Photovoltaic generation power prediction method based on self-learning composite data source autoregression model

A photovoltaic power generation, autoregressive model technology, applied in forecasting, data processing applications, instruments, etc., can solve problems such as photovoltaic power generation uncertainty, uncontrollable power grid security, stability and economic operation

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

With the continuous improvement of the grid-connected scale of new energy, the uncertainty and uncontrollability of...

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  • Photovoltaic generation power prediction method based on self-learning composite data source autoregression model
  • Photovoltaic generation power prediction method based on self-learning composite data source autoregression model
  • Photovoltaic generation power prediction method based on self-learning composite data source autoregression model

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Embodiment Construction

[0050] 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.

[0051] A method for forecasting photovoltaic power generation based on self-learning composite data derived from a regression model, including inputting data to obtain autoregressive model parameters,

[0052] And input the input data required for photovoltaic power generation prediction into the autoregressive model determined according to the parameters of the above autoregressive model to obtain the prediction result; perform post-evaluation on the prediction result, that is, analyze the error between the predicted value and the measured value, such as the prediction error Greater than the maximum error allowed, then re-carry out the autoregressive model AR (p) or...

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Abstract

The invention discloses a photovoltaic generation power prediction method based on a self-learning composite data source autoregression model. The photovoltaic generation power prediction method based on the self-learning 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 photovoltaic generation power prediction are input into the autoregression model determined according to the parameters of the autoregression model, so that a prediction result is obtained; post-evaluation is conducted on the prediction result, namely the error between a predicted value and a measured value is analyzed, and order determination of the autoregression model AR(p) and estimation of the parameters of the model AR(p) are conducted again if a predicted error is larger than an allowable maximum error. 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 self-learning composite data derived 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 tr...

Claims

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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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