Abstract
This paper presents an optimization-based simulation method to evaluate architectural parameters' impacts on the buildings' energy performance in different climate zones. To achieve this goal, a building energy simulator software EnergyPlus has been coupled to the particle swarm optimization algorithm by GenOpt program to determine the decision variables' optimal values. The decision parameters include building orientation, material properties, window size, overhang tilt, green roof type, and phase change MPSaterial type and position. In the optimization process, the impact of each variable and their cumulative impacts has been investigated. This method was applied for a building in different climates of Iran, and the results indicated that 8.92–19.44% of energy saving could be achieved depending on climate conditions. At the same time, the phase change material has the most considerable role in this saving process. The most and least amount of energy saving belongs to cold and hot-humid climates, respectively. This study's findings have revealed that this approach can be applied to indicate the impact of climatic and architectural parameters on buildings' energy-saving potential.
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Abbasizade, F., Abbaspour, M., Soltanieh, M., & Kani, A. (2020). An innovative executive and financial mechanism for energy conservation in new and existing buildings in Iran. International Journal of Environmental Science and Technology. https://doi.org/10.1007/s13762-020-02728-7.
Abbaspour, M., & Abbasizade, F. (2020). Energy performance evaluation based on SDGs. In J. Bishop (Ed.), Encyclopedia of the UN sustainable development goals (pp. 1–15). Cham: Springer Nature.
Amani, N., & Kiaee, E. (2020). Developing a two-criteria framework to rank thermal insulation materials in nearly zero energy buildings using multi-objective optimization approach. Journal of Cleaner Production, 276, 122592. https://doi.org/10.1016/j.jclepro.2020.122592.
Ávila-Hernández, A., Simá, E., Xamán, J., et al. (2020). Test box experiment and simulations of a green-roof: Thermal and energy performance of a residential building standard for Mexico. Energy and Buildings, 209, 109709. https://doi.org/10.1016/j.enbuild.2019.109709.
Bagheri, F., Mokarizadeh, V., & Jabbar, M. (2013). Developing energy performance label for office buildings in Iran. Energy and Buildings, 61, 116–124. https://doi.org/10.1016/j.enbuild.2013.02.022.
Bali, P. N. (2019). Study on thermal properties of bio-PCM candidates in comparison with propylene glycol and salt based PCM for sub-zero energy storage applications. In IOP conference series: materials science and engineering.
Bandara RMPS, Attalage R a (2012) Optimization Methodologies for Building Performance Modelling and Optimization. National Engineering Conference, 18th ERU Symposium 32–37
Bigot, D., Miranville, F., Boyer, H., et al. (2013). Model optimization and validation with experimental data using the case study of a building equipped with photovoltaic panel on roof: Coupling of the building thermal simulation code ISOLAB with the generic optimization program GenOpt. Energy and Buildings, 58, 333–347. https://doi.org/10.1016/j.enbuild.2012.10.017.
Bonab, H. B. (2019). Simulation and optimization of energy consumption systems in buildings in varying climatic conditions. International Journal of Energy and Water Resources. https://doi.org/10.1007/s42108-019-00028-6.
Brown C, Glicksman L, Lehar M (2010) Toward zero energy buildings: Optimized for energy use and cost C. SimBuild 452–457.
Bui, D.-K., Nguyen, T. N., Ghazlan, A., et al. (2020). Enhancing building energy efficiency by adaptive façade: A computational optimization approach. Applied Energy, 265, 114797. https://doi.org/10.1016/j.apenergy.2020.114797.
Delgarm, N., Sajadi, B., Azarbad, K., & Delgarm, S. (2018). Sensitivity analysis of building energy performance: A simulation-based approach using OFAT and variance-based sensitivity analysis methods. Journal of Building Engineering, 15, 181–193. https://doi.org/10.1016/j.jobe.2017.11.020.
Delgarm, N., Sajadi, B., Kowsary, F., & Delgarm, S. (2016). Multi-objective optimization of the building energy performance: A simulation-based approach by means of particle swarm optimization (PSO). Applied Energy, 170, 293–303. https://doi.org/10.1016/j.apenergy.2016.02.141.
DOE (2015) EnergyPlus TM Documentation Getting Started with EnergyPlus Basic Concepts Manual - Essential Information You Need about Running
Ehsan Asadi, Manuel Gameiro da Silva, Carlos Henggeler Antunes, Luís Dias, (2012) A multi-objective optimization model for building retrofit strategies using TRNSYS simulations, GenOpt and MATLAB. Building and Environment 56:370-378
EIA (2016) International Energy Outlook 2016-World energy demand and economc outlook. US Energy Information Administration
Gohari, P. (2019). The influence of building material, windows and insulators on energy saving in different climate zones in Iran. International Journal of Energy and Water Resources, 3, 283–289. https://doi.org/10.1007/s42108-019-00044-6.
