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

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası:
  • Doi Numarası: 10.1109/sgcf.2015.7354915
  • Basıldığı Şehir: İstanbul
  • Basıldığı Ülke: Türkiye
  • Anahtar Kelimeler: component, energy performance of residential buildings, heating load, cooling load, k-nearest neighbor, artificial bee colony algorithm, genetic algorithm, artificial neural network
  • Karadeniz Teknik Üniversitesi Adresli: Evet

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.