IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v10y2018i10p3777-d176790.html
   My bibliography  Save this article

A System to Pre-Evaluate the Suitability of Energy-Saving Technology for Green Buildings

Author

Listed:
  • Shilei Lu

    (School of Environmental Science and Engineering, Tianjin University, Tianjin 300350, China)

  • Minchao Fan

    (School of Environmental Science and Engineering, Tianjin University, Tianjin 300350, China)

  • Yiqun Zhao

    (School of Environmental Science and Engineering, Tianjin University, Tianjin 300350, China)

Abstract

Rating systems for green buildings often give assessments from the perspective of the overall performance of a single building or architecture complex but rarely target specific green building technologies. As some of the rating systems are scored according to whether the technologies are used or not, some developers tend to pile up energy-saving technologies blindly just for the sake of certifications without considering their suitability for the application. Such behavior may lead to the failure of achieving the energy goals for green buildings. To solve this problem, a system that pre-evaluates the suitability of green building energy-saving technologies is devised based on modified TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method, SA (simulated annealing) algorithm and unascertained theory-based data analysis method. By setting indices from technology performance, economy, human satisfaction aspects and by using the building prior information and measured database of technology usage, this system can make a quantifiable and multi-dimensional grading assessment for the target green building energy-saving technologies in the design stage. The system aims at helping the designer choose technologies in the design phase that best enhance the performance of the finished green building. It also helps prevent the sub-optimal performance of unsuitable technologies caused by the “pile up” behavior mentioned earlier. To verify this evaluation system, two building designs which use energy-recovery technology are evaluated, and the predicted performance for both designs matched the actual operation of the technology in the buildings themselves well.

