Multi-objective optimization design for windows and shading configuration: considering energy consumption, thermal environment, visual performance and sound insulation effect
- Sichuan Agriculture University, Sichuan, CN
Published in Issue 2021-08-28
How to Cite
Sun, Z., Cao, Y., Wang, X., & Yu, J. (2021). Multi-objective optimization design for windows and shading configuration: considering energy consumption, thermal environment, visual performance and sound insulation effect. International Journal of Energy and Environmental Engineering, 12(4 (December 2021). https://doi.org/10.1007/s40095-021-00413-0
Abstract
Abstract The window and shading configuration is the weak link of heat insulation in the outer protective structure. And it is also an important means of visual performance, which plays an important part in building energy savings. Resulting from the influence of weather and solar radiation, there are contradictions among the energy consumption, visual performance and thermal environment. Therefore, in order to optimize the three factors, an effective optimization method is necessary. For the window design, the existing studies mostly focus on the analysis of energy consumption performance, less on the sound insulation performance. In addition, the optimal configuration of windows and shading system under different climatic regions and orientations has been solved. In this paper, a multi-objective optimization model considering building energy consumption, thermal environment and visual performance was proposed by introducing window orientation, window–wall ratio, window configuration, shading angle and length parameters. And it uses the non-dominated sequencing genetic algorithm NSGA-II and energy simulation software EnergyPlus. The corresponding Pareto solution set was obtained from the assumed room in a cold region, hot summer and cold winter region and hot summer and warm winter region, respectively. The optimal recommended values of window parameters in each direction were determined by analyzing the Pareto solution set. The effectiveness of the multi-objective optimization model is proved by using the linear weighted sum method, and the optimization method of sound insulation effect is discussed. The optimization model in this paper is helpful for designers to choose the optimal design scheme, so that it can comply with the design requirements in terms of energy consumption, thermal environment, visual performance and achieve the overall optimal performance.Keywords
- Multi-objective optimization,
- Windows and shading configuration design,
- Energy consumption,
- Thermal environment,
- Visual performance,
- Sound insulation effect
References
- IEA—International Energy Agency: IEA—International Energy Agency.
- https://www.iea.org
- . Accessed 29 May 2020
- The 13th five-year plan for economic and social development of the People Republic of China.
- http://www.gov.cn/xinwen/2016-03/17/content_5054992.htm
- (2016). Accessed 29 May 2020
- Echenagucia et al. (2015) The early design stage of a building envelope: multi-objective search through heating, cooling and lighting energy performance analysis (pp. 577-591) https://doi.org/10.1016/j.apenergy.2015.04.090
- Mangkuto et al. (2019) Faridah: design optimisation of internal shading device in multiple scenarios: case study in Bandung, Indonesia https://doi.org/10.1016/j.jobe.2019.100745
- Aydn and Mihlayanlar (2020) A case study on the impact of building envelope on energy efficiency in high-rise residential building 13(1) (pp. 5-18) https://doi.org/10.21307/ACEE-2020-001
- Zhai et al. (2019) A multi-objective optimization methodology for window design considering energy consumption, thermal environment and visual performance (pp. 1190-1199) https://doi.org/10.1016/j.renene.2018.09.024
- Sun et al. (2018) A review of transparent insulation material (TIM) for building energy saving and daylight comfort (pp. 713-729) https://doi.org/10.1016/j.apenergy.2018.05.094
- Xiaotu (2010) China Architecture & Building Press
- Manzan (2014) Genetic optimization of external fixed shading devices (pp. 431-440) https://doi.org/10.1016/j.enbuild.2014.01.007
- Lau et al. (2016) Potential of shading devices and glazing configurations on cooling energy savings for high-rise office buildings in hot-humid climates: the case of Malaysia 5(2) (pp. 387-399) https://doi.org/10.1016/j.ijsbe.2016.04.004
- Al-Masrani et al. (2018) Design optimisation of solar shading systems for tropical office buildings: challenges and future trends (pp. 849-872) https://doi.org/10.1016/j.solener.2018.04.047
- Goia et al. (2013) Optimizing the configuration of a façade module for office buildings by means of integrated thermal and lighting simulations in a total energy perspective (pp. 515-527) https://doi.org/10.1016/j.apenergy.2013.02.063
- Alam and Islam (2017) Effect of external shading and window glazing on energy consumption of buildings in Bangladesh 11(2) (pp. 180-192) https://doi.org/10.1080/17512549.2016.1190788
- Khoroshiltseva et al. (2016) A Pareto-based multi-objective optimization algorithm to design energy-efficient shading devices (pp. 1400-1410) https://doi.org/10.1016/j.apenergy.2016.05.015
- Carlucci et al. (2015) Multi-objective optimization of a nearly zero-energy building based on thermal and visual discomfort minimization using a non-dominated sorting genetic algorithm (NSGA-II) (pp. 378-394) https://doi.org/10.1016/j.enbuild.2015.06.064
- Shi et al. (2016) A review on building energy efficient design optimization rom the perspective of architects (pp. 872-884) https://doi.org/10.1016/j.rser.2016.07.050
