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

A technology of photovoltaic power generation and autoregressive sliding, which is applied in forecasting, instrumentation, data processing applications, etc., and can solve the problems of photovoltaic power generation uncertainty, uncontrollable power grid security, stability and economic operation, etc.

Inactive Publication Date: 2014-07-16
STATE GRID CORP OF CHINA +2
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With the continuous improvement of the grid-connected scale of new energy, the uncertainty and uncontrollability of ph

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

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[0054] 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.

[0055] An ultra-short-term prediction method for photovoltaic power generation based on an autoregressive moving average model, including inputting data to obtain autoregressive moving average model parameters;

[0056] Input the input data required for photovoltaic electric power prediction into the autoregressive moving average model determined according to the parameters of the above-mentioned autoregressive moving average model to obtain the prediction result.

[0057] The operation of power systems including large-scale photovoltaic power generation relies on huge and accurate data sets, and if the photovoltaic power generation prediction can effectively integra...

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Abstract

The invention discloses an ultra-short-term photovoltaic generation power prediction method based on an autoregression moving average model. The ultra-short-term photovoltaic generation 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 photovoltaic generation 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 photovoltaic generation power generated during photovoltaic generation. The ultra-short-term photovoltaic generation 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 forecasting photovoltaic power generation in the process of new energy power generation, in particular to a method for ultra-short-term forecasting of photovoltaic power generation 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 t...

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/06
CPCY04S10/50
Inventor 汪宁渤路亮刘光途王定美吕清泉
Owner STATE GRID CORP OF CHINA
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