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- W2896855932 abstract "In order to improve the prediction accuracy of medium and long term wind speed, the deep neural network algorithm is applied to the medium and long term wind speed forecasting in this article. Automatically extract the pattern of wind speed change through powerful nonlinear mapping ability of deep neural networks. In the specific process, take historical wind speed and meteorological as input, deep convolution neural network framework is used to train the model. Through the deep structure of the network, we can learn the internal relationship between the sequences and achieve the prediction of the future wind speed series. The effect of the results is perfect after verification. The prediction result of wind speed in some wind field in Ganhekou of Gansu Province shows, the wind speed prediction deviation of the two models of CNN+MLP and CNN+LSTM are at a relatively low level in most cases. Compared with the traditional weather forecast, improved accuracy of prediction, better acceptance of the abnormal data, and stronger generalization ability of the model. In summary, this method has certain practical value." @default.
- W2896855932 created "2018-10-26" @default.
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- W2896855932 date "2018-08-01" @default.
- W2896855932 modified "2023-09-24" @default.
- W2896855932 title "The Deep Neural Network Algorithm Based on Meteorological Features is in the Medium and Long Term" @default.
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- W2896855932 doi "https://doi.org/10.1109/irce.2018.8492973" @default.
- W2896855932 hasPublicationYear "2018" @default.
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