Please use this identifier to cite or link to this item: https://ktisis.cut.ac.cy/handle/10488/883
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dc.contributor.authorEftekhari, Mahrooen
dc.contributor.authorMarjanovic, Ljiljanaen
dc.contributor.authorKalogirou, Soteris A.-
dc.contributor.authorEftekhari, Mahroo-
dc.contributor.authorMarjanovic, Ljiljana-
dc.contributor.otherΚαλογήρου, Σωτήρης Α.-
dc.date.accessioned2009-08-26T06:36:50Zen
dc.date.accessioned2013-05-17T05:30:05Z-
dc.date.accessioned2015-12-02T11:34:56Z-
dc.date.available2009-08-26T06:36:50Zen
dc.date.available2013-05-17T05:30:05Z-
dc.date.available2015-12-02T11:34:56Z-
dc.date.issued2001en
dc.identifier.citationProceedings of CLIMA 2000 International Conference, Naples, Italy, 2001.en
dc.identifier.urihttp://ktisis.cut.ac.cy/handle/10488/883en
dc.descriptionThis paper is published in the CLIMA 2000 International Conference, Naples, Italy, 2001.en
dc.description.abstractThe objective of this work is to use Artificial Neural Networks for the estimation of the daily heating and cooling loads. The daily loads of nine different building structures have been estimated using the TRNSYS program and a typical meteorological year of Cyprus. This set of data has been used to train a neural network. For each day of the year the maximum and minimum loads were obtained from which heating or cooling loads can be determined. All the buildings considered, had the same areas but different structural characteristics. Single and double walls have been considered as well as a number of different roof insulations. A multislab feedforward architecture having 3 hidden slabs has been employed. Each hidden slab comprised of 36 neurons. For the training data set the R2-values obtained were 0.9896 and 0.9918 for the maximum and minimum loads respectively. The method was validated by using actual (modeled) data for one building, for all days of the year, which the network has not seen before. The R2-values obtained in this case are 0.9885 and 0.9905 for the two types of loads respectively. The results indicate that the proposed method can be used for the required predictions for buildings of different constructions. At present the method was used primarily to investigate its suitability for this kind of predictions.en
dc.formatpdfen
dc.language.isoenen
dc.titleEstimation of the Daily Heating and Cooling Loads Using Artificial Neural Networksen
dc.typeConference Papersen
dc.collaborationHigher Technical Institute Cyprus-
dc.countryCyprus-
dc.dept.handle123456789/54en
item.grantfulltextopen-
item.fulltextWith Fulltext-
item.languageiso639-1other-
crisitem.author.deptDepartment of Mechanical Engineering and Materials Science and Engineering-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.orcid0000-0002-3654-1437-
crisitem.author.parentorgFaculty of Engineering and Technology-
Appears in Collections:Δημοσιεύσεις σε συνέδρια/Conference papers
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