Wind power combined predication method based on ensemble average empirical mode decomposition and improved Elman neural network

Abstract

The invention discloses a wind power combined predication method based on ensemble average empirical mode decomposition and an improved Elman neural network, and belongs to the technical field of wind power prediction. The method comprises the following steps: step 1, extracting wind speed sequence historical data and normalizing the data; step 2, making sequence analysis of the extracted wind speed sequence historical data through empirical mode decomposition; step 3, reconstructing a phase space for the sequences obtained through empirical mode decomposition; step 4, cyclically selecting the number of nodes in a hidden layer to train an Elman neural network, and superposing the prediction results of the sequences to obtain a wind speed prediction result; and step 5, making error analysis of the wind speed prediction result. The modeling process of the method is simple and practical. By adopting the method, the wind power can be predicted quickly and effectively. The method is of great significance to the security and stability and dispatching operation of a power system under the condition of wind power grid-connection.

Description

technical field [0001] The invention discloses a wind power combined prediction method based on overall average empirical mode decomposition and improved Elman neural network, belonging to the technical field of wind power prediction. Background technique [0002] In today's increasingly prominent energy problems, wind energy, as a widely distributed renewable energy, has attracted widespread attention. With the maturity of wind power generation technology, the proportion of wind power in the total power generation of the power system is gradually increasing. However, the randomness and intermittency of wind energy have adverse effects on the power quality and the safe and stable operation of the power grid, and effective prediction of wind power is to reduce the above-mentioned effects and system operating costs, and increase the penetration power limit of wind power. Therefore, it has important theoretical and practical significance to study the short-term prediction meth...

AI Technical Summary

Problems solved by technology

This kind of statistical method can meet the accuracy requirements for the wind power prediction results several hours in advance, but the accuracy is not enough for the prediction results of a longer time in advance

With the deepening of wind power technology, these methods have exposed defects that are difficult to overcome, such as poor prediction accuracy, slow convergence speed, and limitations.

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