Please use this identifier to cite or link to this item: https://ktisis.cut.ac.cy/handle/10488/7501
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dc.contributor.authorMellit, Adel-
dc.contributor.authorDrif, Mahmoud-
dc.contributor.authorKalogirou, Soteris A.-
dc.date.accessioned2013-03-04T10:53:59Zen
dc.date.accessioned2013-05-17T10:30:33Z-
dc.date.accessioned2015-12-09T09:40:48Z-
dc.date.available2013-03-04T10:53:59Zen
dc.date.available2013-05-17T10:30:33Z-
dc.date.available2015-12-09T09:40:48Z-
dc.date.issued2010-12-
dc.identifier.citationRenewable Energy, 2010, vol. 35, no. 12, pp. 2881–2893en_US
dc.identifier.issn1879-0682-
dc.description.abstractIn this paper, an artificial neural network-based genetic algorithm (ANN-GA) model was developed for generating the sizing curve of stand-alone photovoltaic (SAPV) systems. Firstly, a numerical method is used for generating the sizing curves for different loss of load probability (LLP) corresponding to 40 sites located in Algeria. The inputs of ANN-GA are the geographical coordinates (Lat, Lon and Alt) and the LLP while the output is the sizing curve represented by CA=f(CS). Subsequently, the proposed ANN-GA model has been trained by using a set of 36 sites, whereas data for 4 sites which are not included in the training dataset have been used for testing the ANN-GA model. The results obtained are compared and tested with those of the numerical method. In addition, two new regression models have been developed and compared with the conventional regression models. The results show that, the proposed exponential regression model with three coefficients presents more accurate results than the conventional regression models. A new ANN has been used for predicting the sizing coefficients for the best regression model. These coefficients can be used for developing the sizing curve in different locations in Algeria. The results obtained showed that the coefficient of multiple determination (R2) is 0.9998, which can be considered as very promisingen_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.relation.ispartofRenewable Energyen_US
dc.rights© Elsevieren_US
dc.subjectNeural networks (Computer science)en_US
dc.subjectGenetic algorithmsen_US
dc.subjectNumber theoryen_US
dc.titleApplication of neural networks and genetic algorithms for sizing of photovoltaic systemsen_US
dc.typeArticleen_US
dc.collaborationCentre de développement des énergies renouvelablesen_US
dc.collaborationCyprus University of Technologyen_US
dc.collaborationJijel Universityen_US
dc.subject.categoryEnvironmental Engineeringen_US
dc.journalsSubscriptionen_US
dc.reviewpeer reviewed-
dc.countryAlgeriaen_US
dc.countryCyprusen_US
dc.subject.fieldEngineering and Technologyen_US
dc.publicationPeer Revieweden_US
dc.identifier.doi10.1016/j.renene.2010.04.017en_US
dc.dept.handle123456789/141en
dc.relation.issue12en_US
dc.relation.volume35en_US
cut.common.academicyear2010-2011en_US
dc.identifier.spage2881en_US
dc.identifier.epage2893en_US
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.openairetypearticle-
item.languageiso639-1en-
item.cerifentitytypePublications-
crisitem.journal.journalissn0960-1481-
crisitem.journal.publisherElsevier-
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-
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