Estimation of greenhouse heating requirements using artifical neural networks

dc.contributor.authorYelmen, Bekir
dc.contributor.authorÖztekin, Serdar
dc.date.accessioned2019-06-11T11:45:00Z
dc.date.available2019-06-11T11:45:00Z
dc.date.issued2011
dc.departmentOrtaköy Meslek Yüksekokulu
dc.descriptionYelmen, Bekir (Aksaray, Yazar)
dc.description.abstractIn this study by taking into account the latitude, longitude, height, months and mean temperature data of the city and districts of Adana, the heating need for unit base and surface zone is determined. In the model of artificial neural nets the heating need for the green house which is longitude, latitude, height and means temperature data is used as entry layer and the need for heater need is used as exit layer. Of the data belonging to Adana Province and 7 districts; 6 district education data, Adana Seyhan and yüreğir district are- used as test data in artificial neural net model. The data has been tested by using Levenberg-Marquardt algoritm and an estimate (R2) of value from an average of 99% has been found. The average of quadric error square root value is 0.0533 in average and is 0.0021 for education data. The mean absolute error for test data is 0.0485 and 0.0015 for education data. In conclusion, this study focused on the successful estimate of green house heater need by using the model of artificial neural nets. Konu Alanı:
dc.identifier.endpage386en_US
dc.identifier.issn1306-0007
dc.identifier.issue4en_US
dc.identifier.startpage379en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12451/1231
dc.identifier.volume7en_US
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.publisherTarım Makinaları Derneği
dc.relation.ispartofTarım Makinalar Bilimi Dergisi
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectGreenhouse
dc.subjectHeater Need
dc.subjectArtificial Neural Nets
dc.subjectAdana
dc.subjectYeşil Ev
dc.subjectIsıtıcı İhtiyacı
dc.subjectYapay Sinir Ağları
dc.titleEstimation of greenhouse heating requirements using artifical neural networks
dc.typeArticle

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