IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v206y2017icp541-557.html
   My bibliography  Save this article

A multi-stage optimization of passively designed high-rise residential buildings in multiple building operation scenarios

Author

Listed:
  • Chen, Xi
  • Yang, Hongxing

Abstract

This article proposes a two-stage design optimization approach which is applied to a prototype passively designed high-rise residential building under different ventilation modes and thermal load requirements. Machine learning methods are employed to develop surrogate models for improving the computation efficiency of the multi-objective optimization process. The surrogate model is trained by modeling experiments with EnergyPlus and R, which can provide reliable energy performance indicators of generic building models featuring passive design parameters including the building layout, envelope thermophysics, building geometry and infiltration & air-tightness. The lighting, cooling and heating demands of the generic building model are determined by the hybrid ventilation and light diming control strategies in compliance with local green building assessment criteria in Hong Kong. The multiple linear regression (MLR), multivariate adaptive regression splines (MARS), and support vector machines (SVM) are examined by the statistical modelling. SVM is capable of fitting a surrogate model with the best prediction performance based on the coefficient of determination and root mean square error. In addition, both single-sided ventilation and cross-ventilation models under varied thermal load requirements are investigated to compare the preferable design solutions for each scenario. The multi-stage optimization approach is also applied to a Mediterranean climate to explore optimal design solutions in more diverse external environmental conditions. This research can provide a highly efficient design optimization tool to appropriately deploy passive architectural strategies in a green building project.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:206:y:2017:i:c:p:541-557
    DOI: 10.1016/j.apenergy.2017.08.204
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261917312370
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2017.08.204?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. 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.
    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. Rackes, Adams & Melo, Ana Paula & Lamberts, Roberto, 2016. "Naturally comfortable and sustainable: Informed design guidance and performance labeling for passive commercial buildings in hot climates," Applied Energy, Elsevier, vol. 174(C), pages 256-274.
    4. 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.
    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.
    6. 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.
    7. Fan, Cheng & Xiao, Fu & Zhao, Yang, 2017. "A short-term building cooling load prediction method using deep learning algorithms," Applied Energy, Elsevier, vol. 195(C), pages 222-233.
    8. Si, Binghui & Tian, Zhichao & Jin, Xing & Zhou, Xin & Tang, Peng & Shi, Xing, 2016. "Performance indices and evaluation of algorithms in building energy efficient design optimization," Energy, Elsevier, vol. 114(C), pages 100-112.
    9. Takahashi, Akihito & Kurosawa, Takeshi, 2016. "Regression correlation coefficient for a Poisson regression model," Computational Statistics & Data Analysis, Elsevier, vol. 98(C), pages 71-78.
    10. 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.
    11. 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.
    12. Tian, Wei & Song, Jitian & Li, Zhanyong, 2014. "Spatial regression analysis of domestic energy in urban areas," Energy, Elsevier, vol. 76(C), pages 629-640.
    13. Kneifel, Joshua & Webb, David, 2016. "Predicting energy performance of a net-zero energy building: A statistical approach," Applied Energy, Elsevier, vol. 178(C), pages 468-483.
    14. 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.
    15. 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.
    16. 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.
    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. Najjar, Mohammad & Figueiredo, Karoline & Hammad, Ahmed W.A. & Haddad, Assed, 2019. "Integrated optimization with building information modeling and life cycle assessment for generating energy efficient buildings," Applied Energy, Elsevier, vol. 250(C), pages 1366-1382.
    2. Wang, Ran & Lu, Shilei & Feng, Wei, 2020. "A three-stage optimization methodology for envelope design of passive house considering energy demand, thermal comfort and cost," Energy, Elsevier, vol. 192(C).
    3. 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.
    4. 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.
    5. Hossein Atashbar & Esmatullah Noorzai, 2023. "Optimization of Exterior Wall Cladding Materials for Residential Buildings Using the Non-Dominated Sorting Genetic Algorithm II (NSGAII) Based on the Integration of Building Information Modeling (BIM)," Sustainability, MDPI, vol. 15(21), pages 1-20, November.
    6. Flor, Jan-Frederik & Liu, Dingming & Sun, Yanyi & Beccarelli, Paolo & Chilton, John & Wu, Yupeng, 2018. "Optical aspects and energy performance of switchable ethylene-tetrafluoroethylene (ETFE) foil cushions," Applied Energy, Elsevier, vol. 229(C), pages 335-351.
    7. Grant Mosey & Brian Deal, 2020. "Multivariate Optimization in Large-Scale Building Problems: An Architectural and Urban Design Approach for Balancing Social, Environmental, and Economic Sustainability," Sustainability, MDPI, vol. 12(23), pages 1-22, December.
    8. Geyer, Philipp & Singaravel, Sundaravelpandian, 2018. "Component-based machine learning for performance prediction in building design," Applied Energy, Elsevier, vol. 228(C), pages 1439-1453.
    9. 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.
    10. Ding, Yan & Wang, Qiaochu & Kong, Xiangfei & Yang, Kun, 2019. "Multi-objective optimisation approach for campus energy plant operation based on building heating load scenarios," Applied Energy, Elsevier, vol. 250(C), pages 1600-1617.
    11. Razmi, Afshin & Rahbar, Morteza & Bemanian, Mohammadreza, 2022. "PCA-ANN integrated NSGA-III framework for dormitory building design optimization: Energy efficiency, daylight, and thermal comfort," Applied Energy, Elsevier, vol. 305(C).
    12. 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.
    13. Xu, Yizhe & Yan, Chengchu & Yan, Shanhui & Liu, Huifang & Pan, Yan & Zhu, Faxing & Jiang, Yanlong, 2022. "A multi-objective optimization method based on an adaptive meta-model for classroom design with smart electrochromic windows," Energy, Elsevier, vol. 243(C).
    14. Habiba Aly & Omar Abdelaziz, 2024. "Sustainable Design Trends in the Built-Environment Globally and in Egypt: A Literature Review," Sustainability, MDPI, vol. 16(12), pages 1-25, June.
    15. 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).
    16. Ferrara, Maria & Rolfo, Andrea & Prunotto, Federico & Fabrizio, Enrico, 2019. "EDeSSOpt – Energy Demand and Supply Simultaneous Optimization for cost-optimized design: Application to a multi-family building," Applied Energy, Elsevier, vol. 236(C), pages 1231-1248.
    17. Zhao, Zeming & Li, Hangxin & Wang, Shengwei, 2022. "Identification of the key design parameters of Zero/low energy buildings and the impacts of climate and building morphology," Applied Energy, Elsevier, vol. 328(C).
    18. Ngoc-Son Truong & Duc Long Luong & Quang Trung Nguyen, 2023. "BIM to BEM Transition for Optimizing Envelope Design Selection to Enhance Building Energy Efficiency and Cost-Effectiveness," Energies, MDPI, vol. 16(10), pages 1-24, May.

