Very Short-Term Wind Direction Multistep Forecast Based on VMD-LSTM

  Introduction  In order to accurately forecast the wind direction in the next 4 hours, a very short-term wind direction multistep forecast algorithm based on VMD-LSTM（Variational Mode Decomposition-Long Short-Term Memory） is proposed.

  Method  Wind direction sequence was collected from 3 wind turbines of a wind farm of Mingyang Smart Energy Group for preprocessing and analysis. The correlation of wind direction in different periods was calculated using the autocorrelation function (ACF) to select the characteristic length of wind direction sequence. Based on variational mode decomposition (VMD), the wind direction sequence was decomposed into relatively intrinsic mode functions, the number of which was determined by minimum sample entropy. Models were build for each intrinsic mode function to make very short-term wind direction 24-step forecast. Finally, the wind direction sequence was reconstructed from the forecasted intrinsic mode functions.

  Result  The results obtained demonstrate that the average MAE（Mean Absolute Error）, RMSE（Root Mean Square Error） and MAPE（Mean Absolute Percentage Error） of the 24-step wind direction forecast based on VMD-LSTM in 4 quarters are 8.430°, 16.870° and 9.155, respectively. The algorithm performs better than other common data modeling methods regarding each error evaluation index at different time scales in each quarter.

  Conclusion  The proposed algorithm can optimize the control yaw angle in the actual production of wind farms.