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Wavelet transform and particle swarm optimized grey model-based short-term wind speed forecasting method

A technology of wind speed prediction and wavelet transform, which is applied in calculation models, biological models, fluid speed measurement, etc., can solve the problem of inaccurate short-term wind speed prediction results

Inactive Publication Date: 2015-12-02
HOHAI UNIV
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  • Application Information

AI Technical Summary

Problems solved by technology

The short-term wind speed prediction results obtained by GM (gray model) independent prediction are sometimes not accurate enough, so it needs to be combined with other methods to improve its performance

Method used

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  • Wavelet transform and particle swarm optimized grey model-based short-term wind speed forecasting method
  • Wavelet transform and particle swarm optimized grey model-based short-term wind speed forecasting method
  • Wavelet transform and particle swarm optimized grey model-based short-term wind speed forecasting method

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Embodiment

[0156] In order to verify the effect of the present invention, a certain wind farm is taken as an example to illustrate the specific implementation process of an improved gray prediction system based on wavelet transform and particle swarm. The wind farm installed data measuring instruments, and the observation points recorded the data from mid-April to July 2009. Arrange the data in chronological order, and the selected training samples are the recorded data of the observation points at the first 1158 moments, which is about 4 days of wind speed data. The test sample is the recorded data of the subsequent 288 observation points. Use Matlab to program the constructed calculation example, and analyze the results, such as Figure 4 shown. For comparison, the conventional gray model GM and only parameter-optimized GMIPSO predictions were used as references.

[0157] The MAE (average absolute error) of GM is 0.4948, and its MAPE (average absolute percentage error) is 9.0012%; t...

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Abstract

The invention discloses a wavelet transform and particle swarm optimized grey model-based short-term wind speed forecasting method. The method comprises the following steps: decomposing and analyzing wind speed data by utilizing wavelet decomposition and reconstruction; inputting the reconstructed data into a particle swarm optimized grey wind speed forecasting model one by one to obtain forecasting values; adding the forecasting values to obtain a wind speed forecasting value; and finally evaluating the wind speed forecasting ability. According to the method provided by the invention, the problem of high-frequency component overfitting is solved on the basis of ensuring the low-frequency component fitting; and less information is required and the forecasting can be completed under the condition of being relatively lack of data, so that the precision and stability of the method is higher than that of the traditional grey model forecasting method.

Description

technical field [0001] The invention relates to a short-term wind speed prediction method based on wavelet transform and particle swarm improved gray model, which belongs to the field of short-term wind speed prediction. Background technique [0002] The dual pressure of world energy crisis and environmental protection has prompted countries and regions around the world to pay more attention to the development and utilization of sustainable energy and clean energy. Among them, wind energy is clean and pollution-free, with large energy and broad application prospects. Nowadays, the research on wind power generation at home and abroad is getting deeper and wider, including large-scale wind turbines, wind power penetration power and so on. The amount of power generated is determined by the size of the wind energy. The size of the wind energy is affected by the size of the wind speed. It can be concluded that the size of the wind speed is closely related to the efficiency of ...

Claims

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Application Information

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IPC IPC(8): G06F19/00G06N3/00G01P5/00
Inventor 卫志农郭勉臧海祥孙国强孙永辉朱瑛范磊陈胜
Owner HOHAI UNIV
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