Empirical mode decomposition and Elman neural network combined wind power forecasting method

Abstract

The invention discloses an empirical mode decomposition and Elman neural network combined wind power forecasting method, comprising the following steps: screening samples which are used for forecasting a wind power field, and selecting wind power outputs of forecasting periods within fluctuation months to implement forecasting; implementing empirical mode decomposition on multiple groups of output time sequence sample data of the wind power field, and ensuring that each group can obtain multiple intrinsic mode functions (IMFs) and trend components Res according to decomposition termination conditions; implementing fluctuation degree classification on decomposed IMFs according to a run distinguishing method, and reconstructing the IMFs according to a similar fluctuation frequency principle to obtain total high-frequency components and total low-frequency components; establishing an Elman neural network model, and implementing data normalization on the total high-frequency components, the total low-frequency components and the trend components to obtain training and test data of a neural network; and implementing day-ahead power forecasting for 72h by adopting an Elman improved learning algorithm to obtain a day-ahead forecasting power value of 72h of target wind power outputs. By adopting the empirical mode decomposition and Elman neural network combined wind power forecasting method disclosed by the invention, the number of forecasting components can be reduced, and the forecasting accuracy and forecasting speed can be increased.

Description

technical field [0001] The invention belongs to the technical field of electric power system control, and in particular relates to a wind power prediction method combining empirical mode decomposition and Elman neural network. Background technique [0002] The neural network is composed of a large number of neurons connected by a connection method closely combined with the training network learning algorithm. It has strong fault tolerance and robustness, and has self-learning, self-adapting, self-organizing parallel processing capabilities and information synthesis. ability to adequately approximate arbitrarily complex nonlinearities. At present, the types of neural networks widely used in the field of power system prediction mainly include BP feedforward neural network, RBF radial basis neural network and so on. Because the BP feedforward neural network is a static feedforward network, most of the systems in practical applications are dynamic, and there are obvious deficie...

AI Technical Summary

Problems solved by technology

Since the BP feedforward neural network is a static feedforward network, most of the systems in practical applications are dynamic, and there are obvious deficiencies in using static feedforward networks to identify dynamic systems.

However, the RBF rad

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