A ultra-short-term wind power probability prediction method

A technology of probabilistic forecasting and wind power, applied in forecasting, instruments, calculation models, etc., can solve the problems of reduced model forecasting accuracy and difficulty in adapting forecasting models

Active Publication Date: 2019-01-25
HOHAI UNIV
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However, in specific operations, if the given data is relatively active, making it difficult f

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  • A ultra-short-term wind power probability prediction method
  • A ultra-short-term wind power probability prediction method
  • A ultra-short-term wind power probability prediction method

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[0023] Below in conjunction with specific embodiment, further illustrate the present invention, should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention, after having read the present invention, those skilled in the art will understand various equivalent forms of the present invention All modifications fall within the scope defined by the appended claims of the present application.

[0024] Such asfigure 1 As shown, an ultra-short-term wind power probability prediction method based on wavelet analysis and extreme learning machine, the specific steps are as follows:

[0025] 1) Analyze and study the wind farm data, extract the features closely related to the wind power data, collect the historical wind power, historical wind speed and weather type data vectors of the wind farm, and obtain the training sample set [x 1 ,x 2 ,x 3 ,x 4 ,...x 15 ,y], where y is the wind power va...

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Abstract

The invention discloses an ultra-short-term wind power probability prediction method, which collects historical data of a wind farm and obtains a training sample set. According to the historical dataof the influencing factors, the input variables are generated and the sample set is obtained. Wavelet decomposition and wavelet coefficient reconstruction are used to get wavelet sample set. The parameters of each wavelet sample set are trained by the limit learning machine, the prediction model of wavelet limit learning machine is obtained, a test set is brought into the network to obtain a wavelet ultra-short-term point prediction value, the training error of each wavelet limit learning machine model and the point prediction value are stored and added and averaged, Gaussian distribution function of wavelet model training error is obtained by estimating the Gaussian distribution parameter of the real error of the model. The ultra-short-term probability prediction interval of wavelet can be obtained by the prediction value of the joint point according to the confidence requirement.

Description

technical field [0001] The invention relates to an ultra-short-term wind power probability prediction method based on wavelet analysis and extreme learning machine, which performs probability interval prediction on wind power and belongs to the technical field of new energy consumption. Background technique [0002] At present, the global economy is developing rapidly, and the energy structure is developing in the direction of low-carbon and clean new energy. As an important part of new energy, wind energy has always been valued by the world. With the continuous development of wind power technology in recent years, the installed capacity of wind power in my country has continued to increase, and wind power has gradually become the third largest source of electricity after hydropower and thermal power. [0003] Many problems have also been highlighted in the process of wind energy utilization. For example, the wind has strong randomness and instability, which makes the electr...

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/06G06N20/00
CPCG06Q10/04G06Q50/06Y04S10/50
Inventor 孙永辉王朋候栋宸翟苏巍武小鹏王义吕欣欣周衍张宇航钟永洁陈凯夏响张闪铭
Owner HOHAI UNIV
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