Wind power plant short-term wind power prediction method based on VMD-FCM-GRU

Abstract

The invention discloses a wind power plant short-term wind power prediction method based on VMD-FCM-GRU, and belongs to the technical field of wind power prediction. The method comprises the followingsteps: firstly, obtaining a series of finite stable subsequence components based on a VMD normalized complex wind power time sequence, clustering the subsequence components by using an FCM algorithm,clustering the subsequences with similar fluctuation trends, and superposing the subsequences so as to shorten the training time; and training a GRU neural network for prediction by using the clustered sequences, and superposing and reversely normalizing prediction results of the sub-sequences to obtain a final prediction result. According to the method, in combination with the strong calculationefficiency of the GRU, the VMD-FCM-GRU combined model can realize relatively good prediction precision and relatively short training time, and the precision and speed of short-term wind power prediction can be remarkably improved.

Description

technical field [0001] The invention relates to the technical field of wind power forecasting, in particular to a VMD-FCM-GRU-based short-term wind power forecasting method for wind farms. Background technique [0002] With the reduction of world energy, the research and development and utilization of renewable energy have become an important development strategy. With the advantages of wide range, renewable and non-polluting, wind energy has gradually become the most promising energy source. The inherent randomness and volatility of wind power generation creates an imbalance between wind power supply and load demand. Improving the prediction accuracy of wind power is an effective way to solve these problems, and it is also a research hotspot for a long time. [0003] Wind forecasting technology can be divided into two categories according to the form of forecasting results: uncertain forecasting and definite forecasting. Uncertain forecasting methods are usually based on...

AI Technical Summary

Problems solved by technology

However, LSTM has a complex structure and requires a long training and prediction time

When the training samples are insufficient or the training time is insufficient, it is easy to cause the problem that the model training does not converge or the error is large

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