IEA (2017) Global Status Report 2017. United Nations Environment Programme (UNEP)
IEA (2018) 2018 World Energy Outlook: Executive Summary. International Energy Agency, Washington
Kheiri, F. (2019). Optimization of building fenestration and shading for climate-based daylight performance using the coupled genetic algorithm and simulated annealing optimization methods. Indoor and Built Environment. https://doi.org/10.1177/1420326X19888008.
Maria A, Enrico F, Joseph F, Marco V (2014) A simulation-based optimization method for cost-optimal analysis of nearly Zero Energy Buildings. Energy & Buildings 84:442–457. https://doi.org/10.1016/j.enbuild.2014.08.031
Nguyen, A., Reiter, S., & Rigo, P. (2014). A review on simulation-based optimization methods applied to building performance analysis. Applied Energy, 113, 1043–1058. https://doi.org/10.1016/j.apenergy.2013.08.061.
Pernodet, F., Lahmidi, H., Keilholz, W., et al. (2011). Development of a multicriteria tool for optimizing the renovation of buildings. Applied Energy, 88, 1386–1394. https://doi.org/10.1016/j.apenergy.2010.10.002.
Pisello, A.L., Rosso, F. (2015). Natural materials for thermal insulation and passive cooling application. In Key engineering materials (pp. 1–16).
Qin, H., & Pan, W. (2020). Energy use of subtropical high-rise public residential buildings and impacts of energy saving measures. Journal of Cleaner Production, 254, 120041. https://doi.org/10.1016/j.jclepro.2020.120041.
Rackes A, Waring MS (2014) Using multiobjective optimizations to discover dynamic building ventilation strategies that can improve indoor air quality and reduce energy use. Energy & Buildings 75:272–280. https://doi.org/10.1016/j.enbuild.2014.02.024
Rosso, F., Ciancio, V., Dell’Olmo, J., & Salata, F. (2020). Multi-objective optimization of building retrofit in the Mediterranean climate by means of genetic algorithm application. Energy and Buildings, 216, 109945. https://doi.org/10.1016/j.enbuild.2020.109945.
Taherahmadi, J., Noorollahi, Y., & Panahi, M. (2020). Toward comprehensive zero energy building definitions: A literature review and recommendations. International Journal of Sustainable Energy. https://doi.org/10.1080/14786451.2020.1796664.
Tuhus-dubrow, D., & Krarti, M. (2010). Genetic-algorithm based approach to optimize building envelope design for residential buildings. Building and Environment, 45, 1574–1581. https://doi.org/10.1016/j.buildenv.2010.01.005.
Víctor Pérez-Andreu, Carolina Aparicio-Fernández, Ana Martínez-Ibernón, José-Luis Vivancos, (2018) Impact of climate change on heating and cooling energy demand in a residential building in a Mediterranean climate. Energy 165:63-74
Vukadinović, A., Radosavljević, J., & Đorđević, A. (2020). Energy performance impact of using phase-change materials in thermal storage walls of detached residential buildings with a sunspace. Solar Energy, 206, 228–244. https://doi.org/10.1016/j.solener.2020.06.008.
Wetter, M. (2011). Generic optimization program user manual. Energy 1998–2011.
Yong, Z., Li-juan, Y., Qian, Z., & Xiao-yan, S. (2020). Multi-objective optimization of building energy performance using a particle swarm optimizer with less control parameters. Journal of Building Engineering, 32, 101505. https://doi.org/10.1016/j.jobe.2020.101505.
Yu, J., Tian, L., Yang, C., et al. (2013). Sensitivity analysis of energy performance for high-rise residential envelope in hot summer and cold winter zone of China. Energy and Buildings, 64, 264–274. https://doi.org/10.1016/j.enbuild.2013.05.018.
Zhang, R., & Lam, K. P. (2011). Coupling of whole-building energy simulation and multi-dimensional numerical optimization for minimizing the life cycle costs of office buildings. Building Thermal, Lighting, and Acoustics Modeling. https://doi.org/10.1007/s12273-013-0128-5.
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Abbasizade, F., Abbaspour, M. Developing an optimization-based simulation approach for building energy performance evaluation (case study: Iran). Int J Energ Water Res 5, 277–286 (2021). https://doi.org/10.1007/s42108-020-00112-2
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DOI: https://doi.org/10.1007/s42108-020-00112-2