A forecast model of short-term wind speed based on the attention mechanism and long short-term memory

Gale is a kind of disaster weather, and the forecast of wind speed is a difficult point in operational weather forecast. In this study, we propose a method to forecast the time series of wind speed in the future period at the target station by using the time series of wind speed in the past period at the target station and its adjacent stations. This method is established by using deep learning technology. Based on the infrastructure of encoder-decoder, the driving series at the adjacent stations and the target series at the target station are taken as the input of the encoder module and the decoder module, respectively. There are two attention layers in the encoder module. One is used to strengthen the contribution of each influence factor in the input driving series to the hidden state in the long short-term memory (LSTM) layer. The other is used to enable the encoder to adaptively select the hidden state output by the LSTM layer. The loss function based on the Gaussian kernel function is adopted in the forecast model of this study, and the dynamic weight is designed to optimize the attention to the errors of the output results at different forecast leading times in the training process of the neural network model, thus improving the model forecast performance for longer forecast leading times. The results show that the performance of this method is excellent in the wind speed forecast from T+1 to T+24. The mean absolute error and root mean squared error of the forecast results at T+24 are 0.796 m <middle dot> s-1 and 1.029 m <middle dot> s-1, respectively, which are better than those of the other two models in the experiment. It is proved that the method proposed in this study can not only be applied to the wind speed forecast but also can provide technical support for operational applications such as early-warning of gale disaster and wind power prediction.