Forecasting the Nysa Kłodzka flow rate in order to predict the available flow for a run-off-river (ROR) power plant
1 AGH University, Department of Management Engineering, 30-067, Gramatyka 10 St., Cracow, Poland
2 Institute of Meteorology and Water Management - NRI, 01-673 Warsaw, Podleśna 61, Poland
* Corresponding author: email@example.com
Hydroelectricity is generally perceived as a stable and predictable power source. However ROR power plant without reservoir energy output is mainly driven by changing flow rate. This study applies artificial neural networks to create flow rate forecasts with one hour lead time. Forecasting models were built for Nysa Kłodzka catchment which possesses significant potential for new hydropower plants development as well as leads to frequent floods. The best of the obtained model gives satisfactory results both in terms of root mean square error (0.6379 m3/s) as well as Nash-Sutcliffe performance indicator (0.9978). Obtained results were compared with currently used forecasting models and were proven to be superior.
© The Authors, published by EDP Sciences, 2017
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