An Artificial Neural Network Model for Predicting the Greenhouse Heat Requirement in Adana Climate Conditions

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dc.contributor.authorÇaylı, Alitr
dc.contributor.departmentKahramanmaraş Sütçü İmam Üniversitesi,Ziraat Fakültesitr_TR
dc.date.accessioned2022-01-04T12:42:43Z
dc.date.available2022-01-04T12:42:43Z
dc.date.issued2019-09-01
dc.description.abstractIn addition to the application of modern cultivation techniques and technological solutions, plant quality and yield can be increased through heating in a greenhouse during the cold winter months. Within a greenhouse heating system, the greenhouse heat requirement is the most important parameter for efficient operation. Calculations of the heat requirement should take into consideration the long-term average temperature and the regional climatic conditions. Based on these calculations, the greenhouse heating system power and production costs can be predicted. In this study, an artificial neural network (ANN) model which can be used for planning, feasibility studies, and automation systems was developed to estimate the heat requirement of modern greenhouses. In this model, the performance of the activation and training algorithms was determined with the aim to provide heat requirement estimates that are close to actual consumption values. The fuel consumption data from a commercial greenhouse operation in Adana for the 2015 production year and climatic data from an official meteorological station were used to test the model. By comparing different activation functions and training algorithms, the most suitable algorithm for the model was able to be determined. A total of eight models were then created and their performances compared statistically. As a result, a model that was able to produce estimates that were very close to the actual fuel consumption was developed.tr_TR
dc.identifier.endpage6548tr_TR
dc.identifier.issue9tr_TR
dc.identifier.startpage6537tr_TR
dc.identifier.urihttps://acikerisim.ksu.edu.tr/bitstreams/aba9160e-f7d6-4066-b945-01863f03de41/downloadtr_TR
dc.identifier.volume28tr_TR
dc.language.isoengtr_TR
dc.publisherParlar Scientific Publications (P S P), Angerstr. 12, Freising, Germany, 85354tr_TR
dc.relation.journalFresenius Environmental Bulletintr_TR
dc.subjectArtificial neural networkstr_TR
dc.subjectGreenhouse heatingtr_TR
dc.subjectANNtr_TR
dc.subjectGreenhousestr_TR
dc.titleAn Artificial Neural Network Model for Predicting the Greenhouse Heat Requirement in Adana Climate Conditionstr_TR
dc.typeArticletr_TR

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