Nonparametric kernel density estimation-based wind power prediction method

A non-parametric kernel density and wind power forecasting technology, which is applied in forecasting, computing, data processing applications, etc., to achieve the effect of improving the accuracy of accurate forecasting

Inactive Publication Date: 2017-03-29
CHINA UNIV OF GEOSCIENCES (WUHAN)
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Problems solved by technology

[0003] The present invention provides a method for predicting wind power based on non-parametric kernel density estimation, which solves the technical problem that wind generators cannot generate power according to the ideal wind power curve due to the uncertainty of wind in the prior art, and achieves improved wind power efficiency. Technical Effects of Power Accurate Prediction Accuracy

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  • Nonparametric kernel density estimation-based wind power prediction method
  • Nonparametric kernel density estimation-based wind power prediction method
  • Nonparametric kernel density estimation-based wind power prediction method

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Embodiment Construction

[0052] In order to better understand the above-mentioned technical solution, the above-mentioned technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0053] The method of wind power prediction based on non-parametric kernel density estimation provided by the present application, the method does not use prior knowledge about data distribution, does not attach any assumptions to the data, and uses the data sample itself to study the data distribution The feature method, which relies entirely on the training data itself for estimation, can be used to estimate the probability density function of any shape, which can better reflect the real distribution of the data itself; directly find the law from the power data itself in a short range of wind speed, and capture the true nature of the data. Distributions whose probability density functions may be of arbitrary shape, e.g. asymmetric and non-unimodal....

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Abstract

The invention discloses a nonparametric kernel density estimation-based wind power prediction method which comprises the following steps: in a first step, historical wind power plant data in a preset time range is obtained, actually measured wind power data is divided into a plurality of subintervals, and the wind power plant data comprises actually measured wind speed and actually measured wind power; in a second step, statistics are run on actually measured wind power in each subinterval, and wind power probability density functions are established according to wind power distribution in each actually measured wind speed subinterval; in a third step, a confidence interval of the actually measured wind power is determined, wind power data outside the confidence interval is deleted, and data in the confidence interval is screened modeling data; in a fourth step, an S type function is used for fitting the modeling data and establishing a wind power prediction model which can be used for prediction.

Description

technical field [0001] The invention relates to the field of wind power prediction, in particular to a method for wind power prediction based on non-parametric kernel density estimation. Background technique [0002] With the rapid consumption of fossil energy, human beings are facing the dual crises of energy depletion and environmental degradation. Therefore, clean and renewable wind energy has received extensive attention and development worldwide in recent years. The total installed capacity of wind power in China has leapt to the first place in the world. The large-scale development of wind power and the reduction of the use of fossil energy have alleviated the energy crisis to a certain extent. However, due to the strong intermittency and randomness of wind energy, with the increase in the number of wind farms and the increasing installed capacity, the large-scale grid connection of wind power has brought great challenges to the safe and economical operation of the gri...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/06
Inventor 丁敏吴敏安剑奇谢华
Owner CHINA UNIV OF GEOSCIENCES (WUHAN)
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