IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v261y2022ipas0360544222021703.html
   My bibliography  Save this article

Extraction of key parameters and simplification of sub-system energy models using sensitivity analysis in subway stations

Author

Listed:
  • Su, Ziyi
  • Li, Xiaofeng

Abstract

Energy consumption of subway stations has become a focus of scientific research. For energy modeling, complete coverage of inputs makes it difficult to collect information for large-scale engineering application. To this consideration, this study conducted sensitivity analysis (SA) to determine key inputs and simplify energy model of subway stations. Firstly, four commonly-used SA methods (PEAR, SPEA, Morris and Sobol) were compared. Secondly, according to sensitivity indices, key inputs were extracted for ventilation and air-conditioning (VAC), lighting (LIGHT) and vertical transport (TRANS) model. For VAC model, the most important factors are humidity and temperature of the outside air and mechanical fresh air volume, contributing 40%, 33% and 11% of the change on energy simulation results, respectively. For LIGHT model, lighting power density of the hall exerts the greatest impact, with 50% of the change. For TRANS model, key parameters include train departure density and number of passengers, contributing 45% and 32% of the change, respectively. These outcomes provide guidance for engineers in the field of energy-saving operation for subway stations. Eventually, simplified VAC and TRANS energy models were investigated. Results indicate that CV-RMSE for simplified models are within 7.6%. The simplification greatly reduces the workload of data collection and model simulation.

