基于改进AWNN的风电功率超短期多步预测

为提高风电功率超短期多步预测精度,针对梯度修正学习算法采用随机初始化网络参数训练自适应小波神经网络(AWNN)易陷入局部最优的缺点,将粒子群(PSO)算法和差分进化(DE)算法相结合,提出利用IPSO-DE算法优化AWNN的初始化网络参数,得到改进AWNN模型(IAWNN)并将其用于风电功率超短期多步预测。仿真结果表明:IPSO-DE算法优化AWNN初始化网络参数的性能优于IPSO算法、DE算法和梯度修正学习算法,所提改进模型的多步预测性能优于AWNN模型、持续法(PM)模型和BP神经网络(BPNN)模型。

ULTRA-SHORT-TERM WIND POWER MULTI-STEP FORECASTING BASED ON IMPROVED AWNN

In order to improve the accuracy of ultra-short-term wind power multi-step prediction and solve the problem that the gradient correction learning algorithm can easily fall into local optimum when the adaptive wavelet neural network(AWNN)trained by random initialization parameters. An improved model based on the adaptive wavelet neural network(AWNN)and improved PSO-DE algorithm is put forward in this article. The IPSO-DE algorithm is the combination of the improved particle swarm optimization(IPSO)algorithm and the differential evolution(DE)algorithm. It is used to optimize the random initialization parameters of the AWNN. The improved model, which is the IAWNN, is applied to the ultra-short-term wind power multi-step forecasting. The simulation results show the IPSODE algorithm is better than gradient correction learning algorithm, IPSO algorithm and DE algorithm in optimizing the random initialization parameters of the AWNN. The proposed model estimated performance is obviously superior to AWNN model,persistence method(PM)model and BP neural network(BPNN)model.