An ultra-short-term wind power sliding prediction method

Abstract

The invention relates to an ultrashort-term slide prediction method for wind power. An atomic sparse decomposition method with quite high non-stable signal tracking and prediction capacity is used as a front decomposition method of a neural network. A wind power time sequence is decomposed into an atomic component and a residual error component, the atomic component is automatically predicted, the residual error component is predicted by the neural network, atomic decomposition results are updated by adding the latest wind power real-time data, and further the wind power of a next moment is slidably predicted. Actual wind field data prove that the model can effectively avoid non-stability of the wind power, sparser decomposition effects are achieved, and statistical intervals of absolute average error and root mean square error computation values can be remarkably reduced. Therefore, the ultrashort-term slide prediction method has the advantages that non-stability of the wind power can be effectively avoided, the sparser decomposition effects are achieved, and the statistical intervals of the absolute average error and root mean square error computation values can be remarkably reduced.

Description

technical field [0001] The invention relates to an ultra-short-term wind power prediction method, in particular to an ultra-short-term wind power sliding prediction method. Background technique [0002] As a new energy source with low cost, mature technology and high reliability among renewable energy sources, wind energy has developed rapidly in recent years and has begun to play an important role in energy supply. With the increase in the scale of wind farms, the fluctuation and non-stationarity of wind speed have become serious problems that restrict the large-scale and efficient grid connection of wind power. Wind power forecasting technology is one of the key technologies to solve wind power fluctuations, wind power grid integration and grid dispatching, which also puts forward higher requirements for accurate wind power forecasting. [0003] In order to obtain higher prediction accuracy, many researches at home and abroad focus on constructing a suitable prediction mo...

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Problems solved by technology

[0031] However, there is no technical solution for applying the atomic sparse theory to wind power prediction in this field.

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