The optimization of a residential building envelope using particle swarm method in a warm climate
Abstract views: 73
/ 
PDF downloads: 45
DOI:
https://doi.org/10.59287/ijanser.395Keywords:
Cooling Load, Heating Load, Thermal Comfort, Building Orientation, OptimizationAbstract
Energy efficiency in buildings requires an optimization process to reduce energy needs without sacrificing comfort conditions. In this study, a residential building was considered and optimum envelope design options were determined by the particle swarm optimization. Two case were created to evaluate the design options depending on the building orientation. It is shown that the optimum envelope design depending on the building orientation partially affects the energy consumption and has a significant effect on thermal comfort. Finally, it has been shown that the optimum solutions by sensitivity analysis were also applicable in Adana climate conditions.
Downloads
References
Pérez-Lombard L, Ortiz J, Pout C. A review on buildings energy consumption information. Energy Build 2008;40:394–8.
EIGM. TC Enerj İşleri Genel Müdürlüğü 2016. http://www.eigm.gov.tr/tr-TR/DengeTablolari/Denge-Tablolari.
Ferrara M, Filippi M, Sirombo E, Cravino V. A simulation-based optimization method for the integrative design of the building envelope. Energy Procedia 2015;78:2608–13.
Delgarm N, Sajadi B, Kowsary F, Delgarm S. Multi-objective optimization of the building energy performance: A simulation-based approach by means of particle swarm optimization (PSO). Appl Energy 2016;170:293–303.
Tuhus-Dubrow D, Krarti M. Genetic-algorithm based approach to optimize building envelope design for residential buildings. Build Environ 2010;45:1574–81.
Lartigue B, Lasternas B, Loftness V. Multi-objective optimization of building envelope for energy consumption and daylight. Indoor Built Environ 2014;23:70–80.
Yaman K, Arslan G. Analysis of annual energy requirement of a residential house with TS825 and ASHRAE heat balance methods. Turkish J Eng 2017;1:5–10.
Akan AE, Ünal F, Koçyiğit F. Investigation of Energy Saving Potential in Buildings Using Novel Developed Lightweight Concrete. Int J Thermophys 2021;42:4.
Koçyiğit F, Ünal F, Koçyiğit Ş. Experimental analysis and modeling of the thermal conductivities for a novel building material providing environmental transformation. Energy Sources, Part A Recover Util Environ Eff 2020;42:3063–79.
Yaman K, Arslan G. The impact of hourly solar radiation model on building energy analysis in different climatic regions of Turkey. Build Simul 2018;11:483–95.
Kennedy J, Eberhart R. Particle swarm optimization. Proc. ICNN’95 - Int. Conf. Neural Networks, vol. 4, IEEE; 1995, p. 1942–8.
Kennedy J, Eberhart RC. Discrete binary version of the particle swarm algorithm. Proc IEEE Int Conf Syst Man Cybern 1997;5:4104–8.
Ozkaya, U., Seyfi, L. Optimal Rectangular Microstrip Antenna with and without Air Gaps Design by Means of Particle Swarm Optimization and Vortex Search Algorithm. International Journal of Computer and Communication Engineering, 6(1) (2017), 75.
M. Clerc, J. Kennedy, The particle swarm – explosion, stability, and convergence in a multidimensional complex space, IEEE Transactions on Evolutionary Computation 6 (2002) 58–73.
M. Clerc The swarm and the queen: towards a deterministic and adaptive particle swarm optimization Proc. 1999 ICEC, Washington, DC (1999), pp. 1951–1957.
ABD Enerji Bakanlığı bünyesindeki Lawrence Berkeley National Laboratory’de 1998 n.d.
TS825: Thermal Insulation Requirements for Buildings 2013.
Fanger, P.O. 1970. Thermal Comfort-Analysis and Applications in Environmental Engineering, Danish Technical Press, Copenhagen. n.d.
Hamby DM. A review of techniques for parameter sensitivity analysis of environmental models. Environ Monit Assess 1994;32:135–54.
