How to Cite: Sümer Haydaraslan, K., Haydaraslan, E., Kahraman, H.T., and Yaşar, Y. (2018). Trabzon Limanı Elleçleme Ekipmanlarının Yakıt Tüketim Maliyetleri Üzerine Bir Araştırm, Technological Applied Sciences (NWSATAS), 13(3): 272-283. DOI:10.12739/NWSA.2018.13.3.2A0157. Technological Applied Sciences Status : Original Study ISSN: 1308 7223 (NWSATAS) Received: November 2017 ID: 2018.13.3.2A0157 Accepted: July 2018 Kübra Sümer Haydaraslan Ersin Haydaraslan H. Tolga Kahraman Yalçın Yaşar Karadeniz Teknik University, Trabzon-Turkey kubrahaydaraslan@ktu.edu.tr; ersin.haydaraslan@erdogan.edu.tr; htolgakahraman@ktu.edu.tr; yyasar@ktu.edu.tr DOI http://dx.doi.org/10.12739/NWSA.2018.13.3.2A0157 ORCID ID 0000-0003-0663-6141 0000-0003-1042-0271 - - CORRESPONDING AUTHOR Kübra Sümer Haydaraslan AN ESTIMATE OF ENERGY CONSUMPTION FOR HOUSING BUILDINGS IN HOT CLIMATIC ZONES THROUGH ARTIFICAL INTELLIGENCE METHODS: CASE OF ANTALYA ABSTRACT Buildings use about one-third of total energy consumed in order to meet their heating and cooling needs. The building envelope that enables to protect it from physical factors in the outer environment is quite effective upon the amount of energy consumed. For the energy efficient solutions, it is necessary to enhance the heating and cooling performance of the building envelope. With this aim, in the study, the energy loads were calculated, which were consumed for heating and cooling by a building established as a reference through a simulation program in the province of Antalya, which respects a hot climatic zone, and the shifts in yearly heating and cooling loads of the alternative models were examined, which were developed by changing the thermal insulation thickness and the window-to-wall area ratio. In the study, the modern, effective artificial intelligence methods were used to enhance the energy performance of multi-dimensional buildings. Of the models for which heating and cooling load calculation had not been made before, the estimates for the thermal loads were made using an energy simulation program, and it has been reached that thermal insulation thickness and window-to-wall area ratio have effect on both loads. Keywords: Thermal Performance, Heating Load, Cooling Load, Thermal Insulation, Artificial Intelligence 1. INTRODUCTION Thanks to economic and social developments, a higher quality of life is offered to building users. The increase in quality of life brings with it more energy consumption. Energy consumption is at the core of today's growth and development plans. According to studies, energy consumption mostly occurs in housing, industry, transportation and agriculture. Energy consumption in housing, in particular, has the biggest share in Turkey as it is in many countries [1]. According to regulations on energy consumption in buildings, it is possible to improve the thermal performance of buildings. It is possible to reduce the energy used in houses by 25-45% with only a few design measures taken [2]. Most of the energy used is consumed for heating and cooling buildings. The amount of energy consumed for thermal requirements can