Artificial neural network (ANN) modeling of reservoir operation at Kainji hydropower dam, Nigeria

Abstract

Hydropower dams are one of the many kinds of modifications that human beings make to natural waterways, but they can have especially profound ramifications for riverine ecosystems and the animals that live in them. The operation of hydropower reservoirs in Nigeria does not take into cognizance the ecological situation of the reservoir as it may affect aquatic life that depend on it. This study is sought to model energy generation at Kainji hydropower reservoir, Nigeria considering ecological flow release (EFR) scenarios. Two neural network (NN) approaches were used to optimize the energy generation at the station using the current operating rule and incorporation of EFR scenarios. The study revealed that the correlation coefficients (r) for energy generation without considering EFR using multilayer perceptron neural network (MLPNN) and radial basis neural network (RBFNN) were 0.94 and 0.84, respectively. Average annual energy generation at the station using MLPNN and RBFNN approaches, without considering EFR were 223930 and 222926 MWh, respectively. The two NN modeling approaches were found to perform well in simulating energy generation. The appropriate reservoir operation rule stipulated that the EFR that will not negatively affect energy generation should range between 10 to 25% of reservoir storage.