Neural-network-based water inflow forecasting

被引：29

作者：

Golob, R

Stokelj, T

Grgic, D

机构：

[1] Univ Ljubljana, Fac Elect Engn, Ljubljana 1000, Slovenia

[2] Soske Elektrarne SENG, Nova Gorica 5000, Slovenia

来源：

CONTROL ENGINEERING PRACTICE | 1998年 / 6卷 / 05期

关键词：

hydroelectric systems; forecasts; neural networks; efficiency enhancement; power-system control;

D O I：

10.1016/S0967-0661(98)00037-9

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Water inflow forecasting is usually based on precipitation data collected by the ombrometer stations in the river basin. Solution of this problem is rather complex, due to the highly non-linear relation between the amount of precipitation at different locations and the water inflow into the head hydro power plant reservoir. In this paper, a new approach to forecasting water inflow, based on neural networks, is presented. First, selection of input parameters is discussed. Next, the most appropriate architecture of the neural networks, is chosen. Finally, the efficacy of the proposed method is tested for a practical case, and some results are presented. (C) 1998 Elsevier Science Ltd. AII rights reserved.

引用

页码：593 / 600

页数：8