Short-Term Wind Power Forecasting Based on Attention Mechanism of CNN-LSTM

To improve the prediction accuracy of wind power, in view of the characteristic of intermittent and time-sequence of wind power data, a convolutional neural networks-long short-term memory (abbr. CNN-LSTM) prediction model based on attention mechanism was proposed. Firstly, by use of CNN the multi-dimension feature of dynamic variation of wind power data was extracted. Secondly, the feature vector was constructed into time sequence form and was used as the input of the LSTM network. Finally, the attention mechanism was used to optimize, then by means of endowing different weights to the hidden layer of LSTM to enhance the role of important information, so that the wind power prediction was completed. The wind power data of a certain domestic wind farm is utilized for simulation and simulation results show that the prediction accuracy by the proposed model is higher than those from support vector machine, LSTM model and CNN-LSTM model.