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Multistep-ahead streamflow and reservoir level pbkp_rediction using ANNs for production planning in hydroelectric stations

Abstract:

In this work, a methodology to estimate the reservoir level in a hydroelectric dam, based on the pbkp_redicted streamflow and desired active power, is presented. The streamflow is pbkp_redicted using two multistep-ahead pbkp_rediction methods: Close-Loop Pbkp_rediction (CLP) and Open-Loop Pbkp_rediction (OLP). Streamflow pbkp_redictors and dam model are based on Artificial Neural Networks (ANNs). Further analysis of historical streamflow data demonstrated the presence of three climatic seasons and allowed to set the better configuration of ANNs topology and horizons. The pbkp_rediction system was tested in a hydroelectric power plant in Ecuador. In particular, a comparison between the results obtained from the different combinations of streamflow pbkp_redictors and dam model was performed concerning success percentage of pbkp_rediction. Finally, dam model revealed a good accuracy for reservoir level pbkp_redictions when combined with the most promising streamflow pbkp_redictors implemented with CLP method for the summer season.