Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/2484
DC FieldValueLanguage
dc.contributor.authorKalogirou, Soteris A.-
dc.date.accessioned2009-08-26T06:20:22Zen
dc.date.accessioned2013-05-17T05:30:05Z-
dc.date.accessioned2015-12-02T11:27:05Z-
dc.date.available2009-08-26T06:20:22Zen
dc.date.available2013-05-17T05:30:05Z-
dc.date.available2015-12-02T11:27:05Z-
dc.date.issued2002-
dc.identifier.citationWorld Renewable Energy Congress VII, 2002, 29 June – 5 July, Cologne, Germanyen_US
dc.identifier.urihttps://hdl.handle.net/20.500.14279/2484-
dc.description.abstractThe possibility of developing a machine that would “think” has intrigued human beings since ancient times. Artificial intelligence (AI) systems comprise two major areas, expert systems (ES) and artificial neural networks (ANNs). The major objective of this paper is to illustrate how artificial intelligence techniques might play an important role in modelling and prediction of the performance of renewable energy systems. The paper outlines an understanding of how expert systems and neural networks operate by way of presenting a number of problems in the different disciplines of renewable energy engineering. The various applications of expert systems and neural networks are presented in a thematic rather than a chronological or any other order. Results presented in this paper, are testimony to the potential of artificial intelligence as a design tool in many areas of renewable energy engineering.en_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.subjectArtificial intelligenceen_US
dc.subjectArtificial neural networksen_US
dc.subjectRenewable energy systemsen_US
dc.titleArtificial Intelligence in Renewable Energy Systems Modelling and Predictionen_US
dc.typeConference Papersen_US
dc.collaborationHigher Technical Institute Cyprusen_US
dc.subject.categoryEnvironmental Engineeringen_US
dc.countryCyprusen_US
dc.subject.fieldEngineering and Technologyen_US
dc.publicationPeer Revieweden_US
dc.relation.conferenceWorld Renewable Energy Congress VIIen_US
dc.dept.handle123456789/54en
cut.common.academicyear2020-2021en_US
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_c94f-
item.fulltextWith Fulltext-
item.grantfulltextopen-
item.openairetypeconferenceObject-
item.cerifentitytypePublications-
crisitem.author.deptDepartment of Mechanical Engineering and Materials Science and Engineering-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.orcid0000-0002-4497-0602-
crisitem.author.parentorgFaculty of Engineering and Technology-
Appears in Collections:Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation
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