IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v194y2017icp422-439.html
   My bibliography  Save this article

Developing a meta-model for sensitivity analyses and prediction of building performance for passively designed high-rise residential buildings

Author

Listed:
  • Chen, Xi
  • Yang, Hongxing
  • Sun, Ke

Abstract

This paper aims to develop a green building meta-model for a representative passively designed high-rise residential building in Hong Kong. Modelling experiments are conducted with EnergyPlus to explore a Monte Carlo regression approach, which intends to interpret the relationship between input parameters and output indices of a generic building model and provide reliable building performance predictions. Input parameters are selected from different passive design strategies including the building layout, envelop thermophysics, building geometry and infiltration & air-tightness, while output indices are corresponding indoor environmental indices of the daylight, natural ventilation and thermal comfort to fulfil current green building requirements. The variation of sampling size, application of response transformation and bootstrap method, as well as different statistical regression models are tested and validated through separate modelling datasets. A sampling size of 100 per regression coefficient is determined from the variation of sensitivity coefficients, coefficients of determination and prediction uncertainties. The rank transformation of responses can calibrate sensitivity coefficients of a non-linear model, by considering their variation obtained from sufficient bootstrapping replications. Furthermore, the acquired meta-model with MARS (Multivariate Adaptive Regression Splines) is proved to have better model fitting and predicting performances. This research can accurately identify important architectural design factors and make robust building performance predictions associated with the green building assessment. Sensitivity analysis results and obtained meta-models can improve the efficiency of future optimization studies by pruning the problem space and shorten the computation time.

