基于人工神经网络的风电功率预测优化算法

针对BP神经网络容易陷入过拟合和局部极小值的缺陷,采用殖民竞争全局优化算法,将BP神经网络的权值和阈值作为变量,并将均方差作为目标函数,组成了一种新的ICA-BP神经网络算法.结合风电厂的实际数据在Matlab平台上对该方法进行了验证,并与粒子群算法、遗传算法进行比较,得出该算法可以提高风电功率预测精度的结论.;In view of the fact that BP algorithms are fast but they tend to be trapped in local minimums, ICA is employed as a global optimum search algorithm to overcome BP neural network adversities, ANN connection weights are formed as variables of ICA and the Mean Square Error is used as a cost function in ICA, composing the new ICA-BP algorithm. Combined with the actual data of wind power plants on the MATLAB platform to validate the method, and a conclusion is made that this algorithm can improve the precision of wind power forecasting