Ultrashort-term wind power prediction method

A technology for ultra-short-term forecasting and wind power power, applied in forecasting, data processing applications, calculations, etc., can solve problems such as low forecasting accuracy and complex wind power forecasting methods, achieve accurate forecasting models, reduce training time overhead, and structural risks minimized effect

Inactive Publication Date: 2012-06-27
NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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Problems solved by technology

[0004] Aiming at the shortcomings of the existing wind power prediction methods mentioned in the above background technology such as complexity and low prediction accuracy, the present invention proposes a wind power ultra-short-term prediction method

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  • Ultrashort-term wind power prediction method

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[0042] The preferred embodiments will be described in detail below with reference to the accompanying drawings. It should be emphasized that the following description is exemplary only, and is not intended to limit the scope of the invention and its application.

[0043] A good prediction model must consider both the accuracy of the prediction and the complexity of space and time. Considering the above reasons, this method adopts the idea of ​​combining the deep auto-encoder network and the correlation vector regression model for prediction. The directly collected data is rough, uneven and noisy, so the ridgelet transform is used to process the sample set data. However, the training space and time complexity under the high-dimensional sample set is too large, and the Deep Autoencoder Network (DAN) method adopts the network structure of the Continouse Restricted Boltzmann Machine (CRBM) model. , by training a bidirectional deep neural network with multiple intermediate layers ...

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Abstract

The invention discloses the technical field of wind power prediction, particularly, relates to an ultrashort-term power prediction method. The method comprises the following steps of: firstly, acquiring the wind speed, the wind direction and the wind power of a wind power farm to form a sample set; then, preprocessing the data of the sample set; reducing dimensions of the preprocessed sample set by a depth autocoder network; and finally, training a relevance vector machine regression model by the sample set with reduced dimensions, and predicting the ultrashort-term wind power through the trained relevance vector machine regression model. The method reduces the training time of a prediction model, satisfies the requirements on precision and real-time property in system status estimation, and enables the prediction method to be more accurate.

Description

technical field [0001] The invention belongs to the technical field of wind power prediction, and in particular relates to an ultra-short-term prediction method of wind power. Background technique [0002] With the increasingly prominent energy and environmental problems, wind energy, as the fastest-growing energy source in renewable energy power generation technology, has been paid more and more attention by people. After the wind power is connected to the power grid, it will have an important impact on the power quality of the entire power grid and the stability of the power system operation. In order to reduce this adverse effect, the prediction accuracy of wind power is particularly important. The "Guidelines for Wind Power Prediction Function" in the "People's Republic of China Electric Power Industry Standards" issued by the National Energy Administration of the People's Republic of China points out that ultra-short-term wind power prediction refers to predicting the a...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04
Inventor 李元诚杨瑞仙
Owner NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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