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Design optimization of insulation usage and space conditioning load using energy simulation and genetic algorithm

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  • Shi, Xing

Abstract

Architectural design is a process to find the best solution to satisfy various design criteria. To achieve sustainable and green design, performance simulations are often used to verify these criteria and modify the design. The conventional approach of manual trial-and-error is too time-consuming to be practical. Introducing optimization technique can greatly improve the design efficiency and help designers find the optimal design. In this paper, modeFRONTIER was used as the design optimization environment to find the best insulation strategy to minimize the space conditioning load of an office building located in Nanjing, China while keeping the insulation usage at minimum. EnergyPlus was integrated into the optimization tool by writing a DOS batch file to automate the work flow. The search engine was the genetic algorithm and it proved to be able to generate a well-defined Pareto frontier in a reasonable number of runs. Based on the Pareto frontier, the designer can specify his preferences and select the final design. The case study shows that an energy simulation program can be effectively integrated into a design optimization environment to find the optimal design. The technique presented has a broad application in architectural design, especially when the design considerations are multi-objective.

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  • Shi, Xing, 2011. "Design optimization of insulation usage and space conditioning load using energy simulation and genetic algorithm," Energy, Elsevier, vol. 36(3), pages 1659-1667.
    • Handle: RePEc:eee:energy:v:36:y:2011:i:3:p:1659-1667
    DOI: 10.1016/j.energy.2010.12.064
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    References listed on IDEAS

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    2. Bornatico, Raffaele & Hüssy, Jonathan & Witzig, Andreas & Guzzella, Lino, 2013. "Surrogate modeling for the fast optimization of energy systems," Energy, Elsevier, vol. 57(C), pages 653-662.
    3. Hae Jin Kang, 2017. "Development of an Nearly Zero Emission Building (nZEB) Life Cycle Cost Assessment Tool for Fast Decision Making in the Early Design Phase," Energies, MDPI, vol. 10(1), pages 1-21, January.
    4. Wei, Zhongbao & Li, Xiaolu & Xu, Lijun & Cheng, Yanting, 2013. "Comparative study of computational intelligence approaches for NOx reduction of coal-fired boiler," Energy, Elsevier, vol. 55(C), pages 683-692.
    5. Shi, Xing & Tian, Zhichao & Chen, Wenqiang & Si, Binghui & Jin, Xing, 2016. "A review on building energy efficient design optimization rom the perspective of architects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 872-884.
    6. Machairas, Vasileios & Tsangrassoulis, Aris & Axarli, Kleo, 2014. "Algorithms for optimization of building design: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 101-112.
    7. Tafaoli-Masoule, M. & Bahrami, A. & Elsayed, E.M., 2014. "Optimum design parameters and operating condition for maximum power of a direct methanol fuel cell using analytical model and genetic algorithm," Energy, Elsevier, vol. 70(C), pages 643-652.
    8. Rodrigues, Eugénio & Gaspar, Adélio Rodrigues & Gomes, Álvaro, 2014. "Improving thermal performance of automatically generated floor plans using a geometric variable sequential optimization procedure," Applied Energy, Elsevier, vol. 132(C), pages 200-215.
    9. Mardan, Nawzad & Klahr, Roger, 2012. "Combining optimisation and simulation in an energy systems analysis of a Swedish iron foundry," Energy, Elsevier, vol. 44(1), pages 410-419.
    10. Theodoridou, Ifigeneia & Karteris, Marinos & Mallinis, Georgios & Papadopoulos, Agis M. & Hegger, Manfred, 2012. "Assessment of retrofitting measures and solar systems' potential in urban areas using Geographical Information Systems: Application to a Mediterranean city," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(8), pages 6239-6261.
    11. Liu, Min (Max), 2014. "Probabilistic prediction of green roof energy performance under parameter uncertainty," Energy, Elsevier, vol. 77(C), pages 667-674.
    12. Yaolin Lin & Shiquan Zhou & Wei Yang & Chun-Qing Li, 2018. "Design Optimization Considering Variable Thermal Mass, Insulation, Absorptance of Solar Radiation, and Glazing Ratio Using a Prediction Model and Genetic Algorithm," Sustainability, MDPI, vol. 10(2), pages 1-15, January.
    13. Wenjing Li & Zhuoyang Sun & Mehdi Makvandi & Qingchang Chen & Jiayan Fu & Lei Gong & Philip F. Yuan, 2023. "The Use of Normative Energy Calculation beyond the Optimum Retrofit Solutions in Primary Design: A Case Study of Existing Buildings on a Campus," Sustainability, MDPI, vol. 15(9), pages 1-19, April.

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