M. Boukadoum, H. Lounis, G. Mai, H. Sahraoui, and V. Siveton (Canada)
forecasting and prediction, knowledge based systems, machine learning, hydro power system.
We compared the performance of an extended Elman neural network vs. that of a tree-based fuzzy regression system when using a database of historical hydrological data to predict the natural contributions flow in a hydroelectric power generation network. The neural network was trained with the Resilient Backpropagation (RPROP) algorithm and the fuzzy regression tree consisted of a new design where input fuzzification is accomplished by using mathematical morphology and output defuzzification is done by a multilayer perceptron (MLP) trained with the backpropagation with momentum algorithm. The purpose of the comparison was to select the best prediction technique to be part of a software framework adapted to hydroelectric power system assessment. The framework uses variable prediction to support rule-based decision processes. Our results are that the best prediction accuracy is obtained with the extended Elman neural network.
Important Links:
Go Back
