IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i19p6207-d645834.html
   My bibliography  Save this article

Energy Savings Results from Small Commercial Building Retrofits in the US

Author

Listed:
  • Rachael Sherman

    (Engineering Technology and Construction Management, University of North Carolina, Charlotte, NC 28223, USA)

  • Hariharan Naganathan

    (Construction Management, School of Management, Wentworth Institute of Technology, Boston, MA 02115, USA)

  • Kristen Parrish

    (School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ 85287, USA)

Abstract

Small commercial buildings, or those comprising less than 50,000 square feet of floor area, represent 94% of U.S commercial buildings by count and consume approximately 8% of the nation’s primary energy; as such, they represent a largely unexploited opportunity for energy savings. Small commercial buildings also represent a large economic market—the National Institute of Building Sciences (NIBS) estimated the small commercial retrofit market at USD 35.6 billion. Despite the prominence of small commercial buildings and the economic opportunity for energy retrofits, many energy efficiency programs focus on large commercial buildings, and create efficiency solutions that do not meet the needs of the small commercial market. This paper presents an analysis of 34 small commercial case study projects that implemented energy efficiency retrofits. This paper contributes to the existing building retrofit body of knowledge in the following ways: (1) it identifies the decision criteria used by small commercial building stakeholders that decided to complete an energy retrofit; (2) it identifies the most commonly implemented efficiency measures in small commercial buildings, and discusses why this is the case; and (3) it provides empirical evidence about the efficacy of installing single energy efficiency measures (EEMs) compared to packages of EEMs in small commercial buildings by reporting verified energy savings. To the authors’ knowledge, this paper is the first to catalog decision criteria and energy savings for the existing small commercial buildings market, and this research illustrates that small commercial building decision-makers seem most motivated to retrofit their spaces by energy cost savings and operational concerns. Furthermore, small commercial building decision-makers opted to implement single-system retrofits in fifteen (15) of the thirty-four cases studied. Finally, this research documents the improved savings, in the small commercial buildings market, associated with a more integrated package of EEMs compared to a single-system approach, achieving approximately 10% energy savings for a single-system approach and more than 20% energy savings for integrated approaches. These savings translate to CO 2 savings of 1,324,000 kgCO 2 /year to 2,647,000 kgCO 2 /year, respectively, assuming small commercial buildings are retrofit at a rate of 0.95% of the stock annually.

Suggested Citation

  • Rachael Sherman & Hariharan Naganathan & Kristen Parrish, 2021. "Energy Savings Results from Small Commercial Building Retrofits in the US," Energies, MDPI, vol. 14(19), pages 1-16, September.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:19:p:6207-:d:645834
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/19/6207/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/14/19/6207/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hong, Tianzhen & Piette, Mary Ann & Chen, Yixing & Lee, Sang Hoon & Taylor-Lange, Sarah C. & Zhang, Rongpeng & Sun, Kaiyu & Price, Phillip, 2015. "Commercial Building Energy Saver: An energy retrofit analysis toolkit," Applied Energy, Elsevier, vol. 159(C), pages 298-309.
    2. Evonne Miller & Laurie Buys, 2008. "Retrofitting commercial office buildings for sustainability: tenants' perspectives," Journal of Property Investment & Finance, Emerald Group Publishing Limited, vol. 26(6), pages 552-561, September.
    3. Mališa Đukić & Margareta Zidar, 2021. "Sustainability of Investment Projects with Energy Efficiency and Non-Energy Efficiency Costs: Case Examples of Public Buildings," Sustainability, MDPI, vol. 13(11), pages 1-15, May.
    4. Lee, Sang Hoon & Hong, Tianzhen & Piette, Mary Ann & Taylor-Lange, Sarah C., 2015. "Energy retrofit analysis toolkits for commercial buildings: A review," Energy, Elsevier, vol. 89(C), pages 1087-1100.
    5. Chung, William & Hui, Y.V. & Lam, Y. Miu, 2006. "Benchmarking the energy efficiency of commercial buildings," Applied Energy, Elsevier, vol. 83(1), pages 1-14, January.
    6. Lee, Sang Hoon & Hong, Tianzhen & Piette, Mary Ann & Sawaya, Geof & Chen, Yixing & Taylor-Lange, Sarah C., 2015. "Accelerating the energy retrofit of commercial buildings using a database of energy efficiency performance," Energy, Elsevier, vol. 90(P1), pages 738-747.
    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. Seif Khiati & Rafik Belarbi & Ammar Yahia, 2023. "Sustainable Buildings: A Choice, or a Must for Our Future?," Energies, MDPI, vol. 16(6), pages 1-5, March.
    2. Prasertsak Charoen & Nathavuth Kitbutrawat & Jasada Kudtongngam, 2022. "A Demand Response Implementation with Building Energy Management System," Energies, MDPI, vol. 15(3), pages 1-21, February.
    3. Fernanda Cruz Rios & Sulaiman Al Sultan & Oswald Chong & Kristen Parrish, 2023. "Empowering Owner-Operators of Small and Medium Commercial Buildings to Identify Energy Retrofit Opportunities," Energies, MDPI, vol. 16(17), pages 1-20, August.

