Wind power prediction method based on self-learning radial basis function support vector machine

Abstract

The invention discloses a wind power prediction method based on a self-learning radial basis function support vector machine. The wind power prediction method based on the self-learning radial basis function support vector machine comprises the steps that model training is conducted to enable an SVM model to be obtained; data required by wind power prediction are input into the SVM model obtained through training, so that a prediction result is obtained. Key information is provided for new energy power generation real-time scheduling, a new energy power generation plan, new energy power generation capability evaluation and wind curtailment power estimation by predicting the wind power generated during wind power generation. The ultra-short-term prediction accuracy is effectively improved by the adoption of a composite data source, and thus high-accuracy short-term wind power prediction is achieved.

Description

technical field [0001] The present invention relates to the technical field of wind power forecasting in the process of new energy power generation, that is, a wind power forecasting method based on self-learning radial basis kernel function support vector machine, and specifically relates to a method using composite data sources based on self-learning radial basis kernel function support Vector machine-based short-term wind power forecasting method. Background technique [0002] Most of the large-scale new energy bases generated after my country's wind power enters the stage of large-scale development are located in the "three north regions" (Northwest, Northeast, and North China). Large-scale new energy bases are generally far away from the load center, and their power needs to be transmitted to load center for consumption. Due to the intermittence, randomness and volatility of wind and light resources, the output of wind power and photovoltaic power generation in large-sc...

AI Technical Summary

Problems solved by technology

With the continuous improvement of the grid-connected scale of new energy, the uncertainty and uncontrollability of wind power generation have brought many problems to the safe, stable and economical operation of the power grid.

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