Hourly cooling load prediction of a vehicle in the southern region of Turkey by Artificial Neural Network

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dc.contributor.authorSolmaz, Ozgur
dc.contributor.authorOzgoren, Muammer
dc.contributor.authorAksoy, Muharrem Hilmi
dc.date.accessioned2020-03-26T18:50:59Z
dc.date.available2020-03-26T18:50:59Z
dc.date.issued2014
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractIn this study, Artificial Neural Networks (ANNs) method for prediction hourly cooling load of a vehicle was implemented. The cooling load of the vehicle was calculated along the cooling season (1 May-30 September) for Antalya, Konya, Mersin, Mugla and Sanliurfa provinces in Turkey. For ANN model, seven neurons determinated as input signals of latitude, longitude, altitude, day of the year, hour of the day, hourly mean ambient air temperature and hourly solar radiation were used for the input layer of the network. One neuron producing an output signal of the hourly cooling load was utilized in the output layer. All data were divided into two categories for training and testing of the ANN. The 80% of the data was reserved to training and the remaining was used for testing of the model. Neuron numbers in the hidden layer from 7 to 40 were tested step by step to find the best matching ANN structure. The obtained results for different numbers of neurons were compared in terms of root mean squared error (RMSE), coefficient of determination (R-2) and mean absolute error (MAE). The best matching results for the training and testing were obtained as 8 neurons for the minimum testing RMSE value for the prediction of cooling load by the ANN model on the 23rd day of each month along the cooling season. For the model with 8 neurons RMSE, R-2 and MAE (Training/Testing) were found to be 0.0128/0.0259, 0.9959/0.9818 and 78.81/174.71 W/m(2), respectively. It is shown that the cooling load of a vehicle can be successfully predicted by means of the ANNs from geographical characteristics and meteorological data. (C) 2014 Elsevier Ltd. All rights reserved.en_US
dc.description.sponsorshipCoordinatorship of Selcuk University's Scientific Research Office (BAP)Selcuk University [10101014]en_US
dc.description.sponsorshipThis work was supported by the Coordinatorship of Selcuk University's Scientific Research Office (BAP) Contract No. 10101014. The authors would like to thank to Turkish State Meteorological Service for providing data. This study is prepared as a section of Ozgur Solmaz's Ph.D Thesis.en_US
dc.identifier.doi10.1016/j.enconman.2014.03.017en_US
dc.identifier.endpage187en_US
dc.identifier.issn0196-8904en_US
dc.identifier.issn1879-2227en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage177en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.enconman.2014.03.017
dc.identifier.urihttps://hdl.handle.net/20.500.12395/30905
dc.identifier.volume82en_US
dc.identifier.wosWOS:000336017800018en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPERGAMON-ELSEVIER SCIENCE LTDen_US
dc.relation.ispartofENERGY CONVERSION AND MANAGEMENTen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectAir-conditioningen_US
dc.subjectArtificial Neural Networksen_US
dc.subjectCooling loaden_US
dc.subjectPredictionen_US
dc.subjectVehicleen_US
dc.titleHourly cooling load prediction of a vehicle in the southern region of Turkey by Artificial Neural Networken_US
dc.typeArticleen_US

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