摘要
包含多个预见期的入库径流过程预报误差的随机模拟随着维度增加而难度增大。为了更加精确快速地分析和得到入库径流过程预报误差的变化规律,本文利用变分自编码器(VAE)方法耦合神经网络和低维隐变量的模拟生成复杂高维数据的特性,建立了基于VAE的入库径流过程预报误差随机模拟模型。以锦屏一级水电站的入库径流过程预报误差模拟为例,将以上模型与改进的Gibbs方法的模拟效果进行对比。结果表明,本文模型所得误差序列的均值、标准差、峰度系数等特征统计量更贴近于实际误差序列,且程序运行时间相比改进的Gibbs方法减少了69%~94%,为考虑入库径流预报不确定性的水电站水库调度提供了更为丰富的参考信息。
Stochastic simulations of the errors in reservoir inflow process forecasts with multiple forecast periods become more difficult as the number of dimensions increases.To examine the variation trends of the errors accurately and quickly,we first generate the characteristics of complex high-dimensional data through numerical simulations using the neural network coupled with low-dimensional hidden variables and the Variational AutoEncoders(VAE)method.Then,we develop a stochastic simulation model of the forecast errors of reservoir inflow process based on VAE.This model is compared with the improved Gibbs method in a case study of the JinpingⅠhydropower station.The results show that it gives better agreement of the mean,standard deviation,and variation coefficient with the real error sequence,and its computational time reduces by 69%to 94%compared with the improved Gibbs method.These results provide more information for hydropower station regulation considering uncertainty in reservoir inflow forecast.
作者
张验科
邰雨航
王远坤
马秋梅
ZHANG Yanke;TAI Yuhang;WANG Yuankun;MA Qiumei(School of Water Resources and Hydropower Engineering,North China Electric Power University,Beijing 102206)
出处
《水力发电学报》
CSCD
北大核心
2022年第4期62-70,共9页
Journal of Hydroelectric Engineering
基金
国家自然科学基金项目(41901028)
中央高校基本科研业务费专项资金资助(2021MS041)。
关键词
入库径流
预报误差
多维随机模拟
变分自编码器
改进的Gibbs方法
锦屏一级水电站水库
reservoir inflow
forecast errors
multi-dimensional stochastic simulation
Variational AutoEncoders
improved Gibbs method
JinpingⅠhydropower station
