Ultra-short-term wind power prediction method based on autoregression moving average model

An autoregressive sliding, ultra-short-term forecasting technology, applied in forecasting, instrumentation, data processing applications, etc., can solve problems such as power transmission network charging power fluctuations, wind power, photovoltaic power generation output fluctuations, etc.

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 autoregression moving average model
  • Ultra-short-term wind power prediction method based on autoregression moving average model
  • Ultra-short-term wind power prediction method based on autoregression moving average model

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Embodiment Construction

[0055] 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.

[0056] An ultra-short-term prediction method for wind power based on an autoregressive moving average model, including inputting data to obtain autoregressive moving average model parameters;

[0057] The input data required for wind power prediction is input into the autoregressive moving average model determined according to the parameters of the above-mentioned autoregressive moving average model to obtain the prediction result.

[0058] The operation of power systems including large-scale wind power depends on huge and accurate data sets, and if wind power forecasting can effectively integrate and utilize these data, the prediction accuracy can be effectively imp...

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Abstract

The invention discloses an ultra-short-term wind power prediction method based on an autoregression moving average model. The ultra-short-term wind power prediction method based on the autoregression moving average model comprises the steps that data are input to enable parameters of the autoregression moving average model to be obtained; input data required by wind power prediction are input into the autoregression moving average model determined according to the parameters of the autoregression moving average model, so that a prediction result is obtained. 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 derived from a regression moving average 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 s...

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/06
CPCY02A90/10
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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