This study both presents a novel methodology and compares the performance of computational intelligence techniques for the predictive modeling of the monthly potential for hydropower generation. Two different approaches are employed to forecasting energy generation: polynomial neural network and conventional artificial neural network (ANN). The first one technique is a deep learning type named group method of data handling (GMDH). And the second one is the multilayer perceptron (MLP) feed forward with back-propagation algorithm. The ANN dealt with two different optimization algorithms for training the model: Levenberg-Marquardt and Bayesian regulation. Rainfall data are used as inputs to feed the models. The performance of each model is scrutinized based on three statistical performance criteria. The results found that the computational intelligence techniques can model the dynamic, seasonal and non-linear behavior of the studied issue. The predictions from the GMDH method resulted in slightly better accuracy than the values obtained by the conventional ANN. The analyzes also showed that the values that determine the steady energy of the hydropower plant are well captured by the models. This feature makes the model an important tool for energy planning and decision making.
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