Neural-network-based water inflow forecasting

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 hyd...

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Bibliographic Details
Published in:Control engineering practice 1998-05, Vol.6 (5), p.593-600
Main Authors: Golob, R., Štokelj, T., Grgič, D.
Format: Article
Language:English
Subjects:
efficiency enhancement
forecasts
Hydroelectric systems
neural networks
power-system control
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Summary: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.
ISSN:0967-0661
1873-6939
DOI:10.1016/S0967-0661(98)00037-9