Sönmez, YusufGüvenç, UğurKahraman, H. TolgaYılmaz, Cemal2020-04-302020-04-302015978-1-4673-6625-0https://doi.org/10.1109/SGCF.2015.7354915https://hdl.handle.net/20.500.12684/23393rd International Istanbul Smart Grid Congress and Fair (ICSG) -- APR 29-30, 2015 -- Istanbul, TURKEYGUVENC, Ugur/0000-0002-5193-7990; sonmez, yusuf/0000-0002-9775-9835; kahraman, hamdi/0000-0001-9985-6324WOS: 000379385200002This 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.en10.1109/SGCF.2015.7354915info:eu-repo/semantics/closedAccesscomponentenergy performance of residential buildingsheating loadcooling loadk-nearest neighborartificial bee colony algorithmgenetic algorithmartificial neural networkConference ObjectN/A