Suggested Citation

  • Shilei Lu & Minchao Fan & Yiqun Zhao, 2018. "A System to Pre-Evaluate the Suitability of Energy-Saving Technology for Green Buildings," Sustainability, MDPI, vol. 10(10), pages 1-19, October.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:10:p:3777-:d:176790
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/10/10/3777/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/10/10/3777/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. A.M. Fogheri, 2015. "Energy Efficiency in Public Buildings," Rivista economica del Mezzogiorno, Società editrice il Mulino, issue 3-4, pages 763-784.
    2. Zhou, Juan & Chen, Youming, 2010. "A review on applying ventilated double-skin facade to buildings in hot-summer and cold-winter zone in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(4), pages 1321-1328, May.
    3. Perpiña Castillo, Carolina & Batista e Silva, Filipe & Lavalle, Carlo, 2016. "An assessment of the regional potential for solar power generation in EU-28," Energy Policy, Elsevier, vol. 88(C), pages 86-99.
    4. Kuo, Ting, 2017. "A modified TOPSIS with a different ranking index," European Journal of Operational Research, Elsevier, vol. 260(1), pages 152-160.
    5. 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.
    6. Rouleau, Jean & Gosselin, Louis & Blanchet, Pierre, 2018. "Understanding energy consumption in high-performance social housing buildings: A case study from Canada," Energy, Elsevier, vol. 145(C), pages 677-690.
    7. Hugh Byrd & Eziaku Onyeizu Rasheed, 2016. "The Productivity Paradox in Green Buildings," Sustainability, MDPI, vol. 8(4), pages 1-12, April.
    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. Cristino, T.M. & Lotufo, F.A. & Delinchant, B. & Wurtz, F. & Faria Neto, A., 2021. "A comprehensive review of obstacles and drivers to building energy-saving technologies and their association with research themes, types of buildings, and geographic regions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    2. Nan Yang & Weixiu Shi & Zihong Zhou, 2023. "Research on Application and International Policy of Renewable Energy in Buildings," Sustainability, MDPI, vol. 15(6), pages 1-25, March.
    3. Zhenmin Yuan & Jianliang Zhou & Yaning Qiao & Yadi Zhang & Dandan Liu & Hui Zhu, 2020. "BIM-VE-Based Optimization of Green Building Envelope from the Perspective of both Energy Saving and Life Cycle Cost," Sustainability, MDPI, vol. 12(19), pages 1-16, September.
    4. Su, Yuan & Wang, Linwei & Feng, Wei & Zhou, Nan & Wang, Luyuan, 2021. "Analysis of green building performance in cold coastal climates: An in-depth evaluation of green buildings in Dalian, China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(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. Maltais, Louis-Gabriel & Gosselin, Louis, 2022. "Forecasting of short-term lighting and plug load electricity consumption in single residential units: Development and assessment of data-driven models for different horizons," Applied Energy, Elsevier, vol. 307(C).
    2. Østergård, Torben & Jensen, Rasmus L. & Maagaard, Steffen E., 2016. "Building simulations supporting decision making in early design – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 61(C), pages 187-201.
    3. Zhang, Tiantian & Tan, Yufei & Yang, Hongxing & Zhang, Xuedan, 2016. "The application of air layers in building envelopes: A review," Applied Energy, Elsevier, vol. 165(C), pages 707-734.
    4. Vrînceanu, Alexandra & Dumitrașcu, Monica & Kucsicsa, Gheorghe, 2022. "Site suitability for photovoltaic farms and current investment in Romania," Renewable Energy, Elsevier, vol. 187(C), pages 320-330.
    5. Shen, Yuxuan & Pan, Yue, 2023. "BIM-supported automatic energy performance analysis for green building design using explainable machine learning and multi-objective optimization," Applied Energy, Elsevier, vol. 333(C).
    6. Yu Dong & Tongyu Qin & Siyuan Zhou & Lu Huang & Rui Bo & Haibo Guo & Xunzhi Yin, 2020. "Comparative Whole Building Life Cycle Assessment of Energy Saving and Carbon Reduction Performance of Reinforced Concrete and Timber Stadiums—A Case Study in China," Sustainability, MDPI, vol. 12(4), pages 1-24, February.
    7. Carolina Rodriguez & María Coronado & Marta D’Alessandro & Juan Medina, 2019. "The Importance of Standardised Data-Collection Methods in the Improvement of Thermal Comfort Assessment Models for Developing Countries in the Tropics," Sustainability, MDPI, vol. 11(15), pages 1-22, August.
    8. Re Cecconi, F. & Moretti, N. & Tagliabue, L.C., 2019. "Application of artificial neutral network and geographic information system to evaluate retrofit potential in public school buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 110(C), pages 266-277.
    9. Pourshab, Nasrin & Tehrani, Mehdi Dadkhah & Toghraie, Davood & Rostami, Sara, 2020. "Application of double glazed façades with horizontal and vertical louvers to increase natural air flow in office buildings," Energy, Elsevier, vol. 200(C).
    10. Liu, Che & Sun, Bo & Zhang, Chenghui & Li, Fan, 2020. "A hybrid prediction model for residential electricity consumption using holt-winters and extreme learning machine," Applied Energy, Elsevier, vol. 275(C).
    11. Barbosa, Sabrina & Ip, Kenneth, 2014. "Perspectives of double skin façades for naturally ventilated buildings: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 40(C), pages 1019-1029.
    12. Leurent, Martin & Jasserand, Frédéric & Locatelli, Giorgio & Palm, Jenny & Rämä, Miika & Trianni, Andrea, 2017. "Driving forces and obstacles to nuclear cogeneration in Europe: Lessons learnt from Finland," Energy Policy, Elsevier, vol. 107(C), pages 138-150.
    13. Yu, Jinghua & Ye, Hong & Xu, Xinhua & Huang, Junchao & Liu, Yunxi & Wang, Jinbo, 2018. "Experimental study on the thermal performance of a hollow block ventilation wall," Renewable Energy, Elsevier, vol. 122(C), pages 619-631.
    14. Liangwen Yan & Fengfeng Qian & Wei Li, 2018. "Research on Key Parameters Operation Range of Central Air Conditioning Based on Binary K-Means and Apriori Algorithm," Energies, MDPI, vol. 12(1), pages 1-13, December.
    15. Zhang, Sheng & Lin, Zhang & Ai, Zhengtao & Huan, Chao & Cheng, Yong & Wang, Fenghao, 2019. "Multi-criteria performance optimization for operation of stratum ventilation under heating mode," Applied Energy, Elsevier, vol. 239(C), pages 969-980.
    16. Yildiz, B. & Bilbao, J.I. & Sproul, A.B., 2017. "A review and analysis of regression and machine learning models on commercial building electricity load forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 1104-1122.
    17. Pätäri, Eero & Karell, Ville & Luukka, Pasi & Yeomans, Julian S, 2018. "Comparison of the multicriteria decision-making methods for equity portfolio selection: The U.S. evidence," European Journal of Operational Research, Elsevier, vol. 265(2), pages 655-672.
    18. 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.
    19. Wang, Y. & Mauree, D. & Sun, Q. & Lin, H. & Scartezzini, J.L. & Wennersten, R., 2020. "A review of approaches to low-carbon transition of high-rise residential buildings in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    20. Langevin, J. & Reyna, J.L. & Ebrahimigharehbaghi, S. & Sandberg, N. & Fennell, P. & Nägeli, C. & Laverge, J. & Delghust, M. & Mata, É. & Van Hove, M. & Webster, J. & Federico, F. & Jakob, M. & Camaras, 2020. "Developing a common approach for classifying building stock energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(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:gam:jsusta:v:10:y:2018:i:10:p:3777-:d:176790. 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.