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Design Optimization Considering Variable Thermal Mass, Insulation, Absorptance of Solar Radiation, and Glazing Ratio Using a Prediction Model and Genetic Algorithm

Author

Listed:
  • Yaolin Lin

    (School of Civil Engineering and Architecture, Wuhan University of Technology, Wuhan 430070, China)

  • Shiquan Zhou

    (School of Civil Engineering and Architecture, Wuhan University of Technology, Wuhan 430070, China)

  • Wei Yang

    (College of Engineering and Science, Victoria University, Melbourne 8001, Australia)

  • Chun-Qing Li

    (School of Engineering, RMIT University, Melbourne 3000, Australia)

Abstract

This paper presents the optimization of building envelope design to minimize thermal load and improve thermal comfort for a two-star green building in Wuhan, China. The thermal load of the building before optimization is 36% lower than a typical energy-efficient building of the same size. A total of 19 continuous design variables, including different concrete thicknesses, insulation thicknesses, absorbance of solar radiation for each exterior wall/roof and different window-to-wall ratios for each façade, are considered for optimization. The thermal load and annual discomfort degree hours are selected as the objective functions for optimization. Two prediction models, multi-linear regression (MLR) model and an artificial neural network (ANN) model, are developed to predict the building thermal performance and adopted as fitness functions for a multi-objective genetic algorithm (GA) to find the optimal design solutions. As compared to the original design, the optimal design generated by the MLRGA approach helps to reduce the thermal load and discomfort level by 18.2% and 22.4%, while the reductions are 17.0% and 22.2% respectively, using the ANNGA approach. Finally, four objective functions using cooling load, heating load, summer discomfort degree hours, and winter discomfort degree hours for optimization are conducted, but the results are no better than the two-objective-function optimization approach.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:2:p:336-:d:129114
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    References listed on IDEAS

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    1. Badescu, Viorel & Laaser, Nadine & Crutescu, Ruxandra & Crutescu, Marin & Dobrovicescu, Alexandru & Tsatsaronis, George, 2011. "Modeling, validation and time-dependent simulation of the first large passive building in Romania," Renewable Energy, Elsevier, vol. 36(1), pages 142-157.
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    3. Lin, Yu-Hao & Tsai, Kang-Ting & Lin, Min-Der & Yang, Ming-Der, 2016. "Design optimization of office building envelope configurations for energy conservation," Applied Energy, Elsevier, vol. 171(C), pages 336-346.
    4. Evins, Ralph, 2015. "Multi-level optimization of building design, energy system sizing and operation," Energy, Elsevier, vol. 90(P2), pages 1775-1789.
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    Cited by:

    1. Binghui Si & Zhichao Tian & Wenqiang Chen & Xing Jin & Xin Zhou & Xing Shi, 2018. "Performance Assessment of Algorithms for Building Energy Optimization Problems with Different Properties," Sustainability, MDPI, vol. 11(1), pages 1-22, December.
    2. Karel Struhala & Miroslav Čekon & Richard Slávik, 2018. "Life Cycle Assessment of Solar Façade Concepts Based on Transparent Insulation Materials," Sustainability, MDPI, vol. 10(11), pages 1-16, November.
    3. Leonidas Zouloumis & Georgios Stergianakos & Nikolaos Ploskas & Giorgos Panaras, 2021. "Dynamic Simulation-Based Surrogate Model for the Dimensioning of Building Energy Systems," Energies, MDPI, vol. 14(21), pages 1-13, November.
    4. Seyedeh Farzaneh Mousavi Motlagh & Ali Sohani & Mohammad Djavad Saghafi & Hoseyn Sayyaadi & Benedetto Nastasi, 2021. "The Road to Developing Economically Feasible Plans for Green, Comfortable and Energy Efficient Buildings," Energies, MDPI, vol. 14(3), pages 1-30, January.
    5. Hou, D. & Evins, R., 2024. "A protocol for developing and evaluating neural network-based surrogate models and its application to building energy prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 193(C).
    6. Przemysław Markiewicz-Zahorski & Joanna Rucińska & Małgorzata Fedorczak-Cisak & Michał Zielina, 2021. "Building Energy Performance Analysis after Changing Its Form of Use from an Office to a Residential Building," Energies, MDPI, vol. 14(3), pages 1-24, January.
    7. Małgorzata Fedorczak-Cisak & Katarzyna Nowak & Marcin Furtak, 2019. "Analysis of the Effect of Using External Venetian Blinds on the Thermal Comfort of Users of Highly Glazed Office Rooms in a Transition Season of Temperate Climate—Case Study," Energies, MDPI, vol. 13(1), pages 1-18, December.
    8. Zhai, Yingni & Wang, Yi & Huang, Yanqiu & Meng, Xiaojing, 2019. "A multi-objective optimization methodology for window design considering energy consumption, thermal environment and visual performance," Renewable Energy, Elsevier, vol. 134(C), pages 1190-1199.

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