摘要
针对小水电机组出力预测问题,提出一种基于改进灰狼算法优化自适应相似日选取的小水电预测方法。首先根据小水电的出力规律采用阴历来划分负荷数据,考虑到各因素影响小水电出力的程度是变化的,采用自适应相似日选取方法,并引入改进的灰狼算法来优化各影响因子权重。将筛选出来的相似日样本输入径向基函数(radial basis function,RBF)、反向传播(back propagation,BP)网络这两种单一模型分别进行小水电机组出力预测,并将两个模型的预测结果输入经灰狼算法优化的广义回归神经网络进行非线性组合预测。对某地区进行算例分析,模型相较于单一的BP、RBF和未优化的广义回归神经网络(general regression neural network,GRNN)组合预测模型,平均绝对误差分别降低了3.28%、1.73%和0.29%,验证了模型的有效性。
To predict the output of small hydropower units,a method based on the improved gray wolf algorithm and adaptive similar day selection was proposed.Firstly,the load data was divided by lunar calendar according to the output law of small hydropower.Considering that the influence of various factors on the output of small hydropower was variable,the adaptive similar day selection method and an optimization strategy for the weights of each impact factor based on the improved grey wolf optimization algorithm were employed.Then,the selected similar daily samples were fed into radial basis function(RBF)and back propagation(BP)network for small hydropower units’output forecasting,respectively.Later,the above predicted results were input into the generalized regression neural network(GRNN)optimized by the grey wolf optimization for nonlinear combination forecasting.By the analysis of an example in a certain area,the mean absolute error of the proposed prediction model is reduced by 3.28%,1.73%and 0.29%respectively,compared with that of the BP,RBF and non-optimized GRNN combined prediction model,which verifies the validity of the proposed model.
作者
王凌云
王舟盼
安晓
赵魏
WANG Ling-yun;WANG Zhou-pan;AN Xiao;ZHAO Wei(College of Electrical Engineering&New Energy,China Three Gorges University,Yichang 443002,China;State Grid Hubei Power Transmission and Transformation Engineering Co.,Ltd.,Wuhan 430061,China)
出处
《科学技术与工程》
北大核心
2021年第5期1832-1839,共8页
Science Technology and Engineering
基金
国家自然科学基金(61603212)。
关键词
小水电机组出力
自适应相似日
改进灰狼算法
广义回归神经网络
组合预测
small hydropower unit output
adaptive similar day
improved grey wolf algorithm
generalized regression neural network
combination forecast