    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. 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.
    2. Hou, Jing & Liu, Yisheng & Wu, Yong & Zhou, Nan & Feng, Wei, 2016. "Comparative study of commercial building energy-efficiency retrofit policies in four pilot cities in China," Energy Policy, Elsevier, vol. 88(C), pages 204-215.
    3. Capozzoli, Alfonso & Piscitelli, Marco Savino & Neri, Francesco & Grassi, Daniele & Serale, Gianluca, 2016. "A novel methodology for energy performance benchmarking of buildings by means of Linear Mixed Effect Model: The case of space and DHW heating of out-patient Healthcare Centres," Applied Energy, Elsevier, vol. 171(C), pages 592-607.
    4. Hong, Tianzhen & Piette, Mary Ann & Chen, Yixing & Lee, Sang Hoon & Taylor-Lange, Sarah C. & Zhang, Rongpeng & Sun, Kaiyu & Price, Phillip, 2015. "Commercial Building Energy Saver: An energy retrofit analysis toolkit," Applied Energy, Elsevier, vol. 159(C), pages 298-309.
    5. Thrampoulidis, Emmanouil & Mavromatidis, Georgios & Lucchi, Aurelien & Orehounig, Kristina, 2021. "A machine learning-based surrogate model to approximate optimal building retrofit solutions," Applied Energy, Elsevier, vol. 281(C).
    6. Deb, C. & Schlueter, A., 2021. "Review of data-driven energy modelling techniques for building retrofit," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    7. Liang, Xin & Hong, Tianzhen & Shen, Geoffrey Qiping, 2016. "Improving the accuracy of energy baseline models for commercial buildings with occupancy data," Applied Energy, Elsevier, vol. 179(C), pages 247-260.
    8. Zhan, Sicheng & Liu, Zhaoru & Chong, Adrian & Yan, Da, 2020. "Building categorization revisited: A clustering-based approach to using smart meter data for building energy benchmarking," Applied Energy, Elsevier, vol. 269(C).
    9. Kalevi Piira & Julia Kantorovitch & Lotta Kannari & Jouko Piippo & Nam Vu Hoang, 2022. "Decision Support Tool to Enable Real-Time Data-Driven Building Energy Retrofitting Design," Energies, MDPI, vol. 15(15), pages 1-17, July.
    10. Wu, Wenbo & Dong, Bing & Wang, Qi (Ryan) & Kong, Meng & Yan, Da & An, Jingjing & Liu, Yapan, 2020. "A novel mobility-based approach to derive urban-scale building occupant profiles and analyze impacts on building energy consumption," Applied Energy, Elsevier, vol. 278(C).
    11. Chen, Yixing & Hong, Tianzhen, 2018. "Impacts of building geometry modeling methods on the simulation results of urban building energy models," Applied Energy, Elsevier, vol. 215(C), pages 717-735.
    12. Lu, Mengxue & Lai, Joseph, 2020. "Review on carbon emissions of commercial buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 119(C).
    13. Zheng, Donglin & Yu, Lijun & Wang, Lizhen, 2019. "A techno-economic-risk decision-making methodology for large-scale building energy efficiency retrofit using Monte Carlo simulation," Energy, Elsevier, vol. 189(C).
    14. Fernanda Cruz Rios & Sulaiman Al Sultan & Oswald Chong & Kristen Parrish, 2023. "Empowering Owner-Operators of Small and Medium Commercial Buildings to Identify Energy Retrofit Opportunities," Energies, MDPI, vol. 16(17), pages 1-20, August.
    15. Lešnik, Maja & Premrov, Miroslav & Žegarac Leskovar, Vesna, 2018. "Design parameters of the timber-glass upgrade module and the existing building: Impact on the energy-efficient refurbishment process," Energy, Elsevier, vol. 162(C), pages 1125-1138.
    16. Mahmud, Khizir & Amin, Uzma & Hossain, M.J. & Ravishankar, Jayashri, 2018. "Computational tools for design, analysis, and management of residential energy systems," Applied Energy, Elsevier, vol. 221(C), pages 535-556.
    17. Shen, Pengyuan & Braham, William & Yi, Yunkyu, 2019. "The feasibility and importance of considering climate change impacts in building retrofit analysis," Applied Energy, Elsevier, vol. 233, pages 254-270.
    18. García Kerdan, Iván & Raslan, Rokia & Ruyssevelt, Paul, 2016. "An exergy-based multi-objective optimisation model for energy retrofit strategies in non-domestic buildings," Energy, Elsevier, vol. 117(P2), pages 506-522.
    19. Grillone, Benedetto & Danov, Stoyan & Sumper, Andreas & Cipriano, Jordi & Mor, Gerard, 2020. "A review of deterministic and data-driven methods to quantify energy efficiency savings and to predict retrofitting scenarios in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    20. Ferahtia, Seydali & Rezk, Hegazy & Olabi, A.G. & Alhumade, Hesham & Bamufleh, Hisham S. & Doranehgard, Mohammad Hossein & Abdelkareem, Mohammad Ali, 2022. "Optimal techno-economic multi-level energy management of renewable-based DC microgrid for commercial buildings applications," Applied Energy, Elsevier, vol. 327(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:jeners:v:14:y:2021:i:19:p:6207-:d:645834. 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.