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A railway and wind power plant environment wind speed machine learning prediction method

A technology for wind speed prediction and wind farms, which is applied in the field of wind speed machine learning prediction for railways and wind farms, and can solve problems such as the inability to predict wind speed and the difficulty in accurately finding out the characteristics of wind speed changes.

Active Publication Date: 2019-05-07
CENT SOUTH UNIV
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Problems solved by technology

[0003] Many scholars have invested a lot of energy in studying the law of wind speed change. Due to its complex and nonlinear characteristics, traditional statistical methods and physical methods have great limitations in wind speed prediction. predict

Method used

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  • A railway and wind power plant environment wind speed machine learning prediction method
  • A railway and wind power plant environment wind speed machine learning prediction method
  • A railway and wind power plant environment wind speed machine learning prediction method

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Embodiment Construction

[0059] The present invention will be further described below in conjunction with accompanying drawings and examples.

[0060] Such as figure 1 Shown, a railway and wind farm environment wind speed machine learning prediction method, including the following steps:

[0061] Step 1: Establish a wind measuring station to obtain the original wind speed data;

[0062] Set up a wind measuring station along the railway line or wind farm to be measured, and set the original wind speed data set w(t=[w(t=Δt),w(t=2*Δt), ...,w(t=Q*Δt)];

[0063] The original wind speed data set is a time series set and includes wind speeds at Q sampling moments, and the Q is at least greater than 500;

[0064] Step 2: Construct wind speed model training samples and wind speed model screening samples;

[0065] Use the unscented Kalman equation to filter the original wind speed data set to obtain the processed wind speed data set w'(t)=[w'(t=Δt),w'(t=2*Δt),...,w' (t=Q*Δt)] and wind speed noise set no(t)...

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Abstract

The invention discloses a railway and wind power plant environment wind speed machine learning prediction method. The method includes: selecting various neural network models; establishing 100 wind speed prediction characteristic pre-selection models for each wind speed; carrying out mean square error analysis and correlation analysis; selecting 10 models with good performance and strong feature independence; establishing a plurality of wind speed prediction integrated models; integrating the wind speed prediction characteristics; finally, establishing a wind speed prediction normalization model; carrying out Normalization processing on the wind speed integrated value, calculating the correlation between the predicted wind speed vector and the wind speed vector of the training sample, restoring and predicting the wind speed value through wind speed noise at the corresponding moment with high correlation. The method can effectively predict the non-stable wind speed and has the accurateprediction effect on complex and non-linear wind speed values.

Description

technical field [0001] The invention belongs to the field of wind speed prediction, in particular to a machine learning prediction method for wind speed in railway and wind farm environments. Background technique [0002] In recent years, wind speed prediction has received more and more attention from railway-related safety departments and wind farm fields. The wind speed has the characteristics of randomness, changeability, complex nonlinearity, etc. Realizing super-step and ultra-accurate prediction of wind speed can provide more early warning processing time for train operation in harsh windy environments and ensure driving safety; at the same time, accurate wind speed prediction can provide The wind farm execution scheduling plan provides strong data support to stabilize power generation and ensure power generation safety. [0003] Many scholars have invested a lot of energy in studying the law of wind speed change. Due to its complex and nonlinear characteristics, trad...

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Application Information

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IPC IPC(8): G06N3/04G06Q10/04G06Q50/06
Inventor 刘辉尹恒鑫李燕飞段铸陈浩林
Owner CENT SOUTH UNIV
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