IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v10y2018i6p1821-d149987.html
   My bibliography  Save this article

An Occupant-Oriented Calculation Method of Building Interior Cooling Load Design

Author

Listed:
  • Zhaoxia Wang

    (School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China)

  • Yan Ding

    (School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China
    Key Laboratory of Efficient Utilization of Low and Medium Grade Energy, MOE, Tianjin University, Tianjin 300072, China)

  • Huiyan Deng

    (School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China)

  • Fan Yang

    (School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China)

  • Neng Zhu

    (School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China
    Key Laboratory of Efficient Utilization of Low and Medium Grade Energy, MOE, Tianjin University, Tianjin 300072, China)

Abstract

Given continued improvement in the thermal performance of building envelopes, interior disturbances caused by occupant behavior now have the greatest impact on building loads and energy consumption. The accurate calculation of interior load during design stage was emphasized in this paper, and a new method was proposed. Indoor occupants were considered as the core of interior disturbances, and the relationship with other interior disturbances was explored. The interior heat release was arbitrarily combined with the representative cooling load to be utilized in building cooling load calculation. Field surveys were conducted in three typical university buildings: an office building, a teaching building, and a library, located in a university in Tianjin, China. The oversized chillers supplying cooling for the buildings resulted from the over-estimating of the indoor occupant number and the power density of electric appliances. Through quantitative analysis, it was observed that the maximum representative interior loads were 196.43, 329.94, and 402.58 W/person, respectively, for the case buildings, at least 50% less than the empirical design data. Compared to the measured cooling load during the testing period, the accuracy of the modified cooling load was greater than 90%. This research is intended to serve as a reference for calculating and optimizing the design loads of cooling systems.

Suggested Citation

  • Zhaoxia Wang & Yan Ding & Huiyan Deng & Fan Yang & Neng Zhu, 2018. "An Occupant-Oriented Calculation Method of Building Interior Cooling Load Design," Sustainability, MDPI, vol. 10(6), pages 1-29, May.
  • Handle: RePEc:gam:jsusta:v:10:y:2018:i:6:p:1821-:d:149987
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/10/6/1821/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/10/6/1821/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Masini, Andrea & Menichetti, Emanuela, 2012. "The impact of behavioural factors in the renewable energy investment decision making process: Conceptual framework and empirical findings," Energy Policy, Elsevier, vol. 40(C), pages 28-38.
    2. Zhao, Hai-xiang & Magoulès, Frédéric, 2012. "A review on the prediction of building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3586-3592.
    3. Jia, Mengda & Srinivasan, Ravi S. & Raheem, Adeeba A., 2017. "From occupancy to occupant behavior: An analytical survey of data acquisition technologies, modeling methodologies and simulation coupling mechanisms for building energy efficiency," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 525-540.
    4. Kim, Yang-Seon & Heidarinejad, Mohammad & Dahlhausen, Matthew & Srebric, Jelena, 2017. "Building energy model calibration with schedules derived from electricity use data," Applied Energy, Elsevier, vol. 190(C), pages 997-1007.
    5. Labanca, Nicola & Bertoldi, Paolo, 2018. "Beyond energy efficiency and individual behaviours: policy insights from social practice theories," Energy Policy, Elsevier, vol. 115(C), pages 494-502.
    6. Yang, Zheng & Becerik-Gerber, Burcin, 2015. "A model calibration framework for simultaneous multi-level building energy simulation," Applied Energy, Elsevier, vol. 149(C), pages 415-431.
    7. Andrea Masini & E. Menichetti, 2012. "The impact of behavioural factors in the renewable energy investment decision making process: Conceptual framework and empirical findings," Post-Print hal-00651706, HAL.
    8. Soomi Kim & Hyun-ah Kwon, 2018. "Urban Sustainability through Public Architecture," Sustainability, MDPI, vol. 10(4), pages 1-21, April.
    9. Hong, Tianzhen & Yang, Le & Hill, David & Feng, Wei, 2014. "Data and analytics to inform energy retrofit of high performance buildings," Applied Energy, Elsevier, vol. 126(C), pages 90-106.
    10. Zhaoxia Wang & Jing Zhao, 2018. "Optimization of Passive Envelop Energy Efficient Measures for Office Buildings in Different Climate Regions of China Based on Modified Sensitivity Analysis," Sustainability, MDPI, vol. 10(4), pages 1-28, March.
    11. Wang, Zhaoxia & Zhu, Han & Ding, Yan & Zhu, Tianli & Zhu, Neng & Tian, Zhe, 2018. "Energy efficiency evaluation of key energy consumption sectors in China based on a macro-evaluating system," Energy, Elsevier, vol. 153(C), pages 65-79.
    12. López-Rodríguez, M.A. & Santiago, I. & Trillo-Montero, D. & Torriti, J. & Moreno-Munoz, A., 2013. "Analysis and modeling of active occupancy of the residential sector in Spain: An indicator of residential electricity consumption," Energy Policy, Elsevier, vol. 62(C), pages 742-751.
    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. Nishant Raj Kapoor & Ashok Kumar & Tabish Alam & Anuj Kumar & Kishor S. Kulkarni & Paolo Blecich, 2021. "A Review on Indoor Environment Quality of Indian School Classrooms," Sustainability, MDPI, vol. 13(21), pages 1-43, October.

