基于改进EBP神经网络的回水效应水电站流量预测研究

Changyu Liu, Wei Liu
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引用次数: 2

摘要

在水电站优化调度决策系统中,通常对水库流量进行预测。但当回水效应存在时,流量与下游水位和回水通量之间存在复杂的非线性关系。传统的线性插值方法很难得到满意的预测结果。提出了一种基于误差反向传播(EBP)人工神经网络(ANN)的非线性决策方法来建立水库流量预测模型。提出了改进的EBP算法来处理人工神经网络模型的训练。仿真结果表明,与传统的线性插值方法相比,该方法能更好地预测回水效应下的流量。所提出的人工神经网络模型和改进的EBP算法也适用于其他类似系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Study on forecasting discharge of hydropower station with backwater effect based on improved EBP neural network
In optimal dispatching decision system of hydropower station, discharge of reservoir is forecast usually. However the discharge has complicated nonlinear relations with downriver level and backwater flux when backwater effects exist. It is difficult to get satisfactory forecasting results with traditional linear interpolation method. This paper proposes a nonlinear decision-making method based on error back propagation (EBP) artificial neural network (ANN) to establish forecasting discharge model of reservoir. Improved EBP algorithm is presented to process ANN model training. Simulation results show that the proposed method forecasts discharge with the backwater effect better than traditional linear interpolation method. The ANN model and improved EBP algorithm proposed are also applicable to other similar system.
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