Wind power prediction method based on secondary modal decomposition and cascade deep learning

A technology of wind power prediction and wind power, which is applied in neural learning methods, predictions, biological neural network models, etc., can solve problems such as low prediction accuracy and inability to accurately predict wind power sequences, achieve good application prospects, and effectively predict wind power , the effect of reducing the difficulty of prediction

Pending Publication Date: 2021-03-09
YUNNAN POWER GRID CO LTD ELECTRIC POWER RES INST
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Problems solved by technology

[0005] This application provides a wind power prediction method based on quadratic mode decomposition and cascaded deep learning to solve the problem that the wind power prediction model in the prior art cannot accurately predict the wind power sequence and the prediction accuracy is low

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  • Wind power prediction method based on secondary modal decomposition and cascade deep learning
  • Wind power prediction method based on secondary modal decomposition and cascade deep learning
  • Wind power prediction method based on secondary modal decomposition and cascade deep learning

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Embodiment Construction

[0043] Embodiments will be described in detail below, examples of which are illustrated in the accompanying drawings. Where the following description refers to the drawings, the same numerals in different drawings refer to the same or similar elements unless otherwise indicated. The implementations described in the following examples are not intended to represent all implementations consistent with this application. are merely exemplary of systems and methods consistent with some aspects of the present application as recited in the claims.

[0044] see figure 1 , which is a flow chart of a wind power prediction method based on quadratic modal decomposition and cascaded deep learning.

[0045] A wind power prediction method based on quadratic modal decomposition and cascaded deep learning provided by this application includes the following steps:

[0046] Collect raw wind power data and wind speed data;

[0047] Perform signal preprocessing on the collected original wind po...

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Abstract

The invention discloses a wind power prediction method based on secondary modal decomposition and cascade deep learning, and the method comprises the steps: firstly collecting and obtaining original wind power data and wind speed data, carrying out the signal preprocessing of the collected data, and decomposing a wind power and wind speed time sequence into a series of relatively stable sub-sequences through secondary modal decomposition, so that effective modal decomposition can be carried out on wind power signals, then a convolutional neural network gated cycle unit prediction model (CNN-GRU) is used for extracting implicit features of a coupling relation between time sub-sequences generated by decomposition and wind speed, time correlation between the time sub-sequences is further extracted, finally a prediction value of wind power is output, the wind power can be more effectively predicted, and the optimal prediction performance is achieved, and the method has a good application prospect.

Description

technical field [0001] The present application relates to the technical field of wind power prediction in power systems, and in particular, to a wind power prediction method based on quadratic mode decomposition and cascaded deep learning. Background technique [0002] In the prior art, the wind power prediction accuracy is limited, the stability of the power system and the balance of the power grid are greatly affected, and it is difficult to guarantee the quality of power consumption by users. At the same time, the penalty mechanism of the power sector for the accuracy of wind power forecasting has also become one of the important factors in reducing the profit of wind farms. Relevant statistics show that the average fine of wind farms due to problems with wind power forecasting accuracy is close to 200,000 yuan per site every year; in some areas, it is as high as 2 million yuan per site. [0003] Therefore, in order to ensure the safe and economical operation of large-sc...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06N3/04G06N3/08G06Q50/06
CPCG06Q10/04G06Q50/06G06N3/08G06N3/045
Inventor 杨蕾奚鑫泽向川邢超何廷一郭成刘明群何鑫
Owner YUNNAN POWER GRID CO LTD ELECTRIC POWER RES INST
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