Conditional generative adversarial network-based short-term wind power prediction method
The invention relates to a short-term wind power prediction method based on a generative adversarial network, and the method comprises the steps: extracting a meteorological variable fluctuation time sequence and a historical wind power time sequence from a short-term meteorological scale, generatin...
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Main Authors | , , , , , , , , , , |
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Format | Patent |
Language | Chinese English |
Published |
25.07.2023
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Subjects | |
Online Access | Get full text |
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Summary: | The invention relates to a short-term wind power prediction method based on a generative adversarial network, and the method comprises the steps: extracting a meteorological variable fluctuation time sequence and a historical wind power time sequence from a short-term meteorological scale, generating a feature matrix as condition data, and building a short-term wind power prediction model based on a CNN-LSTM-CGAN improved condition generative adversarial network; inputting the condition data and the random noise data into a generator together to obtain generated prediction data; inputting the generated prediction data and condition data into a discriminator together, and discriminating with real power data; introducing a feature loss function, measuring the feature value deviation between the generated prediction data and the real data, and updating and optimizing the network parameter weight; if the maximum number of iterations is reached, iteration is terminated, and network parameters are output; and perfo |
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Bibliography: | Application Number: CN202310387839 |