imminent for human beings. At the same time, the extensive use of fossil energy has also brought challenges to the social environment. As an important part of renewable energy, wind energy has become one of the important directions of social development by fully exploiting existing wind energy resources and developing clean energy. However, when developing wind energy, there are also very prominent problems, because wind power itself has characteristics such as randomness, fluctuation and intermittent, and these characteristics will make it very difficult for the power grid to absorb wind energy. At ...

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Problems solved by technology

However, in actual operation, if there are too many selected features or influencing factors, the structure of the predicted model may be complicated and the training efficiency will be reduced. The prediction accuracy is very important

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