عنوان
پدید آورنده
موضوع
رده
کتابخانه
محل استقرار
NATIONAL BIBLIOGRAPHY NUMBER
Number
TL48553
LANGUAGE OF THE ITEM
.Language of Text, Soundtrack etc
انگلیسی
TITLE AND STATEMENT OF RESPONSIBILITY
Title Proper
Neural network based energy management for renewable energy sources
General Material Designation
[Thesis]
First Statement of Responsibility
Samson Gebrewahid
Subsequent Statement of Responsibility
El-Sharkh, Mohamed Y.
.PUBLICATION, DISTRIBUTION, ETC
Name of Publisher, Distributor, etc.
University of South Alabama
Date of Publication, Distribution, etc.
2015
PHYSICAL DESCRIPTION
Specific Material Designation and Extent of Item
74
GENERAL NOTES
Text of Note
Committee members: El-Sharkh, Mohamed Y.; Islam, Samantha; Lazarou, Georgios
NOTES PERTAINING TO PUBLICATION, DISTRIBUTION, ETC.
Text of Note
Place of publication: United States, Ann Arbor; ISBN=978-1-339-28799-7
DISSERTATION (THESIS) NOTE
Dissertation or thesis details and type of degree
M.S.E.E.
Discipline of degree
Electrical and Computer Engineering
Body granting the degree
University of South Alabama
Text preceding or following the note
2015
SUMMARY OR ABSTRACT
Text of Note
As the technology and the population growth, the demand for energy also growth. However, energy resources are scarce. Thus, a smart energy management methodology is essential to prevent wasting a valuable portion of energy while minimizing the operational cost. In this thesis, two types of artificial neural network (ANN) based energy management techniques are developed to manage a proton exchange membrane fuel cell (PEMFC) power plant system while considering the cogenerated thermal energy and hydrogen production and storage. The feedforward and the dynamic neural networks produce the near optimal management decision in a very short time compared to other known management techniques such as evolutionary programming (EP). Comparison of the performance of the ANN with the EP indicates that the ANN produces the near optimal management decision in a fraction of a second while EP takes 3 to 4 hours. Based on the test results, a conclusion can be made that ANN is an excellent online management tool for the PEMFC system or any other renewable energy source.
TOPICAL NAME USED AS SUBJECT
Electrical engineering
UNCONTROLLED SUBJECT TERMS
Subject Term
Applied sciences;Artificial neural network - ann;Energy management;Evolutionary programming;Neural network based energy management;Neural network based energy management for renewable energy sources;Renewable energy sources
PERSONAL NAME - PRIMARY RESPONSIBILITY
Lamey, Emeel S.
PERSONAL NAME - SECONDARY RESPONSIBILITY
El-Sharkh, Mohamed Y.
CORPORATE BODY NAME - SECONDARY RESPONSIBILITY
Subdivision
Electrical and Computer Engineering
University of South Alabama
LOCATION AND CALL NUMBER
Call Number
1748045972; 1604767
ELECTRONIC LOCATION AND ACCESS
Electronic name
مطالعه متن کتاب p
[Thesis]
276903
a
Y
