Ultra-short-period wind speed prediction method based on spectral clustering type and genetic optimization extreme learning machine

Abstract

The invention relates to an ultra-short-period wind speed prediction method based on a spectral clustering type and genetic optimization extreme learning machine. The method comprises the steps that S1. data are prepared; S2. the prepared data are preprocessed; S3. the preprocessed data are subjected to wavelet transformation; S4. the data obtained after wavelet transformation are subjected to normalization processing; S5. through correlation analysis, the data obtained after normalization processing are selected, so that an input variable is determined; S6. through main component analysis, the input variable generated in the S5 is subjected to dimension reduction processing; S7. through a spectral clustering type method, the data obtained after dimension reduction processing in S6 are subjected to clustering analysis, and an extreme learning machine sample space is formed with the data obtained after normalization processing in the S4; S8. through the extreme learning machine and a genetic algorithm, the data of the extreme learning machine sample space formed in the S7 are subjected to hierarchical prediction; and S9. hierarchical prediction values are added, and an ultra-short-period wind speed prediction value is obtained. Ultra-short-period and multi-step prediction on wind speed are achieved, prediction accuracy is improved, the computing amount is greatly lowered, and prediction efficiency is improved.

Description

technical field [0001] The invention relates to the technical field of wind speed prediction, in particular to an ultra-short-term wind speed prediction method based on spectral clustering and genetic optimization extreme learning machine. Background technique [0002] Wind power is a kind of clean renewable energy, which is relatively simple to develop and utilize, and has been paid more and more attention by countries all over the world. Effective wind speed prediction is the basic link of wind power generation research, and is the necessary prerequisite and guarantee for the establishment and operation of wind power forecasting and forecasting systems for grid-connected wind farms. High-precision ultra-short-term wind speed prediction for wind farms can effectively reduce grid voltage and frequency fluctuations caused by sudden cut-out of wind turbines, thereby reducing large fluctuations in wind power output and ensuring safe and reliable operation of the power system. ...

AI Technical Summary

Problems solved by technology

However, the traditional neural network learning algorithm needs to manually set the network training parameters, and the gradient descent algorithm is often used to adjust the weight parameters. The learning speed is slow, the generalization performance is poor, and it is easy to generate local optimal solutions, which limits its further application.

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