Ultra-short-term wind power prediction method based on adaptive deep residual network

Abstract

The invention belongs to the technical field of new energy wind power generation power prediction, and relates to an ultra-short-term wind power prediction method based on an adaptive deep residual network, which comprises the following steps: S1, collecting historical data of a wind power plant; s2, screening data by adopting a Pearson correlation coefficient method, and processing abnormal databy adopting a box type diagram analysis method; s3, normalizing the variable data; s4, establishing a deep residual error network wind power prediction model; s5, optimizing the prediction model by adopting an adaptive AdaDelta optimization algorithm to obtain an optimal parameter; s6, inputting the data into a prediction model for training; s7, enabling the output variable to be a wind power prediction value at the to-be-predicted moment; s8, performing reverse normalization processing on the prediction value to obtain a final wind power prediction result; and S9, establishing an evaluation index system, and evaluating the accuracy of the wind power prediction result. According to the method, the problem of network degradation caused by network deepening in the traditional deep neural network model training process is solved, and the model prediction precision is improved.

Description

technical field [0001] The invention relates to the technical field of new energy wind power prediction technology, in particular to an ultra-short-term wind power prediction method based on an adaptive deep residual network. Background technique [0002] With the depletion of non-renewable energy such as coal and oil and the environmental pollution caused by it, renewable energy such as wind energy, solar energy and geothermal energy have become the focus of research. Because wind energy is a clean energy source, and our country is rich in wind resources, the research on wind power generation technology in my country is more mature than other renewable energy technologies, but due to the randomness of wind itself, it has intermittent and fluctuating characteristics. Unstable factors such as large-scale wind power grid connection will have a serious impact on the stable operation of the power system, so accurate prediction of wind power can enhance the safety and stability of...

AI Technical Summary

Problems solved by technology

[0005] The present invention proposes an ultra-short-term wind power prediction method based on an adaptive deep residual network to solve the problems of network degradation, model difficulty in training, and low prediction accuracy during the training process of the traditional deep neural network prediction model.

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