Ultra-short-term wind power prediction method based on composite data source autoregression model

An autoregressive model, ultra-short-term forecasting technology, applied in forecasting, data processing applications, instruments, etc., can solve the problems of wind power, photovoltaic power generation fluctuations, and transmission network charging power fluctuations.

Inactive Publication Date: 2014-07-16
STATE GRID CORP OF CHINA +2
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Problems solved by technology

Due to the intermittence, randomness and volatility of wind and light resources, the output of wind power and photovoltaic power generation in large-scale new energy bases will fluctuate in

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  • Ultra-short-term wind power prediction method based on composite data source autoregression model
  • Ultra-short-term wind power prediction method based on composite data source autoregression model
  • Ultra-short-term wind power prediction method based on composite data source autoregression model

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[0048] The preferred embodiments of the present invention will be described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described here are only used to illustrate and explain the present invention, and are not intended to limit the present invention.

[0049] A super-short-term prediction method of wind power based on composite data derived from regression model, including input data to obtain autoregressive model parameters,

[0050] And inputting the input data required for wind power prediction into the autoregressive model determined according to the parameters of the above-mentioned autoregressive model to obtain the prediction result; wherein the input data to obtain the autoregressive model parameters specifically includes step 101, inputting the basic data for model training,

[0051] Step 102, using the residual variance map method to determine the order of the autoregressive model AR(p),

[0052] Step 10...

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Abstract

The invention discloses an ultra-short-term wind power prediction method based on a composite data source autoregression model. The ultra-short-term wind power prediction method based on the composite data source autoregression model comprises the steps that data are input to enable parameters of the autoregression model to be obtained; input data required by wind power prediction are input into the autoregression model which is determined according to the parameters of the autoregression model, so that a prediction result is obtained, wherein the method for obtaining the parameters of the autoregression model by inputting the data specifically comprises the steps that model training basic data are input, order determination is conducted on the autoregression model AR(p) according to a residual variogram method, and the parameters of the model AR(p) with the determined order are estimated according to a moment estimation method. Key information is provided for new energy power generation real-time scheduling, a new energy power generation day-ahead plan, a new energy power generation monthly plan, new energy power generation capability evaluation and wind curtailment power estimation by predicting the wind power generated during wind power generation. The ultra-short-term wind power prediction accuracy is effectively improved due to the fact a composite data source is introduced, and thus the on-grid energy of new energy resources is effectively increased on the premise that safe, stable and economical operation of a power grid is guaranteed.

Description

technical field [0001] The present invention relates to the technical field of wind power forecasting in the process of new energy power generation, in particular to an ultra-short-term wind power forecasting method based on composite data originating from a regression model. Background technique [0002] Most of the large-scale new energy bases generated after my country's wind power enters the stage of large-scale development are located in the "three north regions" (Northwest, Northeast, and North China). Large-scale new energy bases are generally far away from the load center, and their power needs to be transmitted to load center for consumption. Due to the intermittence, randomness and volatility of wind and light resources, the output of wind power and photovoltaic power generation in large-scale new energy bases will fluctuate in a large range, which will further lead to fluctuations in the charging power of the transmission network, which will affect the safety of po...

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/06
Inventor 汪宁渤路亮何世恩马彦宏赵龙周强马明张健美
Owner STATE GRID CORP OF CHINA
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