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dc.contributor.authorYalçıntaş, Melek
dc.contributor.authorAkkurt, Sedat
dc.date.accessioned2016-07-28T12:14:30Z
dc.date.available2016-07-28T12:14:30Z
dc.date.issued2005-08
dc.identifier.citationYalçıntaş, M., and Akkurt, S. (2005). Artificial neural networks applications in building energy predictions and a case study for tropical climates. International Journal of Energy Research, 29(10), 891-901. doi:10.1002/er.1105en_US
dc.identifier.issn0363-907X
dc.identifier.urihttp://doi.org/10.1002/er.1105
dc.identifier.urihttp://hdl.handle.net/11147/2006
dc.description.abstractThis study presents artificial neural network (ANN) methods in building energy use predictions. Applications of the ANN methods in energy audits and energy savings predictions due to building retrofits are emphasized. A generalized ANN model that can be applied to any building type with minor modifications would be a very useful tool for building engineers. ANN methods offer faster learning time, simplicity in analysis and adaptability to seasonal climate variations and changes in the building's energy use when compared to other statistical and simulation models. The model herein is presented for predicting chiller plant energy use in tropical climates with small seasonal and daily variations. It was successfully created based on both climatic and chiller data. The average absolute training error for the model was 9.7% while the testing error was 10.0%. This indicates that the model can successfully predict the particular chiller energy consumption in a tropical climate.en_US
dc.language.isoengen_US
dc.publisherWileyen_US
dc.relation.isversionof10.1002/er.1105en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectArtificial neural networken_US
dc.subjectBuildingen_US
dc.subjectClimateen_US
dc.subjectEnergy useen_US
dc.subjectEnergy utilizationen_US
dc.titleArtificial neural networks applications in building energy predictions and a case study for tropical climatesen_US
dc.typearticleen_US
dc.contributor.authorIDTR3591en_US
dc.contributor.iztechauthorAkkurt, Sedat
dc.relation.journalInternational Journal of Energy Researchen_US
dc.contributor.departmentİYTE, Mühendislik Fakültesi, Makina Mühendisliği Bölümüen_US
dc.identifier.volume29en_US
dc.identifier.issue10en_US
dc.identifier.startpage891en_US
dc.identifier.endpage901en_US


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