Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/18174
DC FieldValueLanguage
dc.contributor.authorKalogirou, Soteris A.-
dc.contributor.authorSchizas, Christos N.-
dc.date.accessioned2020-03-27T13:00:50Z-
dc.date.available2020-03-27T13:00:50Z-
dc.date.issued2000-06-
dc.identifier.citationISES Europe Solar Congress, 2000, 19-22 June, Copenhagen, Denmarken_US
dc.identifier.urihttps://hdl.handle.net/20.500.14279/18174-
dc.description.abstractThe objective of this work is to use Artificial Neural Networks (ANN) for the yearly performance prediction of thermosyphonic type solar domestic water heating (SDWH) systems. Twenty-seven SDWH systems have been tested and modelled according to the procedures outlined in the standard ISO 9459-2 at three locations in Greece. These data were used for training and testing the networks. Three neural networks were trained to perform different tasks. The first one predicts the system performance characteristics on a daily basis whereas the other two predict the system long-term performance. The collector areas of the considered systems were varying between 1.81 m{sup 2} and 4.38 m{sup 2}. Open and closed thermosyphonic systems have been considered both with horizontal and vertical storage tanks. In this way the networks were trained to accept and handle a number of unusual cases. From the trained networks three dynamic library link (DLL) files were produced and used in different workbooks of the same spreadsheet, using the CALL function. The results from the first workbook, which are produced from the input data described above, are used in the other two to produce the annual results thus reducing drastically the experimental data required. The method was validated by using actual data for one system, which the networks have not seen before. The accuracy obtained indicate that the prediction of the solar energy output of the system for a draw-off equal to the volume of the storage tank is within 3.5% and for the same parameter and the average quantity of the hot water per month for the two demand water temperatures is within 7.8%. These results are considered satisfactory.en_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.subjectArtificial neural networksen_US
dc.subjectSolar domestic water heatingen_US
dc.titleYearly Performance Prediction of Thermosyphon Domestic Water Heating Systems with Neural Networks and Minimum System Testingen_US
dc.typeConference Papersen_US
dc.collaborationHigher Technical Institute Cyprusen_US
dc.collaborationUniversity of Cyprusen_US
dc.subject.categoryEnvironmental Engineeringen_US
dc.countryCyprusen_US
dc.subject.fieldEngineering and Technologyen_US
dc.publicationPeer Revieweden_US
dc.relation.conferenceISES Europe Solar Congressen_US
cut.common.academicyear1999-2000en_US
item.fulltextNo Fulltext-
item.languageiso639-1en-
item.grantfulltextnone-
item.openairecristypehttp://purl.org/coar/resource_type/c_c94f-
item.cerifentitytypePublications-
item.openairetypeconferenceObject-
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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