    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, 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.
    2. 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.
    3. 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.
    4. 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).
    5. 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.
    6. 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).
    7. 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.
    8. 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.
    9. 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).
    10. 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.
    11. García Kerdan, Iván & Raslan, Rokia & Ruyssevelt, Paul & Morillón Gálvez, David, 2017. "A comparison of an energy/economic-based against an exergoeconomic-based multi-objective optimisation for low carbon building energy design," Energy, Elsevier, vol. 128(C), pages 244-263.
    12. 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.
    13. Zou, Rongwei & Yang, Qiliang & Xing, Jianchun & Zhou, Qizhen & Xie, Liqiang & Chen, Wenjie, 2024. "Predicting the electric power consumption of office buildings based on dynamic and static hybrid data analysis," Energy, Elsevier, vol. 290(C).
    14. Tian, Wei & Liu, Yunliang & Heo, Yeonsook & Yan, Da & Li, Zhanyong & An, Jingjing & Yang, Song, 2016. "Relative importance of factors influencing building energy in urban environment," Energy, Elsevier, vol. 111(C), pages 237-250.
    15. Venkatraj, V. & Dixit, M.K., 2022. "Challenges in implementing data-driven approaches for building life cycle energy assessment: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    16. Zhang, Xu & Sun, Yongjun & Gao, Dian-ce & Zou, Wenke & Fu, Jianping & Ma, Xiaowen, 2022. "Similarity-based grouping method for evaluation and optimization of dataset structure in machine-learning based short-term building cooling load prediction without measurable occupancy information," Applied Energy, Elsevier, vol. 327(C).
    17. 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.
    18. Li, Xinyi & Yao, Runming, 2020. "A machine-learning-based approach to predict residential annual space heating and cooling loads considering occupant behaviour," Energy, Elsevier, vol. 212(C).
    19. Torres-Rivas, Alba & Palumbo, Mariana & Haddad, Assed & Cabeza, Luisa F. & Jiménez, Laureano & Boer, Dieter, 2018. "Multi-objective optimisation of bio-based thermal insulation materials in building envelopes considering condensation risk," Applied Energy, Elsevier, vol. 224(C), pages 602-614.
    20. Zhang, Qiang & Tian, Zhe & Ma, Zhijun & Li, Genyan & Lu, Yakai & Niu, Jide, 2020. "Development of the heating load prediction model for the residential building of district heating based on model calibration," Energy, Elsevier, vol. 205(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:206:y:2017:i:c:p:541-557. 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.