Suggested Citation

  • Chen, Xi & Yang, Hongxing & Sun, Ke, 2017. "Developing a meta-model for sensitivity analyses and prediction of building performance for passively designed high-rise residential buildings," Applied Energy, Elsevier, vol. 194(C), pages 422-439.
    • Handle: RePEc:eee:appene:v:194:y:2017:i:c:p:422-439
    DOI: 10.1016/j.apenergy.2016.08.180
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261916312892
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2016.08.180?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Mangkuto, Rizki A. & Rohmah, Mardliyahtur & Asri, Anindya Dian, 2016. "Design optimisation for window size, orientation, and wall reflectance with regard to various daylight metrics and lighting energy demand: A case study of buildings in the tropics," Applied Energy, Elsevier, vol. 164(C), pages 211-219.
    2. Ascione, Fabrizio & Bianco, Nicola & De Stasio, Claudio & Mauro, Gerardo Maria & Vanoli, Giuseppe Peter, 2016. "Multi-stage and multi-objective optimization for energy retrofitting a developed hospital reference building: A new approach to assess cost-optimality," Applied Energy, Elsevier, vol. 174(C), pages 37-68.
    3. Chan, A.L.S., 2012. "Effect of adjacent shading on the thermal performance of residential buildings in a subtropical region," Applied Energy, Elsevier, vol. 92(C), pages 516-522.
    4. Méndez Echenagucia, Tomás & Capozzoli, Alfonso & Cascone, Ylenia & Sassone, Mario, 2015. "The early design stage of a building envelope: Multi-objective search through heating, cooling and lighting energy performance analysis," Applied Energy, Elsevier, vol. 154(C), pages 577-591.
    5. Yıldız, Yusuf & Arsan, Zeynep Durmuş, 2011. "Identification of the building parameters that influence heating and cooling energy loads for apartment buildings in hot-humid climates," Energy, Elsevier, vol. 36(7), pages 4287-4296.
    6. 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.
    7. Eicker, Ursula, 2010. "Cooling strategies, summer comfort and energy performance of a rehabilitated passive standard office building," Applied Energy, Elsevier, vol. 87(6), pages 2031-2039, June.
    8. Storlie, Curtis B. & Swiler, Laura P. & Helton, Jon C. & Sallaberry, Cedric J., 2009. "Implementation and evaluation of nonparametric regression procedures for sensitivity analysis of computationally demanding models," Reliability Engineering and System Safety, Elsevier, vol. 94(11), pages 1735-1763.
    9. Loutzenhiser, Peter G. & Maxwell, Gregory M. & Manz, Heinrich, 2007. "An empirical validation of the daylighting algorithms and associated interactions in building energy simulation programs using various shading devices and windows," Energy, Elsevier, vol. 32(10), pages 1855-1870.
    10. Chen, Xi & Yang, Hongxing & Lu, Lin, 2015. "A comprehensive review on passive design approaches in green building rating tools," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 1425-1436.
    11. Fan, Cheng & Xiao, Fu & Wang, Shengwei, 2014. "Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques," Applied Energy, Elsevier, vol. 127(C), pages 1-10.
    12. Huang, Kuo-Tsang & Hwang, Ruey-Lung, 2016. "Future trends of residential building cooling energy and passive adaptation measures to counteract climate change: The case of Taiwan," Applied Energy, Elsevier, vol. 184(C), pages 1230-1240.
    13. Campaniço, Hugo & Hollmuller, Pierre & Soares, Pedro M.M., 2014. "Assessing energy savings in cooling demand of buildings using passive cooling systems based on ventilation," Applied Energy, Elsevier, vol. 134(C), pages 426-438.
    14. Kim, Kihyung & von Spakovsky, Michael R. & Wang, M. & Nelson, Douglas J., 2011. "A hybrid multi-level optimization approach for the dynamic synthesis/design and operation/control under uncertainty of a fuel cell system," Energy, Elsevier, vol. 36(6), pages 3933-3943.
    15. Tian, Wei & Song, Jitian & Li, Zhanyong & de Wilde, Pieter, 2014. "Bootstrap techniques for sensitivity analysis and model selection in building thermal performance analysis," Applied Energy, Elsevier, vol. 135(C), pages 320-328.
    16. Mavromatidis, Lazaros Elias & Marsault, Xavier & Lequay, Hervé, 2014. "Daylight factor estimation at an early design stage to reduce buildings' energy consumption due to artificial lighting: A numerical approach based on Doehlert and Box–Behnken designs," Energy, Elsevier, vol. 65(C), pages 488-502.
    17. Chen, Xi & Yang, Hongxing & Zhang, Weilong, 2015. "A comprehensive sensitivity study of major passive design parameters for the public rental housing development in Hong Kong," Energy, Elsevier, vol. 93(P2), pages 1804-1818.
    18. 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.
    19. Lam, Joseph C. & Tang, H.L. & Li, Danny H.W., 2008. "Seasonal variations in residential and commercial sector electricity consumption in Hong Kong," Energy, Elsevier, vol. 33(3), pages 513-523.
