Short and mid term hydro power plant reservoir inflow forecasting

In order to improve water management for hydro cascade systems, a new artificial neural network (ANN) approach to forecasting water inflow into the hydro power plant reservoir based on forecasted precipitation data is presented. Water inflow forecasting into the head hydro power plant (HPP) reservoir is one of most important inputs to the cascade hydro system (CHS) optimization process. Ability to properly forecast the increase of natural inflow can result in increased electric energy production due to enhanced flexibility in stored water management. Due to the Soca river torrential character two separate algorithms for short term and mid term water inflow forecasting are designed. Short term water inflow forecasting is based on precipitation data collected by the ombrometer stations in the river basin and is used for forecasts up to 6 hours ahead. The efficacy of the proposed method is tested for a practical case and some results are presented. Mid term water inflow forecasting is based on the forecasted precipitation data and is capable of predicting water inflows for the next two days. The precipitation forecasts data are obtained with the ALADIN (Aire Limitee Adaptation Dynamique development International) program, which was developed by the Meteo-France in cooperation with Slovenian hydrological institute and other Central European hydrological institutes. The data acquisition system to be implemented as a part of new software in the regional control center is briefly described. Finally, some practical results for both short and mid term water inflow forecasting for Soca river are presented.