Wind power forecast method

Abstract

The invention relates to a wind power forecast method. The method is realized through the way that: taking time points of wind power forecasting as basic points; on the basis of standardization of historical data, taking meteorology of the basic points as core elements to perform proximity polymerization on historical meteorological records; performing decorrelation dimension reduction on the meteorological factors of the meteorological records in the polymerization to obtain independent factors; then building a function relationship from independent factor values to standardized values of the wind power according to the mapping relation of the regularization radial basis function network to realize wind power forecast. By adopting the wind power forecast method provided by the invention, not only can problems of high nonlinearity degree of the 'meteorological factors-wind power' mapping relation and large calculated amount, caused by complexity of the meteorological factors, of the conventional methods be solved, but also the problem of poor sensitivity of the conventional wind power forecast methods on fluctuation modes and ranges of the meteorological factors can be solved, so that the wind power forecast accuracy is improved and a high forecast speed is realized.

Description

[0001] Technical field [0002] The invention belongs to the technical field of electric power engineering, and specifically relates to a wind power prediction method. Background technique [0003] At present, wind power is developing rapidly all over the world. Due to the inherent volatility and intermittent nature of wind power, the adequacy of its absorption by the power grid depends on the accuracy and speed of its forecasting method. [0004] The existing wind power prediction method uses multiple time points or even a day as a cycle to establish a mapping relationship from meteorological factors to wind power power in the cycle to realize wind power power prediction. Once this mapping relationship is established, the wind power at various points in a period can be predicted. The existing methods have fast prediction speed, but due to the many time points to be considered in a cycle and the complicated changes of meteorological factors, the mapping relationship is not only hi...

AI Technical Summary

Problems solved by technology

The existing method has a fast prediction speed, but due to the many time points to be considered in a period and the complex changes of meteorological factors, the mapping relationship is not only highly nonlinear, not sensitive enough to the fluctuation pattern and amplitude of meteorological factors, and the accuracy is not high, but also the mapping relationship The establishment requires a large amount of historical data and a large amount of calculation

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