摘要
针对中长期径流预报在水库中长期运行方案制定及调度决策形成中的作用,基于传统和智能预报方法各自的优势,利用均生函数模型记忆时间序列的内在规律,采用偏最小二乘方法对预报因子进行降维处理,建立了结合均生函数的神经网络预报模型,并利用神经网络模型修正预报结果。实例计算表明,该模型不仅可提取径流序列的特征,且预报精度也较单一的均生函数模型和神经网络模型有所提高。
Mid-long term runoff forecasting contributes to the establishment of reservoirs operation scheme of mid-long term and the formulation of dispatching decision.Based on the advantages of traditional and intelligent methods,artificial neural network model combined with mean generation function is set up to forecast mid-long term runoff.Inherent law of time series is memorized with mean generation function.And then the partial least squares method is used to reduce the dimension of forecasting factors.Finally,artificial neural network model is applied to correct predicted result.Application example shows that the model can extract the characteristics of runoff series,and the forecasting accuracy is higher than that of single mean generation function model and neural network model as well.
出处
《水电能源科学》
北大核心
2013年第2期19-22,共4页
Water Resources and Power
基金
国家水体污染控制与治理科技重大专项基金资助项目(2009ZX07423-001)
国家自然科学基金资助项目(51179069)
中央高校基本科研业务费专项基金资助项目(10QX43
11QX53
11QX52)
关键词
中长期径流预报
均生函数
偏最小二乘
神经网络
mid-long term runoff forecasting
mean generation function
partial least squares
artificial neural network
