IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i14p8649-d863018.html
   My bibliography  Save this article

Large Scale Energy Signature Analysis: Tools for Utility Managers and Planners

Author

Listed:
  • Sukjoon Oh

    (Department of Building Research, Korea Institute of Civil Engineering and Building Technology, Goyang-si 10223, Korea)

  • John F. Gardner

    (Mechanical and Biomedical Engineering, Boise State University, 1910 University Drive, Boise, ID 83725, USA)

Abstract

Building energy signature analysis is a well-established tool for understanding the temperature sensitivity of building energy consumption and measuring energy savings. This tool has been used to measure energy savings of residential, commercial, and even industrial buildings. The public availability of electricity loads (i.e., hourly electricity demand (MW)) from entire Balancing Authorities (BAs) provide an interesting opportunity to apply this approach to a large aggregate load. In this paper, we explore that opportunity for BAs and show that the correlations for large geographical areas are surprisingly coherent when the change-point linear regression analysis is used with the daily interval data of electricity demand and outside air temperature. The change-point linear regression models of all the BAs, except WAUW and OVEC, show R 2 of 0.70 or more and CV-RMSE of 10.0% or less. We also suggest an analysis method that allows for meaningful comparisons between BAs and to assess changes in time for a given BA which could be used to interpret changes in load patterns year-to-year, accounting for changes in weather. This approach can be used to verify the impact of energy efficiency programs on a building component/system-wide basis. This study shows the annual electricity demand reductions for SCL and IPCO are 136,655 MWh (1.5%) and 182,053 MWh (1.1%), respectively.