    20. Naji, Sareh & Keivani, Afram & Shamshirband, Shahaboddin & Alengaram, U. Johnson & Jumaat, Mohd Zamin & Mansor, Zulkefli & Lee, Malrey, 2016. "Estimating building energy consumption using extreme learning machine method," Energy, Elsevier, vol. 97(C), pages 506-516.
    21. Fong, K.F. & Lee, C.K., 2012. "Towards net zero energy design for low-rise residential buildings in subtropical Hong Kong," Applied Energy, Elsevier, vol. 93(C), pages 686-694.
    22. Han, Yilong & Taylor, John E. & Pisello, Anna Laura, 2017. "Exploring mutual shading and mutual reflection inter-building effects on building energy performance," Applied Energy, Elsevier, vol. 185(P2), pages 1556-1564.
    23. Chen, Xi & Yang, Hongxing & Sun, Ke, 2016. "A holistic passive design approach to optimize indoor environmental quality of a typical residential building in Hong Kong," Energy, Elsevier, vol. 113(C), pages 267-281.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Prada, A. & Gasparella, A. & Baggio, P., 2018. "On the performance of meta-models in building design optimization," Applied Energy, Elsevier, vol. 225(C), pages 814-826.
    2. Chen, Xi & Yang, Hongxing & Peng, Jinqing, 2019. "Energy optimization of high-rise commercial buildings integrated with photovoltaic facades in urban context," Energy, Elsevier, vol. 172(C), pages 1-17.
    3. Sakiyama, N.R.M. & Carlo, J.C. & Frick, J. & Garrecht, H., 2020. "Perspectives of naturally ventilated buildings: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    4. Edwards, Richard E. & New, Joshua & Parker, Lynne E. & Cui, Borui & Dong, Jin, 2017. "Constructing large scale surrogate models from big data and artificial intelligence," Applied Energy, Elsevier, vol. 202(C), pages 685-699.
    5. Geyer, Philipp & Singaravel, Sundaravelpandian, 2018. "Component-based machine learning for performance prediction in building design," Applied Energy, Elsevier, vol. 228(C), pages 1439-1453.
    6. Chen, Xi & Yang, Hongxing, 2018. "Integrated energy performance optimization of a passively designed high-rise residential building in different climatic zones of China," Applied Energy, Elsevier, vol. 215(C), pages 145-158.
    7. Li, Hangxin & Wang, Shengwei & Cheung, Howard, 2018. "Sensitivity analysis of design parameters and optimal design for zero/low energy buildings in subtropical regions," Applied Energy, Elsevier, vol. 228(C), pages 1280-1291.
    8. Østergård, Torben & Jensen, Rasmus Lund & Maagaard, Steffen Enersen, 2018. "A comparison of six metamodeling techniques applied to building performance simulations," Applied Energy, Elsevier, vol. 211(C), pages 89-103.
    9. Chen, Xi & Yang, Hongxing, 2017. "A multi-stage optimization of passively designed high-rise residential buildings in multiple building operation scenarios," Applied Energy, Elsevier, vol. 206(C), pages 541-557.
    10. Huang, Junchao & Chen, Xi & Yang, Hongxing & Zhang, Weilong, 2018. "Numerical investigation of a novel vacuum photovoltaic curtain wall and integrated optimization of photovoltaic envelope systems," Applied Energy, Elsevier, vol. 229(C), pages 1048-1060.
    11. Wang, Ran & Lu, Shilei & Feng, Wei, 2020. "Impact of adjustment strategies on building design process in different climates oriented by multiple performance," Applied Energy, Elsevier, vol. 266(C).
    12. Wate, P. & Iglesias, M. & Coors, V. & Robinson, D., 2020. "Framework for emulation and uncertainty quantification of a stochastic building performance simulator," Applied Energy, Elsevier, vol. 258(C).
    13. Hwang, Ruey-Lung & Shih, Wen-Mei & Lin, Tzu-Ping & Huang, Kuo-Tsang, 2018. "Simplification and adjustment of the energy consumption indices of office building envelopes in response to climate change," Applied Energy, Elsevier, vol. 230(C), pages 460-470.
    14. Balali, Amirhossein & Yunusa-Kaltungo, Akilu & Edwards, Rodger, 2023. "A systematic review of passive energy consumption optimisation strategy selection for buildings through multiple criteria decision-making techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 171(C).
    15. Prataviera, Enrico & Vivian, Jacopo & Lombardo, Giulia & Zarrella, Angelo, 2022. "Evaluation of the impact of input uncertainty on urban building energy simulations using uncertainty and sensitivity analysis," Applied Energy, Elsevier, vol. 311(C).
    16. Chegari, Badr & Tabaa, Mohamed & Simeu, Emmanuel & Moutaouakkil, Fouad & Medromi, Hicham, 2022. "An optimal surrogate-model-based approach to support comfortable and nearly zero energy buildings design," Energy, Elsevier, vol. 248(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chen, Xi & Yang, Hongxing & Sun, Ke, 2016. "A holistic passive design approach to optimize indoor environmental quality of a typical residential building in Hong Kong," Energy, Elsevier, vol. 113(C), pages 267-281.
