基于VMD-LSTM的超短期风向多步预测

目的]为准确预测未来4 h风向，提出一种基于VMD-LSTM(Variational Mode Decomposition-Long Short-Term Memory)的超短期风向多步预测方法。方法]采集明阳智能某风电场3个风电机组的风向序列，对其进行预处理及时序分析；基于自相关函数（Autocorrelation Function, ACF）计算风向不同时期的相关性，以选取风向序列的特征长度；采用变分模态分解（Variational Mode Decomposition, VMD）将风向序列分解为相对稳定的模态信号，通过最小样本熵确定分解的子模态数，并对分解后的模态信号分别建立预测模型，进行超短期风向24步预测；重构风向序列，叠加各分量预测结果。结果]结果表明，VMD-LSTM在4个季度的24步风向预测的绝对平均误差（Mean Absolute Error, MAE）、均方根误差（Root Mean Square Error, RMSE）、平均绝对百分比误差（Mean Absolute Percentage Error,MAPE）分别为8.430°、16.870°、9.155...

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.