A Comperative Study on Novel Machine Learning Algorithms for Estimation of Energy Performance of Residential Buildings

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dc.contributor.authorSönmez, Yusuf
dc.contributor.authorGüvenç, Uğur
dc.contributor.authorKahraman, H. Tolga
dc.contributor.authorYılmaz, Cemal
dc.date.accessioned2020-04-30T22:38:39Z
dc.date.available2020-04-30T22:38:39Z
dc.date.issued2015
dc.departmentDÜ, Teknoloji Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.description3rd International Istanbul Smart Grid Congress and Fair (ICSG) -- APR 29-30, 2015 -- Istanbul, TURKEYen_US
dc.descriptionGUVENC, Ugur/0000-0002-5193-7990; sonmez, yusuf/0000-0002-9775-9835; kahraman, hamdi/0000-0001-9985-6324en_US
dc.descriptionWOS: 000379385200002en_US
dc.description.abstractThis study aims to improve the energy performance of residential buildings. heating load (HL) and cooling load (CL) are considered as a measure of heating ventilation and air conditioning (HVAC) system in this process. In order to achive an effective estimation, hybrid machine learning algorithms including, artificial bee colony-based k-nearest neighbor (abc-knn), genetic algorithm-based knn (ga-knn), adaptive artificial neural network with genetic algorithm (ga-ann) and adaptive ann with artificial bee colony (abc-ann) are used. Results are compared classical knn and ann methods. Thence, relations between input and target parameters are defined and performance of well-known classical knn and ann is improved substantialy.en_US
dc.identifier.doi10.1109/SGCF.2015.7354915en_US
dc.identifier.isbn978-1-4673-6625-0
dc.identifier.urihttps://doi.org/10.1109/SGCF.2015.7354915
dc.identifier.urihttps://hdl.handle.net/20.500.12684/2339
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIeeeen_US
dc.relation.ispartof2015 3Rd International Istanbul Smart Grid Congress And Fair (Icsg)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectcomponenten_US
dc.subjectenergy performance of residential buildingsen_US
dc.subjectheating loaden_US
dc.subjectcooling loaden_US
dc.subjectk-nearest neighboren_US
dc.subjectartificial bee colony algorithmen_US
dc.subjectgenetic algorithmen_US
dc.subjectartificial neural networken_US
dc.titleA Comperative Study on Novel Machine Learning Algorithms for Estimation of Energy Performance of Residential Buildingsen_US
dc.typeConference Objecten_US

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