A Comperative Study on Novel Machine Learning Algorithms for Estimation of Energy Performance of Residential Buildings
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Dosyalar
Tarih
2015
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Ieee
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
This 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.
Açıklama
3rd International Istanbul Smart Grid Congress and Fair (ICSG) -- APR 29-30, 2015 -- Istanbul, TURKEY
GUVENC, Ugur/0000-0002-5193-7990; sonmez, yusuf/0000-0002-9775-9835; kahraman, hamdi/0000-0001-9985-6324
WOS: 000379385200002
GUVENC, Ugur/0000-0002-5193-7990; sonmez, yusuf/0000-0002-9775-9835; kahraman, hamdi/0000-0001-9985-6324
WOS: 000379385200002
Anahtar Kelimeler
component, energy performance of residential buildings, heating load, cooling load, k-nearest neighbor, artificial bee colony algorithm, genetic algorithm, artificial neural network
Kaynak
2015 3Rd International Istanbul Smart Grid Congress And Fair (Icsg)
WoS Q Değeri
N/A
