基于粗糙集理论-主成分分析的Elman神经网络短期风速预测

Short-term wind speed forecasting using Elman neural network based on rough set theory and principal components analysis

为了解决传统静态前馈神经网络(FNN)在短期风速预测中易陷入局部最优值及动态性能的不足,引入Elman动态神经网络建立风速预测模型,采用主成分分析法(PCA)对原始风速数据进行特征提取以优化神经网络的输入,改进激励函数和网络结构以寻求函数收敛速度和预测精度的最优解。针对Elman神经网络预测模型在风速波动的峰值处预测误差较大及预测精度存在波动性,提出采用粗糙值理论对模型预测值进行修正与补偿,进一步提高预测精度。实验证明:所提出的方法能有效提高预测精度,增强神经网络模型的泛化能力,具有较好的实用性。

Because the traditional static feed forward neural networks （FNN） are easy to fall into local optimum and lack of dynamic performance, the wind speed prediction model using Elman neural network （ElmanNN） is established, the principal component analysis （PCA） is used to extract the feature of wind speed data, which optimizes the inputs of ElmanNN. Furthermore, excitation function and the structures of network are improved to search for the optimum solution of function convergence rate and prediction accuracy. To solve large error and prediction accuracy fluctuations of the ElmanNN model at the peak value of wind speed, the rough set theory is proposed to compensate and correct the predicted values to further improve the forecasted results. Experimental results show that the prediction accuracy is effectively improved and the generalization ability of ElmanNN is enhanced using the proposed method. This model has precise forecasting and strong practicability, so it has promoted value.