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An intelligent prediction method for wind speed feature extraction along high-speed railway

A technology for wind speed forecasting and high-speed railways, applied in forecasting, data processing applications, instruments, etc., and can solve problems such as non-stationary nonlinearity

Active Publication Date: 2020-12-08
CENT SOUTH UNIV
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  • Application Information

AI Technical Summary

Problems solved by technology

[0004] In order to solve the problem of non-stationary and nonlinear wind speed prediction, effectively extract wind speed characteristics, and predict wind speed with high precision, it is urgent to provide an intelligent prediction method for wind speed feature extraction along high-speed railways to achieve high precision and strong adaptability to wind speed , highly robust predictions

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  • An intelligent prediction method for wind speed feature extraction along high-speed railway
  • An intelligent prediction method for wind speed feature extraction along high-speed railway
  • An intelligent prediction method for wind speed feature extraction along high-speed railway

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Embodiment Construction

[0067] The present invention will be further described below in conjunction with accompanying drawings and examples.

[0068] Such as figure 1 As shown, an intelligent prediction method for wind speed feature extraction along a high-speed railway, including the following steps:

[0069] Step 1: Obtain the original wind speed time series data set through the wind station;

[0070] Set up a wind measuring station at a designated location along the high-speed railway, and obtain the original wind speed time series data set w(t)=[w(t=Δt),w(t=2*Δt),…,w(t= n*Δt)];

[0071] The original wind speed time series data set is composed of n original wind speed data; where Δt is the wind speed sampling time interval; n is the number of sampling times, and n is at least greater than 500;

[0072] Step 2: Establish a wind speed model sample A;

[0073] The wind speed model sample A includes a wind speed model training sample A a and wind speed model to screen sample A b ;

[0074] The ...

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Abstract

The invention discloses an intelligent prediction method for extracting wind speed features along a high-speed railway. The method utilizes different wind speed characteristics to carry out K-means clustering, and selects the optimal clustering number K, divides wind speed model samples into K categories, and then targets K Establish 100 wind speed prediction feature pre-selection models for each clustering category, use mathematical analysis and correlation analysis methods to screen the wind speed prediction feature pre-selection models and establish K wind speed prediction feature model groups and K wind speed prediction normalization models, and finally analyze the predicted results The correlation between the wind speed vector and the wind speed time series data set and restore the real wind speed. This method has the characteristics of high precision prediction, intelligent feature extraction, strong adaptability, and high robustness. It is suitable for high-speed railways in windy environments security and other fields.

Description

technical field [0001] The invention relates to an intelligent prediction method for extracting wind speed features along a high-speed railway. Background technique [0002] In recent years, wind speed prediction has rapidly become one of the hot research fields at home and abroad. Wind speed prediction methods play an increasingly important role and are widely used in the safety of high-speed railways in windy environments. The wind speed prediction method is applied to the safety along the railway in a windy environment. The real-time and accurate prediction of the future wind speed can provide more early warning processing time for the train, form a safe driving plan, and ensure driving safety. [0003] Wind speed will be affected by factors such as season, temperature, altitude, etc. It has strong randomness and complex nonlinear characteristics. At present, the wind speed prediction methods mainly include learning methods, physical methods and statistical methods. Ma...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62G06Q10/04G06Q50/30
CPCG06Q10/04G06F18/23G06F18/214G06Q50/40
Inventor 刘辉尹恒鑫李燕飞段铸陈浩林
Owner CENT SOUTH UNIV
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