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, Turkey, 29 - 30 April 2015 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • Doi Number: 10.1109/sgcf.2015.7354915
  • City: İstanbul
  • Country: Turkey
  • Keywords: component, energy performance of residential buildings, heating load, cooling load, k-nearest neighbor, artificial bee colony algorithm, genetic algorithm, artificial neural network
  • Gazi University Affiliated: Yes

Abstract

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.