Decision tree-based approach for online management of PEM fuel cells for residential application

Mohd Rusllim, Mohamed (2004) Decision tree-based approach for online management of PEM fuel cells for residential application. Masters thesis, Universiti Malaysia Pahang (Contributors, UNSPECIFIED: UNSPECIFIED).


Download (955kB)


This thesis demonstrates a new intelligent technique for the online optimal management of PEM fuel cells units for onsite energy production to supply residential utilizations. Classical optimization techniques are based on offline calculations and cannot provide the necessary computational speed for online performance. In this research, a Decision Tree (DT) algorithm is employed to obtain the optimal, or quasioptimal, settings of the fuel cell online and in a general framework. The main idea is 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 is extracted from a previously-performed Genetic Algorithm (GA)-based optimization 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.

Item Type: Thesis (Masters)
Uncontrolled Keywords: Fuel cells Decision trees
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division: Faculty of Electrical & Electronic Engineering
Depositing User: nurudin nasir
Date Deposited: 01 Mar 2012 02:39
Last Modified: 25 Oct 2017 07:35
Download Statistic: View Download Statistics

Actions (login required)

View Item View Item