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


SÖNMEZ Y., GÜVENÇ U., KAHRAMAN H. T. , YILMAZ C.

3rd International Istanbul Smart Grid Congress and Fair (ICSG), İstanbul, Türkiye, 29 - 30 Nisan 2015 identifier identifier

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.