    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. Ali Ghofrani & Esmat Zaidan & Mohsen Jafari, 2021. "Reshaping energy policy based on social and human dimensions: an analysis of human-building interactions among societies in transition in GCC countries," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-26, December.
    2. Bauwens, Thomas, 2019. "Analyzing the determinants of the size of investments by community renewable energy members: Findings and policy implications from Flanders," Energy Policy, Elsevier, vol. 129(C), pages 841-852.
    3. Arnold, Uwe & Yildiz, Özgür, 2015. "Economic risk analysis of decentralized renewable energy infrastructures – A Monte Carlo Simulation approach," Renewable Energy, Elsevier, vol. 77(C), pages 227-239.
    4. Hennessey, Ryan & Pittman, Jeremy & Morand, Annette & Douglas, Allan, 2017. "Co-benefits of integrating climate change adaptation and mitigation in the Canadian energy sector," Energy Policy, Elsevier, vol. 111(C), pages 214-221.
    5. Dirk Johan van Vuuren & Annlizé L. Marnewick & Jan Harm C. Pretorius, 2021. "A Financial Evaluation of a Multiple Inclination, Rooftop-Mounted, Photovoltaic System Where Structured Tariffs Apply: A Case Study of a South African Shopping Centre," Energies, MDPI, vol. 14(6), pages 1-26, March.
    6. Jenner, Steffen & Groba, Felix & Indvik, Joe, 2013. "Assessing the strength and effectiveness of renewable electricity feed-in tariffs in European Union countries," Energy Policy, Elsevier, vol. 52(C), pages 385-401.
    7. Nutkiewicz, Alex & Yang, Zheng & Jain, Rishee K., 2018. "Data-driven Urban Energy Simulation (DUE-S): A framework for integrating engineering simulation and machine learning methods in a multi-scale urban energy modeling workflow," Applied Energy, Elsevier, vol. 225(C), pages 1176-1189.
    8. Toka, Agorasti & Iakovou, Eleftherios & Vlachos, Dimitrios & Tsolakis, Naoum & Grigoriadou, Anastasia-Loukia, 2014. "Managing the diffusion of biomass in the residential energy sector: An illustrative real-world case study," Applied Energy, Elsevier, vol. 129(C), pages 56-69.
    9. Häckel, Björn & Pfosser, Stefan & Tränkler, Timm, 2017. "Explaining the energy efficiency gap - Expected Utility Theory versus Cumulative Prospect Theory," Energy Policy, Elsevier, vol. 111(C), pages 414-426.
    10. Zheng, Xiaotian & Zhou, Youcheng & Iqbal, Sajid, 2022. "Working capital management of SMEs in COVID-19: role of managerial personality traits and overconfidence behavior," Economic Analysis and Policy, Elsevier, vol. 76(C), pages 439-451.
    11. Shahriyar Nasirov & Carlos Silva & Claudio A. Agostini, 2015. "Investors’ Perspectives on Barriers to the Deployment of Renewable Energy Sources in Chile," Energies, MDPI, vol. 8(5), pages 1-21, April.
    12. Zhang, Xinhua & Yang, Hongming & Yu, Qian & Qiu, Jing & Zhang, Yongxi, 2018. "Analysis of carbon-abatement investment for thermal power market in carbon-dispatching mode and policy recommendations," Energy, Elsevier, vol. 149(C), pages 954-966.
    13. Sun, Kaiyu & Hong, Tianzhen & Taylor-Lange, Sarah C. & Piette, Mary Ann, 2016. "A pattern-based automated approach to building energy model calibration," Applied Energy, Elsevier, vol. 165(C), pages 214-224.
    14. Abbasabadi, Narjes & Ashayeri, Mehdi & Azari, Rahman & Stephens, Brent & Heidarinejad, Mohammad, 2019. "An integrated data-driven framework for urban energy use modeling (UEUM)," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    15. Lone Werner & Bert Scholtens, 2017. "Firm Type, Feed-in Tariff, and Wind Energy Investment in Germany: An Investigation of Decision Making Factors of Energy Producers Regarding Investing in Wind Energy Capacity," Journal of Industrial Ecology, Yale University, vol. 21(2), pages 402-411, April.
    16. Shrimali, Gireesh & Nelson, David & Goel, Shobhit & Konda, Charith & Kumar, Raj, 2013. "Renewable deployment in India: Financing costs and implications for policy," Energy Policy, Elsevier, vol. 62(C), pages 28-43.
    17. Blondiau, Yuliya & Reuter, Emmanuelle, 2019. "Why is the grass greener on the other side? Decision modes and location choice by wind energy investors," Journal of Business Research, Elsevier, vol. 102(C), pages 44-55.
    18. Lim, Xin-Le & Lam, Wei-Haur, 2014. "Public Acceptance of Marine Renewable Energy in Malaysia," Energy Policy, Elsevier, vol. 65(C), pages 16-26.
    19. Leszek Dziawgo, 2021. "Energy Sectors on Capital Market – Financing the Process “Towards Sustainability”," European Research Studies Journal, European Research Studies Journal, vol. 0(2B), pages 938-955.
    20. Pedro Paulo Fernandes da Silva & Alberto Hernandez Neto & Ildo Luis Sauer, 2021. "Evaluation of Model Calibration Method for Simulation Performance of a Public Hospital in Brazil," Energies, MDPI, vol. 14(13), pages 1-20, June.

    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:gam:jsusta:v:10:y:2018:i:6:p:1821-:d:149987. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.