An ultra-short-term wind power prediction method based on CNN-LSTM

In order to improve the precision of wind power prediction, a convolutional neural networks-long short-term memory combination method for ultra-short term wind power prediction is proposed. First, a CNN-LSTM ultra-short-term wind power prediction model is built. In the CNN-LSTM model, CNN is used for feature processing of wind power data sets, and it is used as the data input of LSTM model, so as to establish a CNN-LSTM fusion prediction model. The effectiveness of the combined model is verified by analyzing the Numerical Weather Prediction data and historical observation data of a wind farm. The proposed model is compared with various comparative models, leading to the important conclusion the combination model has higher prediction accuracy.