Wind speed prediction method, system and equipment based on hybrid deep learning mechanism

Abstract

The invention provides a wind speed prediction method based on a hybrid deep learning mechanism. The method comprises the following steps: S1, collecting historical wind power data for preprocessing; S2, inputting the preprocessed historical wind power data into a hybrid deep learning mechanism for training; and S3, performing wind speed prediction on the trained prediction model. The invention further provides a wind speed prediction system and equipment based on the hybrid deep learning mechanism. According to the method, the future wind power is predicted only by using the historical wind power data, the neural network is rapidly trained, and the characteristics of the gating circulation unit and the long-short term memory neural network are combined, so that the contradiction between the prediction time and the prediction accuracy is effectively balanced, and the obtained result can promote the power grid to more fully utilize wind resources.

Description

technical field [0001] The present invention relates to the technical field of wind speed prediction, in particular, to a wind speed prediction method, system, and device based on a hybrid deep learning mechanism. Background technique [0002] With the gradual depletion of fossil energy, the sharp increase in electricity consumption required by modern life, and the increasing attention to environmental protection issues, countries around the world are actively carrying out research and construction of green and renewable energy. As an important green and renewable energy, wind energy has the characteristics of large amount of development, less pollution, and smaller footprint. It has become a field that countries around the world have invested heavily in construction. At present, the total installed capacity of wind power in China ranks first in the world. [0003] However, wind power is subject to natural factors, and its output is volatile and uncontrollable. With the co...

AI Technical Summary

Problems solved by technology

The disadvantage of this existing technology is that it relies heavily on the acquired prediction background field data and observation field data, that is, the conventional mainstream prediction method, which requires a long time for network training

However, this existing technology requires a large amount of data, complex types of data, limited prediction accuracy, and does not have the universality of wind power prediction

In addition, this literature has not considered the influence of wind speed variation on wind power prediction

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