Method for predicting short-term wind power probability density based on EWT quantile regression forest

A quantile regression, wind power technology, applied in the field of power system, can solve problems such as low computational efficiency, lack of theoretical basis, modal aliasing, etc.

Inactive Publication Date: 2018-02-16
HOHAI UNIV
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Problems solved by technology

[0007] Purpose of the invention: The present invention is aimed at the problems existing in the existing power system load forecasting technology, such as the general wind power forecasting method can only output deterministic point forecasting results, and it is difficult to fully reflect the randomness

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  • Method for predicting short-term wind power probability density based on EWT quantile regression forest
  • Method for predicting short-term wind power probability density based on EWT quantile regression forest
  • Method for predicting short-term wind power probability density based on EWT quantile regression forest

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[0072] 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.

[0073] In order to effectively improve the prediction accuracy of short-term wind power, the method of the present invention uses EWT to preprocess the original wind power, decomposes it into a series of empirical models with different characteristics, and establishes a prediction model for each empirical model. Empirical wavelet is essentially a group of bandpass filters selected according to the signal spectrum characteristics, so as to adaptively screen out AM-FM components from the original sig...

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Abstract

The invention discloses a method for predicting the short-term wind power probability density based on the EWT quantile regression forest. The method comprises the steps of 1) decomposing an originalwind power sequence into a series of mutually different feature empirical modes by using the empirical wavelet transform (EWT); 2) recombining the empirical modes according to a frequency range to form high frequency, intermediate frequency and low frequency components; 3) select an input variable for each component by using the Pearson correlation coefficient; 4) establishing a quantile regression forest prediction model for each component, and obtaining regression prediction results of different quantile points; 5) superposing the prediction results of the components to obtain a wind power prediction value; and 6) obtaining the prediction of the wind power probability density by nuclear density estimation. The method provided by the invention effectively improves the prediction precisionof the wind power, obtains the prediction of the wind power probability density at any moment, and can well solve the wind power prediction problem in a power system.

Description

technical field [0001] The invention relates to a method for predicting the probability density of short-term wind power in an electric power system, which is used to predict the wind power in the electric power system, and belongs to the technical field of electric power systems. Background technique [0002] The proportion of wind power installed in the power grid has been increasing year by year, which has effectively alleviated the energy shortage and environmental pollution pattern, but its intermittency and uncertainty have seriously affected the safety, stability and economic operation of the power grid. Short-term wind power forecasting is an important decision-making basis for automatic power generation control and power dispatching, which can effectively improve the reliability of power system operation. Therefore, it is necessary to study new technologies and methods to improve the accuracy of wind power prediction and meet the needs of engineering applications. ...

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/06
CPCG06Q10/04G06Q50/06
Inventor 孙国强梁智卫志农臧海祥周亦洲
Owner HOHAI UNIV
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