摘要
传统的风速预测方法往往通过经验来确定模型结构,未考虑输入变量选取、系统的动态特性等问题,导致系统在不同时间尺度下的动态特性没有得以充分反映,降低模型的推广泛化能力。针对上述问题,提出一种基于流形算法和RBF网络相结合的方法,通过模型结构设计和本质特征提取等方法,增加模型预测结果的稳定性和鲁棒性,以提高模型的推广能力。以华东某风电场数据进行实验分析,结果表明,与传统风速预测方法相比,该模型结构选择方法可提高模型计算效率,降低样本复杂度,能够得到更好的预测效果。
The traditional forecast design method depends on the experiences from the designer,which cannot consider the nature of the wind speed signal changes,it results in the low generality ability of the model structure. Therefore,the RBF neural network in combination with the manifold algorithm is proposed to design the model structure and extract essential features in order to increase the stability and robustness, and improve the forecast accuracy and generality ability.Experimental results using the data from a real wind farm in East China show that,compared with the traditional wind speed forecast methods,the proposed model structure selection method can improve the computing efficiency, reduce sample complexity, and has better forecast effect.
出处
《计算机工程》
CAS
CSCD
北大核心
2017年第11期317-321,共5页
Computer Engineering
关键词
超短期风速预测
模型结构选择
RBF网络
流形算法
机器学习
ultra-short term wind speed forecast
model structure selection
RBF network
manifold algorithm
machine learning
