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

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...

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Main Author:	Mohd Rusllim, Mohamed
Format:	Thesis
Language:	English
Published:	2004
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/2184/ http://umpir.ump.edu.my/id/eprint/2184/1/MOHD_RUSLLIM_BIN_MOHAMED.PDF

id	ump-2184
recordtype	eprints
spelling	ump-21842017-10-25T07:35:34Z http://umpir.ump.edu.my/id/eprint/2184/ Decision tree-based approach for online management of PEM fuel cells for residential application Mohd Rusllim, Mohamed TK Electrical engineering. Electronics Nuclear engineering 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. 2004-10 Thesis NonPeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/2184/1/MOHD_RUSLLIM_BIN_MOHAMED.PDF Mohd Rusllim, Mohamed (2004) Decision tree-based approach for online management of PEM fuel cells for residential application. Masters thesis, Universiti Malaysia Pahang.
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering Mohd Rusllim, Mohamed Decision tree-based approach for online management of PEM fuel cells for residential application
description	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.
format	Thesis
author	Mohd Rusllim, Mohamed
author_facet	Mohd Rusllim, Mohamed
author_sort	Mohd Rusllim, Mohamed
title	Decision tree-based approach for online management of PEM fuel cells for residential application
title_short	Decision tree-based approach for online management of PEM fuel cells for residential application
title_full	Decision tree-based approach for online management of PEM fuel cells for residential application
title_fullStr	Decision tree-based approach for online management of PEM fuel cells for residential application
title_full_unstemmed	Decision tree-based approach for online management of PEM fuel cells for residential application
title_sort	decision tree-based approach for online management of pem fuel cells for residential application
publishDate	2004
url	http://umpir.ump.edu.my/id/eprint/2184/ http://umpir.ump.edu.my/id/eprint/2184/1/MOHD_RUSLLIM_BIN_MOHAMED.PDF
first_indexed	2023-09-18T21:55:44Z
last_indexed	2023-09-18T21:55:44Z
_version_	1777414066192515072

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

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