基于人工神经网络的风电功率预测优化算法  

Artificial Intelligence Algorithm and Its Optimization in the Wind Power Forecasting Based on Nerve Network

作　　者：朱海婷[1] 杨宁[2] 王博[1] 

机构地区：[1]上海电力学院电气工程学院,上海200090 [2]上海电力学院自动化工程学院,上海200090

出　　处：《上海电力学院学报》2014年第3期203-207,222,共6页Journal of Shanghai University of Electric Power

基　　金：国家自然科学基金(60801056);上海市青年科技启明星计划基金(11QA1402800);上海教育委员会科研创新重点项目(11ZZ170)

摘　　要：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.

关 键 词：殖民竞争算法 权值阈值优化 BP神经网络 风电功率预测 

分 类 号：TM614[电气工程—电力系统及自动化] TP183[自动化与计算机技术—控制理论与控制工程]