    2. Chen, Xi & Yang, Hongxing & Wang, Yuanhao, 2017. "Parametric study of passive design strategies for high-rise residential buildings in hot and humid climates: miscellaneous impact factors," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 442-460.
    3. Chen, Xi & Yang, Hongxing, 2017. "A multi-stage optimization of passively designed high-rise residential buildings in multiple building operation scenarios," Applied Energy, Elsevier, vol. 206(C), pages 541-557.
    4. Chen, Xi & Yang, Hongxing & Zhang, Weilong, 2015. "A comprehensive sensitivity study of major passive design parameters for the public rental housing development in Hong Kong," Energy, Elsevier, vol. 93(P2), pages 1804-1818.
    5. Saurbayeva, Assemgul & Memon, Shazim Ali & Kim, Jong, 2023. "Integrated multi-stage sensitivity analysis and multi-objective optimization approach for PCM integrated residential buildings in different climate zones," Energy, Elsevier, vol. 278(PB).
    6. Tian, Wei & Song, Jitian & Li, Zhanyong & de Wilde, Pieter, 2014. "Bootstrap techniques for sensitivity analysis and model selection in building thermal performance analysis," Applied Energy, Elsevier, vol. 135(C), pages 320-328.
    7. Waqas Ahmed Mahar & Griet Verbeeck & Sigrid Reiter & Shady Attia, 2020. "Sensitivity Analysis of Passive Design Strategies for Residential Buildings in Cold Semi-Arid Climates," Sustainability, MDPI, vol. 12(3), pages 1-22, February.
    8. Chen, Xi & Yang, Hongxing & Peng, Jinqing, 2019. "Energy optimization of high-rise commercial buildings integrated with photovoltaic facades in urban context," Energy, Elsevier, vol. 172(C), pages 1-17.
    9. Naji, Sareh & Aye, Lu & Noguchi, Masa, 2021. "Sensitivity analysis on energy performance, thermal and visual discomfort of a prefabricated house in six climate zones in Australia," Applied Energy, Elsevier, vol. 298(C).
    10. Chen, Xi & Yang, Hongxing, 2018. "Integrated energy performance optimization of a passively designed high-rise residential building in different climatic zones of China," Applied Energy, Elsevier, vol. 215(C), pages 145-158.
    11. Huang, Junchao & Chen, Xi & Yang, Hongxing & Zhang, Weilong, 2018. "Numerical investigation of a novel vacuum photovoltaic curtain wall and integrated optimization of photovoltaic envelope systems," Applied Energy, Elsevier, vol. 229(C), pages 1048-1060.
    12. Chen, Xi & Yang, Hongxing & Lu, Lin, 2015. "A comprehensive review on passive design approaches in green building rating tools," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 1425-1436.
    13. Li, Hangxin & Wang, Shengwei & Cheung, Howard, 2018. "Sensitivity analysis of design parameters and optimal design for zero/low energy buildings in subtropical regions," Applied Energy, Elsevier, vol. 228(C), pages 1280-1291.
    14. Ascione, Fabrizio & De Masi, Rosa Francesca & de Rossi, Filippo & Ruggiero, Silvia & Vanoli, Giuseppe Peter, 2016. "Optimization of building envelope design for nZEBs in Mediterranean climate: Performance analysis of residential case study," Applied Energy, Elsevier, vol. 183(C), pages 938-957.
    15. Shilei Lu & Ran Wang & Shaoqun Zheng, 2017. "Passive Optimization Design Based on Particle Swarm Optimization in Rural Buildings of the Hot Summer and Warm Winter Zone of China," Sustainability, MDPI, vol. 9(12), pages 1-30, December.
    16. Abdo Abdullah Ahmed Gassar & Choongwan Koo & Tae Wan Kim & Seung Hyun Cha, 2021. "Performance Optimization Studies on Heating, Cooling and Lighting Energy Systems of Buildings during the Design Stage: A Review," Sustainability, MDPI, vol. 13(17), pages 1-47, September.
    17. Kočí, Jan & Kočí, Václav & Maděra, Jiří & Černý, Robert, 2019. "Effect of applied weather data sets in simulation of building energy demands: Comparison of design years with recent weather data," Renewable and Sustainable Energy Reviews, Elsevier, vol. 100(C), pages 22-32.
    18. Wang, Ran & Lu, Shilei & Feng, Wei, 2020. "Impact of adjustment strategies on building design process in different climates oriented by multiple performance," Applied Energy, Elsevier, vol. 266(C).
    19. Roberto Robledo-Fava & Mónica C. Hernández-Luna & Pedro Fernández-de-Córdoba & Humberto Michinel & Sonia Zaragoza & A Castillo-Guzman & Romeo Selvas-Aguilar, 2019. "Analysis of the Influence Subjective Human Parameters in the Calculation of Thermal Comfort and Energy Consumption of Buildings," Energies, MDPI, vol. 12(8), pages 1-23, April.
    20. Chen, Jianli & Gao, Xinghua & Hu, Yuqing & Zeng, Zhaoyun & Liu, Yanan, 2019. "A meta-model-based optimization approach for fast and reliable calibration of building energy models," Energy, Elsevier, vol. 188(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:194:y:2017:i:c:p:422-439. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.