VMD-GRU-based short-term wind speed prediction method

Abstract

The invention discloses a VMD-GRU-based short-term wind speed prediction method. The method is characterized is characterized in that it comprises, acquiring data of wind speeds at the current momentand n moments closest to the current moment to form a time sequence; and preprocessing the time sequence to obtain a plurality of sub-sequences and residual components, respectively inputting each sub-sequence and each residual component into respective corresponding trained GRU models, outputting predicted values by the trained GRU models, and performing post-processing on all the predicted values to obtain future wind speed prediction data at the next moment. According to the method, non-stationary wind speed data is decomposed into sub-sequences and residual components with different frequencies by adopting a variational mode decomposition method, the stability of the sub-sequences and the residual components is good, and better prediction is facilitated; the method has good predictionprecision for the wind speed with strong volatility, randomness and uncertainty, and the operation state of the wind power generation device can be adjusted more reasonably.

Description

technical field [0001] The invention relates to a VMD-GRU-based short-term wind speed prediction method, which belongs to the technical field of wind power generation. Background technique [0002] As an important part of large-scale wind power generation, accurate wind speed prediction is important for the protection of wind turbines and the safety of grid power supply, reducing the impact of wind turbines on the grid and ensuring the stability of the grid. and power grid transformation to provide reliable data protection. [0003] In recent years, with the utilization of renewable energy, the number of wind turbines has continued to increase, and the share of wind power generation in the power grid has continued to increase. Due to the intermittent and uncertain nature of wind power output, it is easy for wind power generation to be damaged during grid-connected power generation. The power grid causes certain voltage fluctuations and reduces the reliability of power suppl...

AI Technical Summary

Problems solved by technology

[0005] At present, some people use wavelet analysis combined with neural network to predict time series, but wavelet analysis needs to select the appropriate mother wavelet and set the number of feasible decomposition layers. The adaptive effect of the decomposition of nonlinear and non-stationary signals is poor, and the prediction accuracy is still low. needs improvement

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