AN ESTIMATE OF ENERGY CONSUMPTION FOR HOUSING BUILDINGS IN HOT CLIMATIC ZONES THROUGH ARTIFICAL INTELLIGENCE METHODS: CASE OF ANTALYA

Year 2018, Volume: 13 Issue: 3, 273 - 284, 23.07.2018

Kübra Sümer Haydaraslan , Ersin Haydaraslan Hamdi Tolga Kahraman , Yalçın Yaşar

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

References

• 1. Kaynaklı, O., (2008). A Study on Residential Heating Energy Requirement and Optimum Insulation Thickness. Renewable Energy, Volume:33, pp:1164-1172.
• 2. Chowdhury, A.A., Rasul, M.G., and Khan, M.M.K., (2008). Thermal-Comfort Analysis and Simulation for Various Low-Energy Cooling-Techonologies Applied to an Office Building in a Subtropical Climate. Applied Enegy, Volume:85, pp:449-462.
• 3. Al-Sanea, S. and Zedan M., (2011). Improving Thermal Performance of Building Walls by Optimizing Insulation Layer Distribution and Thickness for Same Thermal Mass. Applied Energy, Volume:88, pp:3113-3124.
• 4. Cheung, C.K., Fuller, R.J., and Luther, M.B., (2005). Energy-Efficient Envelope Design for High-Rise Apartments. Energy and Buildings, Volume:37, pp:37-48.
• 5. Baykal, C., (2014). Binalarda Yönlere Göre Yalıtım Kalınlığının Ekonomikliğinin Araştırılması. Master of Thesis, Natural&Applied Science, Yıldız Technical University, İstanbul.
• 6. Daouas, N., (2011). A Study on Optimum Insulation Thickness in Walls and Energy Savings in Tunisian Buildings Based on Analytical Calculation of Cooling and Heating Transmission Loads. Applied Energy, Volume:88, pp:156-164.
• 7. Tsanas, A. and Xifara, A., (2012). Accurate quantitative Estimation of Energy Performance of Residential Buildings Using Statistical Machine Learning Tools. Energy and Buildings, Volume:49, pp:560-567.
• 8. Maçka, S., Yaşar, Y., and Pehlevan, A., (2011). Investigating of the Effects on Building Energy Consumption and Life Cycle Cost of Building Envelope Alternatives. 12th International Conference on Durability of Building Materials and Component, Porto, Portugal.
• 9. General Directorate of Meteorology, Antalya Average Temperature Data, (2017. htpp://www.mgm.gov.tr.
• 10. Meteonorm, (2017). http://www.meteonorm.com/en/.
• 11. DesignBuilder Software, (2017). http://www.designbuilder.co.uk/.
• 12. Üstün O., (2009). Determination of Activation Functions in A Feedforward Neural Network by using Genetic Algorithm. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, Volume:15, pp:225-234.
• 13. Kahraman, H.T., (2016). A Novel and Powerful Hybrid Classifier Method: Development and Testing of Heuristic k-nn Algorithm with Fuzzy Distance Metric. Data & Knowledge Engineering, Volume:103, pp:44-59.
• 14. Kahraman, H.T., Bayindir, R., and Sagiroglu, S., (2012). Applying A Genetic Algorithm-Based K-Nearest Neighbor Estimator For Estimating The Excitation Current and Parameter Weighting of Synchronous Motors. Energy Conversion and Management, (Submitted for publication) 2012.
• 15. Goldberg, D.E., (1989). Genetic Algorithms in Search Optimization and Machine Learning. Addison Wesley, Reading, pp:41.