基于混合算法优化神经网络的风电预测模型

Wind Power Forecasting Model Based on Optimized Neural Network of Hybrid Algorithm

针对粒子群算法陷入局部最优以及Elman神经网络泛化能力不足等缺点,提出一种混合小波包分解(wavelet packet decomposition,WPD)和催化粒子群算法(catytic particle swarm optimization,CPSO)优化Elman神经网络(elman neural network,ENN)的短期风电预测方法。通过小波包变换对风电功率样本进行多层序列分解,对单支重构所得的风电功率子序列采用催化粒子群算法优化的神经网络(CPSO-ENN)进行预测,最后叠加各子序列的预测值,得出实际预测结果。在实例分析中,利用某风电场实际运行数据进行仿真验证,结果表明新模型具有较高的预测精度。

In allusion to shortcomings of particle swarm optimization （PSO） algorithm being likely to run into parnal optimization and generalization of Elman neural network being insufficient, this paper presents a kind of method for short-term wind power forecasting based on wavelet packet decomposition （WPD） and catalytic particle swarm optimization （CPSO） algorithm for optimizing Elman neural network （ENN）. This method uses wavelet transform to decompose wind power sam- ples into multi-level sequences, adopts CPSO-ENN optimized by CPSO algorithm for forecasting the sub-sequence of wind power obtained from reconstruction, and finally overlays forecasting value of each sub-sequence to obtain actual forecasting results. Simulating verification for actual operational data of one wind power field indicates that this new model has higher forecasting precision.