摘要
针对水电站上网日电量数据的非线性特征,采用状态相依自回归(state-dependent autoregressive,SD-AR)模型对数据进行描述。用高斯径向基函数(radial basis function,RBF)神经网络来逼近SD-AR模型的函数型系数,采用一种结构化非线性参数优化方法 (structured nonlinear parameter optimization method,SNPOM)来离线辨识RBF-AR模型系数,并利用此模型对数据进行预测。通过对三峡左岸和右岸水电站上网日电量真实数据进行训练和测试,并与其他经典算法进行比较,验证了RBF-AR模型在不确定条件下的预测准确性和可行性。
According to the nonlinear characteristics of the daily grid electricity data of hydropower stations,the state-dependent auto-regressive（ SD-AR） model is used to describe this data. In this study,the Gaussian radial basis function（ RBF） networks are used to approximate the functional coefficients of SD-AR model,and the parameters of the RBF-AR model is estimated by anoffline structured nonlinear parameter optimization method（SNPOM）. After that,this model is applied to predict the data. Through the train and test for the real data of daily grid electricity of hydropower stationsin the left bank and right bank of Three Gorges as well as comparison with other classical algorithms,the forecasting accuracy and feasibility of RBF-AR model are verified.
作者
徐文权
胡慧
卓张华
杨伟
XU Wenquan;HU Hui;ZHUO Zhanghua;YANG Wei(School of Physics and Electrical Engineering, Anqing Normal University, Anqing 246001, China;School of Education, Anqing Normal University, Anqing 246001, China;Cascade dispatching communication center of Three Gorges Project, China Yangtze Power Co. , Ltd, Yichang 443002, China)
出处
《河海大学学报（自然科学版）》
CAS
CSCD
北大核心
2018年第3期275-282,共8页
Journal of Hohai University(Natural Sciences)
基金
安徽省教育厅一般项目(AQSK2014B002)
安庆师范大学校青年基金(SK201208
KJ201315)
关键词
状态相依模型
上网电量预测
非线性系统
RBF-AR模型
参数优化
state dependent model
the grid electricity forecasting
nonlinear system
RBF-AR model
parameter optimization
