A short-term wind power prediction method and system based on DWT and LSTM

Abstract

The present invention relates to the technical field of distributed renewable energy, more particularly, to a short-term wind power generation power prediction method and system based on DWT and LSTM,comprising: decomposing wind power raw data into low-frequency signals and high-frequency signals; standardizing the low frequency and high frequency signals using the z-score normalization method. so that the normalized data obeys normal distribution; dividing low frequency signal and the high frequency signal into a training set, a verification set and a test set according to the time sequence.The independent LSTM is used to train the training set and the verification set, and the test set of each signal is predicted to obtain the predicted value of each component, and the predicted valueof each component is summed up to obtain the predicted result. The invention can more fully mine the main information and the secondary information contained in the detailed information contained in the approximate signal in the data, can effectively avoid the problems of gradient disappearance and gradient explosion, and can better improve the prediction accuracy of the wind power generation.

Description

technical field [0001] The present invention relates to the technical field of distributed renewable energy, and more specifically, to a short-term wind power prediction method and system based on DWT and LSTM. Background technique [0002] Global renewable energy has been showing rapid growth, among which wind power has grown exponentially, and its importance has become increasingly prominent. The increasingly serious environmental pollution and energy crisis have made countries all over the world pay more attention to wind power generation, and a lot of related research work has been carried out. The large-scale grid-connection of wind power can alleviate the problem of power supply shortage. However, due to the random uncertainty, non-stationarity, volatility and variability of wind energy to a certain extent, the greater the grid-connected power, the greater the impact on the grid. It will affect the stability and security of the power system. [0003] Recently, releva...

AI Technical Summary

Problems solved by technology

As the proportion of wind power generation and its grid-connected scale gradually increase, the uncertainty, instability, volatility and variability of wind energy cannot provide reliable data reference for grid dispatching and planning, nor can it ensure the grid safe and stable operation

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