This thesis demonstrates a new intelligent technique for the online optimal
management of PEM fuel cells units for on site energy production to supply
residential utilizations. Classical optimization techniques were based on offline
calculations and cannot provide the necessary computational speed for online
performance. In this research, a Decision Tree (DT) algorithm was employed to
obtain the optimal, or quasi-optimal, settings of the fuel cell online and in a general
framework. The main idea was to employ a classification technique, trained on a
sufficient subset of data, to produce an estimate of the optimal setting without
repeating the optimization process. A database was extracted from a previously�performed Genetic Algorithm (GA)-based optimization that has been used to create a
suitable decision tree, which was intended for generalizing the optimization results.
The approach provides the flexibility of adjusting the settings of the fuel cell online
according to the observed variations in the tariffs and load demands. Results at
different operating conditions are presented to confirm the high accuracy of the
proposed generalization technique. The accuracy of the decision tree has been tested
by evaluating the relative error with respect to the optimized values. Then, the
possibility of pruning the tree has been investigated in order to simplify its structure
without affecting the accuracy of the results. In addition, the accuracy of the DTs to
approximate the optimal performance of the fuel cell is compared to that of the
Artificial Neural Networks (ANNs) used for the same purpose. The results show that
the DTs can somewhat outperform the ANNs with certain pruning levels
