基于变分模态分解的机器学习模型择优风速预测系统

Wind Speed Forecasting System Based on Variational Mode Decomposition and the Optimal Machine Learning Models

精准的风速预报对风力发电系统具有重要意义,但风速信号自身固有的随机性使其波动复杂且不可控,以往的研究采用单一或固定的组合模型很难把握风速序列的特征。提出一种基于分解的机器学习模型择优风速预测系统,采用变分模态分解算法降低原始风速序列的复杂度。进而利用模糊神经网络、非线性自回归神经网络、Elman神经网络、反向传播神经网络和自回归差分移动平均模型构成机器学习模型择优系统,分别对子序列的验证集进行预测,通过均方根误差等性能指数选择其最优模型,提高了整体模型的预测精度。试验采用宁夏地区4个站点的实测风速数据,仿真实验结果表明,所提模型相比于单模型以及较新的深度学习组合模型,具有更高的预测精度。

Due to the limited reserves of traditional energy and the excessive use of non-renewable energy,the global carbon emissions seriously accelerate the global greenhouse effect.Compared with traditional energy,wind energy is a kind of green renewable energy.Accurate wind speed prediction is of great significance to wind power generation system,but the inherent randomness of wind speed signal makes its fluctuation complex and uncontrollable.It is difficult to grasp the characteristics of wind speed series using single or fixed hybrid models in previous studies.Therefore,a wind speed forecasting system is proposed based on decomposition and the optimal machine learning models.First,the Variational Mode Decomposition(VMD)algorithm is used to decompose the original wind speed sequence into several sub-sequences.Then,five models of Fuzzy Neural Network(FNN),Nonlinear Auto-regressive Neural Network(NARX),Elman neural network,Back Propagation Neural Network(BPNN)and Auto-regressive integrated Moving Average model(ARIMA),are selected to construct a machine learning model selection system and predict the validation set of sub-sequences,respectively.The root mean square error(RMSE)is used to select the optimal model and the final prediction result is obtained by summing up the predicted values of each sub-sequence.The measured wind speed data from four stations in Ningxia Hui Autonomous Region was used in the experiment.The sampling height was 70 meters and the time interval was 15 minutes.Three groups of comparison models were established to verify the prediction effect of the proposed model.The first group:five single models used in this experiment:FNN,NARX,Elman,BPNN and ARIMA;The second group:five hybrid models based on VMD decomposition:VMD-FNN,VMDNARX,VMD-Elman,VMD-BPNN and VMD-ARIMA;The third group:newer hybrid models of deep learning:Improved Complete Ensemble Empirical Mode Decomposition(ICEEMDAN)-Long Short-Term Memory(LSTM)and ICEEMDAN-Gated Recurrent Unit(GRU).The experimental results indicate that the proposed model is superior to other baseline models.The VMD decomposition method can effectively reduce the complexity of the original wind speed sequence.For different sub-sequences after decomposition,the optimal method of machine learning model can capture the sequence features and obtain the optimal prediction results.