Forecasting the Nysa Kłodzka flow rate in order to predict the available flow for a run-off-river (ROR) power plant

Abstract

Abstract. 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 m 3 /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.