HYDROPOWER RESERVOIRS WATER INFLOW FORECASTING BASED ON ADVANCED RECURRENT NEURAL NETWORK MODELS

Abstract

Accurate water flow forecasting for hydropower reservoirs has become essential for effective water resource management and optimizing plant performance. It helps to mitigate the negative impacts of droughts and floods, ensures stable electricity production, and promotes the efficient use of water resource. This study introduces advanced artificial neural network models designed to address the limitations of traditional statistical methods for water flow forecasting in hydropower reservoirs. To optimize model performance, cross-validation techniques and grid search are employed to identify the best model’s parameter. The data used in this study is the water flow in Sre Pok 4 hydropower reservoir from January 2013 to May 2023. The model performance evaluation includes key metrics such as Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Nash-Sutcliffe Efficiency (NSE). The results show that the combined CNN-LSTM model can predict the water flow with the MAPE of 6.52%