Wind power forecast method based on error correction and promotion wavelet combination forecast model

A technology of wind power forecasting and wind power, applied in forecasting, electrical components, circuit devices, etc.

Inactive Publication Date: 2017-10-03
ZHEJIANG GONGSHANG UNIVERSITY
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  • Abstract
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  • Claims
  • Application Information

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Problems solved by technology

[0005] The purpose of the present invention is to address the deficiencies in the prior art, to provide a wind power forecasting method based on error correction and lifting wavelet combination forecasting model, using lifting wavelet to process historical data, not only can extract the main characterist

Method used

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  • Wind power forecast method based on error correction and promotion wavelet combination forecast model
  • Wind power forecast method based on error correction and promotion wavelet combination forecast model
  • Wind power forecast method based on error correction and promotion wavelet combination forecast model

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Embodiment

[0128] Embodiment: Select the wind power data of a certain wind farm for 5 days as a sample, and use the wind power data of the 6th day to carry out virtual prediction and comparative analysis. The time resolution of the wind power data is one sampling data point every 15 minutes (96 points a day) . Combined model prediction and error correction based on lifting wavelet are performed on the wind farm power sequence, such as figure 1 shown, including the following steps:

[0129] Step (1): Obtain the historical data sequence of wind power: the above sampling data points will be selected to obtain the historical wind power sequence in For the training data, for the test data.

[0130] Step (2): Using lifting wavelet to decompose the historical wind power sequence The low frequency component and high frequency component of the historical wind power sequence are obtained.

[0131] According to the simulation experiment, the three-level lifting wavelet decomposition of th...

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Abstract

The invention discloses a wind power forecast method based on an error correction and promotion wavelet combination forecast model. According to the method, wind power history data is processed through utilization of a promotion wavelet decomposition technology, main characteristics of a power data sequence can be improved, frequency components with relatively obvious characteristics can be obtained, and the denoising effect can be achieved, thereby applying to various forecast algorithms. A suitable forecast model is selected according to the characteristics of high and low frequency components, the deficiency of a single forecast method can be removed, and the forecast precision can be greatly improved. According to the method, processing errors are corrected through utilization of an error layered analysis method. Compared with the method of obtaining a next moment error forecast value by directly using the forecast model, the method has the advantage that under the condition that relatively high fluctuation occurs in the errors, the next moment error condition and compensation strength can be analyzed precisely, the errors resulting from an error forecast process can be reduced, and the errors of the integrated forecast method can be reduced.

Description

technical field [0001] The invention belongs to the technical field of power system forecasting and control, and in particular relates to a wind power forecasting method based on an error correction and lifting wavelet combined forecasting model. Background technique [0002] With the continuous development of wind power technology and the increasing scale of wind farms, in order to ensure the stable operation of the power system and the reliability of power supply, it is necessary to effectively plan and dispatch the wind power system. The unique intermittence and uncertainty of wind power itself increase the difficulty of grid dispatching, and increase the difficulty of power companies in arranging the start and stop of grid generating units and formulating unit maintenance plans. Therefore, it is necessary to predict the output power of wind farms. Only by accurately predicting the power generated by the wind farm can the operating cost of the wind power system be effecti...

Claims

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Application Information

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IPC IPC(8): H02J3/00G06Q10/04G06Q50/06
CPCG06Q10/04G06Q50/06H02J3/00H02J2203/20
Inventor 戴文战袁婷李静
Owner ZHEJIANG GONGSHANG UNIVERSITY
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