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Wind electricity power probability density predicting method based on genetic algorithm and support vector quantile regression

A quantile regression, wind power technology, applied in the field of wind power, can solve the problems of complex calculation and low reliability

Active Publication Date: 2017-01-04
HEFEI UNIV OF TECH
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Problems solved by technology

[0006] In order to overcome the disadvantages of the existing prediction methods such as low reliability and complicated calculation, the present invention proposes a method for predicting the probability density of wind power power based on genetic algorithm and support vector quantile regression, in order to be able to search globally through the genetic algorithm. It improves the prediction accuracy of wind power and can quantify the uncertainty of wind power, which provides a basis for the safe and stable operation of wind power integration

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  • Wind electricity power probability density predicting method based on genetic algorithm and support vector quantile regression
  • Wind electricity power probability density predicting method based on genetic algorithm and support vector quantile regression
  • Wind electricity power probability density predicting method based on genetic algorithm and support vector quantile regression

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[0062] In this example, a wind power power probability density prediction method based on genetic algorithm and support vector quantile regression, the overall flow chart is as follows figure 1 As shown in Fig. 1, the collected wind power data set is first cleaned, and the cleaned data set is normalized, and the training set and test set data are selected; then the genetic algorithm is used for global search and optimization to find the support vector quantile The optimal parameters of the regression model, and reconstruct the prediction model, and finally obtain the probability density function of wind power at different time points in the future according to the kernel density estimation function; specifically, the detailed process figure 2 shown, follow the steps below:

[0063] Step 1. Collect wind power data and perform data cleaning: this stage is mainly to obtain normal wind power data sets for prediction.

[0064] Step 1.1, collect the historical data of wind power t...

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Abstract

The invention discloses a wind electricity power probability density predicting method based on a genetic algorithm and support vector quantile regression. The method is characterized by comprising the following steps: 1, acquiring data of output power of a wind electric field and carrying out data cleaning; a, carrying out normalization processing on sample data, and selecting data of training sets and testing sets; 3, establishing a support vector quantile regression model; 4, optimizing support vector quantile regression parameters by using the genetic algorithm; and 5, establishing a wind electricity power probability density predicting model to obtain a final wind electricity power predicting result. By the genetic algorithm, global searching can be implemented for optimization, wind electricity power predicting precision is improved, nondeterminacy of wind electricity power can be quantified, and basis is provided for safe and stable running of wind electricity connection.

Description

technical field [0001] The invention belongs to the technical field of wind power, and mainly relates to a method for predicting the probability density of wind power based on genetic algorithm and support vector quantile regression. Background technique [0002] Wind energy is a clean, permanent and renewable new energy source. With the rapid consumption of fossil fuels and the continuous increase of energy demand, its development and utilization have been widely valued by various countries. In recent years, wind power generation technology has gradually matured, and wind energy has become a supplementary energy source to traditional energy sources, and has the fastest growth rate among renewable energy sources. However, because wind power generation is random and has great uncertainty, large-scale wind power grid integration has brought great challenges to the stable operation of the power system. Accurate and effective electric power forecasting results can help the powe...

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Application Information

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IPC IPC(8): G06Q10/04G06N3/12
CPCG06N3/126G06Q10/04Y02D10/00
Inventor 何耀耀李海燕刘瑞王刚郑丫丫严煜东秦杨
Owner HEFEI UNIV OF TECH
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