Wind power ultra-short-term power prediction method based on Grubrum matrix and convolutional neural network

Abstract

The invention relates to a wind power ultra-short-term power prediction method based on a Grubrum matrix and a convolutional neural network. The method is suitable for the wind power generation power prediction field. According to the technical scheme, the wind power ultra-short-term power prediction method based on the Grubrum matrix and the convolutional neural network is characterized by comprising the following steps: obtaining the historical wind speed, the historical wind direction and the historical power, wherein the historical wind speed, the historical wind direction and the historical power comprise the wind speed, the wind direction and the power of n moments before the to-be-predicted moment; performing VMD decomposition on the historical power data to obtain m characteristic signals with different center frequencies; performing normalization processing on the historical wind speed, the historical wind direction and the characteristic signal; carrying out data fusion on the normalized historical wind speed, historical wind direction and characteristic signals, and constructing a Gramer matrix based on data obtained by data fusion; and inputting the Grubrum matrix into a trained wind power prediction model to obtain a power prediction result. The wind power prediction model is constructed based on a convolutional neural network.

Description

technical field [0001] The invention relates to a wind power ultra-short-term power prediction method based on a Gramma matrix and a convolutional neural network. It is applicable to the field of wind power generation power prediction. Background technique [0002] As a new type of energy, wind energy has been widely used due to its unlimited reserves, safety, and cleanliness, and has been vigorously developed by various countries. The non-stationarity of wind speed leads to large randomness and volatility of wind power generation, which brings challenges to the safe, stable and economical operation of large-scale wind power grid connection, and the accuracy of wind power prediction needs to be improved. [0003] Short-term and ultra-short-term forecasting can provide reliable power transient information for power dispatching and safety of wind power grid connection. Therefore, research on wind power forecasting mainly focuses on short-term and ultra-short-term wind power f...

AI Technical Summary

Problems solved by technology

The non-stationarity of wind speed leads to the randomness and volatility of wind power generation, which brings challenges to the safe, stable and economical operation of large-scale wind power grid connection. The accuracy of wind power prediction needs to be improved

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