Wind speed prediction method based on complete total experience modal decomposition and extreme learning machine

A technology of overall empirical mode and empirical mode decomposition, applied in prediction, machine learning, computing models, etc., can solve problems such as low precision and low robustness, and achieve improved prediction accuracy, learning ability, and learning rate Effect

Inactive Publication Date: 2017-06-27
CHANGSHA UNIVERSITY OF SCIENCE AND TECHNOLOGY
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Problems solved by technology

[0006] The present invention proposes a wind speed prediction method based on complete overall empirical mode decomposition and extreme learning machine to solve the existing low precision and low robustness wind speed prediction method

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  • Wind speed prediction method based on complete total experience modal decomposition and extreme learning machine
  • Wind speed prediction method based on complete total experience modal decomposition and extreme learning machine
  • Wind speed prediction method based on complete total experience modal decomposition and extreme learning machine

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Embodiment Construction

[0055] In order to clarify the advantages of the present invention, the above technical solutions will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0056] Such as figure 1 As shown, an example of a wind speed prediction method based on complete overall empirical mode decomposition and extreme learning machine includes the following steps:

[0057] S1. Collect the original wind speed data, and use the complete overall empirical mode decomposition to decompose the original wind speed sequence to obtain multiple stable natural mode components and residual sequences;

[0058] The decomposition steps of the complete overall empirical mode decomposition to the original wind speed sequence are as follows:

[0059] S11. Add Gaussian white noise to the original signal s[τ] to obtain the signal s[τ]+ε 0 ω i [τ], where ω i Represents Gaussian white noise, ε represents the signal-to-noise ratio;

[0060] S12. For s[...

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Abstract

The invention discloses a wind speed prediction method based on complete total experience modal decomposition and an extreme learning machine. According to the method, firstly, complete total experience modal decomposition is utilized to decompose an unstable and random original wind speed sequence to acquire a sequence of stable inherent modal components and one residual error sequence, secondly, the extreme learning machine is utilized to carry out training prediction for each inherent component and the residual error sequence to acquire respective sub prediction result, and lastly, all the sub prediction results are reconstructed to acquire a final wind speed prediction result. Compared with other three wind speed prediction models,not only can the method improve wind speed prediction precision, but also enhances model robustness and a training prediction rate.

Description

technical field [0001] The invention relates to the fields of machine learning and wind power generation, in particular to a wind speed prediction method based on complete overall experience mode decomposition and extreme learning machine. Background technique [0002] With the development of renewable energy and the continuous reduction of fossil energy, wind power, as a clean renewable energy, has attracted more and more attention. Therefore, making full use of wind energy to generate electricity has become an important task. However, due to the constantly changing behavior of wind speed, the wind speed sequence is characterized by randomness, instability and nonlinearity. Due to the instability of wind speed, it will lead to the instability of wind power energy, which will lead to the instability of the whole power system. Therefore, it is necessary to predict the wind speed and take corresponding measures in advance to ensure the stability of the power system. [0003...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/06G06N99/00
CPCG06N20/00G06Q10/04G06Q50/06
Inventor 颜宏文卢格宇
Owner CHANGSHA UNIVERSITY OF SCIENCE AND TECHNOLOGY
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