Wind power prediction method
A wind power forecasting and wind power technology, applied in forecasting, neural learning methods, instruments, etc., can solve the problems of low wind power forecasting accuracy, and achieve the effects of improving modal mixing, improving limitations, and improving false components
Pending Publication Date: 2020-08-28
XIAN UNIV OF TECH
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[0004] The purpose of the present invention is to provide a wind power prediction method, which solves the problem of low prediction accuracy of wind power existing in the prior art
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[0123] The data of a wind farm in northern Shaanxi in August 2009 were analyzed. The present invention uses a total of 4032 data points from the 1st to the 28th of the wind farm to carry out modeling, and a total of 432 data points from the 29th to the 31st are used to predict and test the established prediction model. And the prediction results are combined with least squares support vector machine (LS-SVM), BP neural network (BPNN), long short-term memory method (LSTM), deep neural network (DBN), EMD combined forecasting model, EEMD combined forecasting model, VMD The combined forecasting model is compared with the VMD combined forecasting model using mutual information (MI) to consider influencing factors, and the comparison results are shown in Table 2;
[0124] Table 2 Prediction performance indicators of each model of wind farm in northern Shaanxi
[0125]
[0126] It can be seen from Table 2 that the prediction accuracy of the combined forecasting model is higher th...
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The invention discloses a wind power prediction method, which comprises the following steps: decomposing historical wind power data into a plurality of modal components with different fluctuation characteristics, and dividing the plurality of modal components into high-frequency modal components and low-frequency modal components; establishing a feature set for each modal component; inputting thehigh-frequency modal component and the corresponding feature set into a first prediction model for prediction to obtain a first prediction modal component, and inputting the low-frequency modal component and the corresponding feature set into a second prediction model for prediction to obtain a second prediction modal component; and integrating the first prediction mode component and the second prediction mode component to obtain a wind power prediction value; and evaluating the prediction modal component. The prediction precision of each component can be improved, and the prediction precisionof the whole wind power is further improved.
Description
technical field [0001] The invention belongs to the technical field of new energy, and relates to a wind power prediction method. Background technique [0002] Since the 21st century, with the rapid development of social economy, the consumption of fossil energy has been increasing. However, the storage of fossil energy is limited, therefore, the development and utilization of new energy has attracted extensive attention. Among all new energy sources, the status of wind energy should not be underestimated. The accurate prediction of wind power output has a significant impact on the stability, safety and economy of power system operation. [0003] At present, the traditional wind power prediction method has the following problems in the prediction process: 1. The wind power data has strong volatility, randomness and multi-frequency, and the wind power data is directly used to predict these wind power data. Characteristics; 2. After the wind power data is decomposed by some ...
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IPC IPC(8): G06Q10/04G06Q50/06G06N3/04G06N3/08
CPCG06Q10/04G06Q50/06G06N3/084G06N3/044
Inventor 张刚解佗张靠社罗军刚刘洪池徐奔奔冯培基
Owner XIAN UNIV OF TECH