Suggested Citation

  • Su, Ziyi & Li, Xiaofeng, 2022. "Extraction of key parameters and simplification of sub-system energy models using sensitivity analysis in subway stations," Energy, Elsevier, vol. 261(PA).
    • Handle: RePEc:eee:energy:v:261:y:2022:i:pa:s0360544222021703
    DOI: 10.1016/j.energy.2022.125285
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222021703
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.125285?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. 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).
    2. Christoph Aistleitner & Markus Hofer & Robert Tichy, 2012. "A Central Limit Theorem For Latin Hypercube Sampling With Dependence And Application To Exotic Basket Option Pricing," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 15(07), pages 1-20.
    3. Confalonieri, R. & Bellocchi, G. & Bregaglio, S. & Donatelli, M. & Acutis, M., 2010. "Comparison of sensitivity analysis techniques: A case study with the rice model WARM," Ecological Modelling, Elsevier, vol. 221(16), pages 1897-1906.
    4. 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.
    5. Tian, Wei & Heo, Yeonsook & de Wilde, Pieter & Li, Zhanyong & Yan, Da & Park, Cheol Soo & Feng, Xiaohang & Augenbroe, Godfried, 2018. "A review of uncertainty analysis in building energy assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 285-301.
    6. Frédéric Branger & Louis-Gaëtan Giraudet & Céline Guivarch & Philippe Quirion, 2015. "Global sensitivity analysis of an energy-economy model of the residential building sector," Working Papers 2015.06, FAERE - French Association of Environmental and Resource Economists.
    7. Pang, Zhihong & O'Neill, Zheng, 2018. "Uncertainty quantification and sensitivity analysis of the domestic hot water usage in hotels," Applied Energy, Elsevier, vol. 232(C), pages 424-442.
    8. Han, Yongming & Lou, Xiaoyi & Feng, Mingfei & Geng, Zhiqiang & Chen, Liangchao & Ping, Weiying & Lu, Gang, 2022. "Energy consumption analysis and saving of buildings based on static and dynamic input-output models," Energy, Elsevier, vol. 239(PC).
    9. Heiselberg, Per & Brohus, Henrik & Hesselholt, Allan & Rasmussen, Henrik & Seinre, Erkki & Thomas, Sara, 2009. "Application of sensitivity analysis in design of sustainable buildings," Renewable Energy, Elsevier, vol. 34(9), pages 2030-2036.
    10. Hansen, Clifford W. & Helton, Jon C. & Sallaberry, Cédric J., 2012. "Use of replicated Latin hypercube sampling to estimate sampling variance in uncertainty and sensitivity analysis results for the geologic disposal of radioactive waste," Reliability Engineering and System Safety, Elsevier, vol. 107(C), pages 139-148.
    11. 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.
    12. Mavromatidis, Georgios & Orehounig, Kristina & Carmeliet, Jan, 2018. "Uncertainty and global sensitivity analysis for the optimal design of distributed energy systems," Applied Energy, Elsevier, vol. 214(C), pages 219-238.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. 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.
    3. Saryazdi, Seyed mohammad Ebrahimi & Etemad, Alireza & Shafaat, Ali & Bahman, Ammar M., 2024. "A comprehensive review and sensitivity analysis of the factors affecting the performance of buildings equipped with Variable Refrigerant Flow system in Middle East climates," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
    4. 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.
    5. 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.
    6. 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.
    7. Zhang, Hu & Tian, Wei & Tan, Jingyuan & Yin, Juchao & Fu, Xing, 2024. "Sensitivity analysis of multiple time-scale building energy using Bayesian adaptive spline surfaces," Applied Energy, Elsevier, vol. 363(C).
    8. Lu, Yuehong & Wang, Shengwei & Yan, Chengchu & Shan, Kui, 2015. "Impacts of renewable energy system design inputs on the performance robustness of net zero energy buildings," Energy, Elsevier, vol. 93(P2), pages 1595-1606.
    9. 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.
    10. Aurora Greta Ruggeri & Laura Gabrielli & Massimiliano Scarpa, 2020. "Energy Retrofit in European Building Portfolios: A Review of Five Key Aspects," Sustainability, MDPI, vol. 12(18), pages 1-38, September.
    11. Ramos Ruiz, Germán & Fernández Bandera, Carlos & Gómez-Acebo Temes, Tomás & Sánchez-Ostiz Gutierrez, Ana, 2016. "Genetic algorithm for building envelope calibration," Applied Energy, Elsevier, vol. 168(C), pages 691-705.
    12. Stevanović, Sanja, 2013. "Optimization of passive solar design strategies: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 25(C), pages 177-196.
    13. Eduardo Vázquez-López & Federico Garzia & Roberta Pernetti & Jaime Solís-Guzmán & Madelyn Marrero, 2020. "Assessment Model of End-of-Life Costs and Waste Quantification in Selective Demolitions: Case Studies of Nearly Zero-Energy Buildings," Sustainability, MDPI, vol. 12(15), pages 1-18, August.
    14. Scarpa, Federico & Tagliafico, Luca A. & Bianco, Vincenzo, 2021. "Financial and energy performance analysis of efficiency measures in residential buildings. A probabilistic approach," Energy, Elsevier, vol. 236(C).
    15. Huang, He & Wang, Honglei & Hu, Yu-Jie & Li, Chengjiang & Wang, Xiaolin, 2022. "Optimal plan for energy conservation and CO2 emissions reduction of public buildings considering users' behavior: Case of China," Energy, Elsevier, vol. 261(PA).
    16. Ma, Zhenjun & Xia, Lei & Gong, Xuemei & Kokogiannakis, Georgios & Wang, Shugang & Zhou, Xinlei, 2020. "Recent advances and development in optimal design and control of ground source heat pump systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    17. Zhan, Sicheng & Chong, Adrian, 2021. "Data requirements and performance evaluation of model predictive control in buildings: A modeling perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 142(C).
    18. Tian Han & Qiong Huang & Anxiao Zhang & Qi Zhang, 2018. "Simulation-Based Decision Support Tools in the Early Design Stages of a Green Building—A Review," Sustainability, MDPI, vol. 10(10), pages 1-23, October.
    19. Singh, Ramkishore & Lazarus, I.J. & Kishore, V.V.N., 2016. "Uncertainty and sensitivity analyses of energy and visual performances of office building with external venetian blind shading in hot-dry climate," Applied Energy, Elsevier, vol. 184(C), pages 155-170.
    20. Petkov, Ivalin & Mavromatidis, Georgios & Knoeri, Christof & Allan, James & Hoffmann, Volker H., 2022. "MANGOret: An optimization framework for the long-term investment planning of building multi-energy system and envelope retrofits," Applied Energy, Elsevier, vol. 314(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:energy:v:261:y:2022:i:pa:s0360544222021703. 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.journals.elsevier.com/energy .

    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.