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Comparison of Factorial and Latin Hypercube Sampling Designs for Meta-Models of Building Heating and Cooling Loads

Author

Listed:
  • Younhee Choi

    (Center for Built Environment, Sungkyunkwan University, Suwon 16419, Korea)

  • Doosam Song

    (School of Civil, Architectural Engineering, and Landscape Architecture, Sungkyunkwan University, Suwon 16419, Korea)

  • Sungmin Yoon

    (Division of Architecture and Urban Design, Incheon National University, Incheon 22012, Korea)

  • Junemo Koo

    (Department of Mechanical Engineering, Kyung Hee University, Yongin 17104, Korea)

Abstract

Interest in research analyzing and predicting energy loads and consumption in the early stages of building design using meta-models has constantly increased in recent years. Generally, it requires many simulated or measured results to build meta-models, which significantly affects their accuracy. In this study, Latin Hypercube Sampling (LHS) is proposed as an alternative to Fractional Factor Design (FFD), since it can improve the accuracy while including the nonlinear effect of design parameters with a smaller size of data. Building energy loads of an office floor with ten design parameters were selected as the meta-models’ objectives, and were developed using the two sampling methods. The accuracy of predicting the heating/cooling loads of the meta-models for alternative floor designs was compared. For the considered ranges of design parameters, window insulation (WDI) and Solar Heat Gain Coefficient (SHGC) were found to have nonlinear characteristics on cooling and heating loads. LHS showed better prediction accuracy compared to FFD, since LHS considers the nonlinear impacts for a given number of treatments. It is always a good idea to use LHS over FFD for a given number of treatments, since the existence of nonlinearity in the relation is not pre-existing information.

Suggested Citation

  • Younhee Choi & Doosam Song & Sungmin Yoon & Junemo Koo, 2021. "Comparison of Factorial and Latin Hypercube Sampling Designs for Meta-Models of Building Heating and Cooling Loads," Energies, MDPI, vol. 14(2), pages 1-23, January.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:2:p:512-:d:483204
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    References listed on IDEAS

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    1. Thrampoulidis, Emmanouil & Mavromatidis, Georgios & Lucchi, Aurelien & Orehounig, Kristina, 2021. "A machine learning-based surrogate model to approximate optimal building retrofit solutions," Applied Energy, Elsevier, vol. 281(C).
    2. Sungjin Lee & Soo Cho & Seo-Hoon Kim & Jonghun Kim & Suyong Chae & Hakgeun Jeong & Taeyeon Kim, 2020. "Deep Neural Network Approach for Prediction of Heating Energy Consumption in Old Houses," Energies, MDPI, vol. 14(1), pages 1-14, December.
    3. Tian, Wei, 2013. "A review of sensitivity analysis methods in building energy analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 20(C), pages 411-419.
    4. Yildiz, Yusuf & Korkmaz, Koray & Göksal Özbalta, Türkan & Durmus Arsan, Zeynep, 2012. "An approach for developing sensitive design parameter guidelines to reduce the energy requirements of low-rise apartment buildings," Applied Energy, Elsevier, vol. 93(C), pages 337-347.
    5. 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, Elsevier, vol. 170(C), pages 293-303.
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