Suggested Citation

  • Sukjoon Oh & John F. Gardner, 2022. "Large Scale Energy Signature Analysis: Tools for Utility Managers and Planners," Sustainability, MDPI, vol. 14(14), pages 1-19, July.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:14:p:8649-:d:863018
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/14/8649/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/14/8649/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Büyükalaca, Orhan & Bulut, Hüsamettin & YIlmaz, Tuncay, 2001. "Analysis of variable-base heating and cooling degree-days for Turkey," Applied Energy, Elsevier, vol. 69(4), pages 269-283, August.
    2. Martin Eriksson & Jan Akander & Bahram Moshfegh, 2022. "Investigating Energy Use in a City District in Nordic Climate Using Energy Signature," Energies, MDPI, vol. 15(5), pages 1-22, March.
    3. Bukarica, Vesna & Tomšić, Željko, 2017. "Energy efficiency policy evaluation by moving from techno-economic towards whole society perspective on energy efficiency market," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 968-975.
    4. Kelly Kissock, J. & Eger, Carl, 2008. "Measuring industrial energy savings," Applied Energy, Elsevier, vol. 85(5), pages 347-361, May.
    5. Christine S. M. Currie & Trivikram Dokka & John Harvey & Arne K. Strauss, 2018. "Future research directions in demand management," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 17(6), pages 459-462, December.
    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. Papada, Lefkothea & Kaliampakos, Dimitris, 2016. "Developing the energy profile of mountainous areas," Energy, Elsevier, vol. 107(C), pages 205-214.
    2. Pasquali, Andrea & Klinge Jacobsen, Henrik, 2019. "Construction of energy savings cost curves: An application for Denmark," MPRA Paper 93076, University Library of Munich, Germany.
    3. Colorado, D. & Hernández, J.A. & Rivera, W. & Martínez, H. & Juárez, D., 2011. "Optimal operation conditions for a single-stage heat transformer by means of an artificial neural network inverse," Applied Energy, Elsevier, vol. 88(4), pages 1281-1290, April.
    4. Ke, Ming-Tsun & Yeh, Chia-Hung & Su, Cheng-Jie, 2017. "Cloud computing platform for real-time measurement and verification of energy performance," Applied Energy, Elsevier, vol. 188(C), pages 497-507.
    5. Chai, Jiale & Huang, Pei & Sun, Yongjun, 2019. "Investigations of climate change impacts on net-zero energy building lifecycle performance in typical Chinese climate regions," Energy, Elsevier, vol. 185(C), pages 176-189.
    6. Ucar, Aynur, 2010. "Thermoeconomic analysis method for optimization of insulation thickness for the four different climatic regions of Turkey," Energy, Elsevier, vol. 35(4), pages 1854-1864.
    7. de la Rue du Can, Stephane & Pudleiner, David & Pielli, Katrina, 2018. "Energy efficiency as a means to expand energy access: A Uganda roadmap," Energy Policy, Elsevier, vol. 120(C), pages 354-364.
    8. Ye, Xianming & Xia, Xiaohua, 2016. "Optimal metering plan for measurement and verification on a lighting case study," Energy, Elsevier, vol. 95(C), pages 580-592.
    9. Bulut, Hüsamettin & Büyükalaca, Orhan & YIlmaz, Tuncay, 2001. "Bin weather data for Turkey," Applied Energy, Elsevier, vol. 70(2), pages 135-155, October.
    10. Dranka, Géremi Gilson & Ferreira, Paula & Vaz, A. Ismael F., 2020. "Cost-effectiveness of energy efficiency investments for high renewable electricity systems," Energy, Elsevier, vol. 198(C).
    11. Xiaotuan Li & Panfei Yang & Yuntao Zou, 2023. "An Empirical Investigation of the “Mezzogiorno Trap” in China’s Agricultural Economy: Insights from Data Envelopment Analysis (2015–2021)," Agriculture, MDPI, vol. 13(9), pages 1-30, September.
    12. Gelegenis, John J., 2009. "A simplified quadratic expression for the approximate estimation of heating degree-days to any base temperature," Applied Energy, Elsevier, vol. 86(10), pages 1986-1994, October.
    13. Ucar, Aynur & Balo, Figen, 2010. "Determination of the energy savings and the optimum insulation thickness in the four different insulated exterior walls," Renewable Energy, Elsevier, vol. 35(1), pages 88-94.
    14. du Plessis, Gideon Edgar & Liebenberg, Leon & Mathews, Edward Henry, 2013. "Case study: The effects of a variable flow energy saving strategy on a deep-mine cooling system," Applied Energy, Elsevier, vol. 102(C), pages 700-709.
    15. Zhang, L.Y. & Jin, L.W. & Wang, Z.N. & Zhang, J.Y. & Liu, X. & Zhang, L.H., 2017. "Effects of wall configuration on building energy performance subject to different climatic zones of China," Applied Energy, Elsevier, vol. 185(P2), pages 1565-1573.
    16. Katsoulakos, Nikolas M. & Kaliampakos, Dimitris C., 2014. "What is the impact of altitude on energy demand? A step towards developing specialized energy policy for mountainous areas," Energy Policy, Elsevier, vol. 71(C), pages 130-138.
    17. Chen, Yongbao & Xu, Peng & Chu, Yiyi & Li, Weilin & Wu, Yuntao & Ni, Lizhou & Bao, Yi & Wang, Kun, 2017. "Short-term electrical load forecasting using the Support Vector Regression (SVR) model to calculate the demand response baseline for office buildings," Applied Energy, Elsevier, vol. 195(C), pages 659-670.
    18. Artur Warchoł & Karolina Pęzioł & Marek Baścik, 2024. "Energy-Saving Geospatial Data Storage—LiDAR Point Cloud Compression," Energies, MDPI, vol. 17(24), pages 1-28, December.
    19. Hong Ye & Yaoyao Ding & Rong Zhang & Yuntao Zou, 2025. "A Study on the Correlation Between Urbanization and Agricultural Economy Based on Efficiency Measurement and Quantile Regression: Evidence from China," Sustainability, MDPI, vol. 17(13), pages 1-32, June.
    20. Büyükalaca, Orhan & Bulut, Hüsamettin, 2004. "Detailed weather data for the provinces covered by the Southeastern Anatolia Project (GAP) of Turkey," Applied Energy, Elsevier, vol. 77(2), pages 187-204, February.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:14:y:2022:i:14:p:8649-:d:863018. 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.