A comperative study on novel machine learning algorithms for estimation of energy performance of residential buildings

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 in...

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Bibliographic Details
Published in2015 3rd International Istanbul Smart Grid Congress and Fair (ICSG) pp. 1 - 7
Main Authors Sonmez, Yusuf, Guvenc, Ugur, Kahraman, H. Tolga, Yilmaz, Cemal
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2015
Subjects
artificial bee colony algorithm
Artificial intelligence
artificial neural network
Artificial neural networks
Buildings
Chlorine
cooling load
energy performance of residential buildings
Estimation
genetic algorithm
heating load
k-nearest neighbor
Measurement
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Summary: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.
DOI:10.1109/SGCF.2015.7354915