- Susorova et al. (2013) The effect of geometry factors on fenestration energy performance and energy savings in office buildings (pp. 6-13) https://doi.org/10.1016/j.enbuild.2012.10.035
- Li et al. (2018) Fast bidirectional building performance optimization at the early design stage 004(011) (pp. 647-661) https://doi.org/10.1007/s12273-018-0432-1
- Vallée et al. (2017) Trade-off between sound insulation performance and cost-optimality in a residential nZEB (pp. 57-66) https://doi.org/10.1016/j.egypro.2017.11.123
- Wu et al. (2018) A multi-objective optimization design method in zero energy building study: a case study concerning small mass buildings in cold district of China (pp. 1613-1624) https://doi.org/10.1016/j.enbuild.2017.10.102
- Wang et al. (2020) A three-stage optimization methodology for envelope design of passive house considering energy demand, thermal comfort and cost https://doi.org/10.1016/j.energy.2019.116723
- Bingham et al. (2019) Whole building optimization of a residential home with PV and battery storage in the Bahamas (pp. 1088-1103) https://doi.org/10.1016/j.renene.2018.08.034
- Ochoa et al. (2012) Considerations on design optimization criteria for windows providing low energy consumption and high visual comfort (pp. 238-245) https://doi.org/10.1016/j.apenergy.2012.02.042
- Harmathy et al. (2016) Multi-criterion optimization of building envelope in the function of indoor illumination quality towards overall energy performance improvement (pp. 302-317) https://doi.org/10.1016/j.energy.2016.07.162
- Lee et al. (2013) Optimization of building window system in Asian regions by analyzing solar heat gain and daylighting elements (pp. 522-531) https://doi.org/10.1016/j.renene.2012.07.029
- Kwon et al. (2018) Evaluation of building energy saving through the development of venetian blinds’ optimal control algorithm according to the orientation and window-to-wall ratio 39(2) https://doi.org/10.1007/s10765-017-2350-3
- Zhao and Du (2020) Multi-objective optimization design for windows and shading configuration considering energy consumption and thermal comfort: a case study for office building in different climatic regions of China (pp. 997-1017) https://doi.org/10.1016/j.solener.2020.05.090
- Crawley and Hand (2008) Contrasting the capabilities of building energy performance simulation programs 43(4) (pp. 661-673) https://doi.org/10.1016/j.buildenv.2006.10.027
- Goldstein and Eley (2014) A classification of building energy performance indices 7(2) (pp. 353-375) https://doi.org/10.1007/s12053-013-9248-0
- OpenStudio: OpenStudio.
- https://www.openstudio.net/
- (2020). Accessed 10 May 2020
- Ascione et al. (2019) Retrofit of villas on Mediterranean coastlines: Pareto optimization with a view to energy-efficiency and cost-effectiveness https://doi.org/10.1016/j.apenergy.2019.113705
- Nguyen et al. (2014) A review on simulation-based optimization methods applied to building performance analysis (pp. 1043-1058) https://doi.org/10.1016/j.apenergy.2013.08.061
- Deb et al. (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II 6(2) (pp. 182-197) https://doi.org/10.1109/4235.996017
- Harkouss et al. (2018) Multi-objective optimization methodology for net zero energy buildings (pp. 57-71) https://doi.org/10.1016/j.jobe.2017.12.003
- Delgarm et al. (2016) A novel approach for the simulation-based optimization of the buildings energy consumption using NSGA-II: case study in Iran (pp. 552-560) https://doi.org/10.1016/j.enbuild.2016.05.052
- Gong et al. (2012) Optimization of passive design measures for residential buildings in different Chinese areas (pp. 46-57) https://doi.org/10.1016/j.buildenv.2012.06.014
- Fang and Cho (2019) Design optimization of building geometry and fenestration for daylighting and energy performance (pp. 7-18) https://doi.org/10.1016/j.solener.2019.08.039
- Reinhart et al. (2006) Dynamic daylight performance metrics for sustainable building design 3(1) (pp. 7-31) https://doi.org/10.1582/LEUKOS.2006.03.01.001
- Carlucci et al. (2015) A review of indices for assessing visual comfort with a view to their use in optimization processes to support building integrated design (pp. 1016-1033) https://doi.org/10.1016/j.rser.2015.03.062
- Fanger et al. (1988) Air turbulence and sensation of draught 12(1) (pp. 21-39) https://doi.org/10.1016/0378-7788(88)90053-9
- Yucheng (2004) China Architecture & Building Press
- Protection, M.O.U.A.: Code for design of sound insulation of civil buildings GB50118-2010 (2011)
- Administration, S.E.P.: Environment quality standard for noise GB3096-2008. (2008)
- Srinivas and Patnaik (2002) Genetic algorithms: a survey 6(27) (pp. 17-26) https://doi.org/10.1109/2.294849
- Lei (2005) Xidian University Press
- Ribeiro Filho and Treleaven (1994) Genetic-algorithm programming environments. 27(6) (pp. 28-43) https://doi.org/10.1109/2.294850
- Ascione et al. (2016) Multi-stage and multi-objective optimization for energy retrofitting a developed hospital reference building: a new approach to assess cost-optimality (pp. 37-68) https://doi.org/10.1016/j.apenergy.2016.04.078
- Delgarm et al. (2016) Multi-objective optimization of the building energy performance: a simulation-based approach by means of particle swarm optimization (PSO) (pp. 293-303) https://doi.org/10.1016/j.apenergy.2016.02.141
- Bre et al. (2016) Residential building design optimisation using sensitivity analysis and genetic algorithm (pp. 853-866) https://doi.org/10.1016/j.enbuild.2016.10.025
10.1007/s40095-021-00413-0