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Accuracy of automated measurement and verification (M&V) techniques for energy savings in commercial buildings

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  • Granderson, Jessica
  • Touzani, Samir
  • Custodio, Claudine
  • Sohn, Michael D.
  • Jump, David
  • Fernandes, Samuel

Abstract

Trustworthy savings calculations are critical to convincing investors in energy efficiency projects of the benefit and cost-effectiveness of such investments and their ability to replace or defer supply-side capital investments. However, today’s methods for measurement and verification (M&V) of energy savings constitute a significant portion of the total costs of efficiency projects. They also require time-consuming manual data acquisition and often do not deliver results until years after the program period has ended. The rising availability of “smart” meters, combined with new analytical approaches to quantifying savings, has opened the door to conducting M&V more quickly and at lower cost, with comparable or improved accuracy. These meter- and software-based approaches, increasingly referred to as “M&V 2.0”, are the subject of surging industry interest, particularly in the context of utility energy efficiency programs. Program administrators, evaluators, and regulators are asking how M&V 2.0 compares with more traditional methods, how proprietary software can be transparently performance tested, how these techniques can be integrated into the next generation of whole-building focused efficiency programs.

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  • Granderson, Jessica & Touzani, Samir & Custodio, Claudine & Sohn, Michael D. & Jump, David & Fernandes, Samuel, 2016. "Accuracy of automated measurement and verification (M&V) techniques for energy savings in commercial buildings," Applied Energy, Elsevier, vol. 173(C), pages 296-308.
    • Handle: RePEc:eee:appene:v:173:y:2016:i:c:p:296-308
    DOI: 10.1016/j.apenergy.2016.04.049
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    References listed on IDEAS

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    Cited by:

    1. Abdurahman Alrobaie & Moncef Krarti, 2022. "A Review of Data-Driven Approaches for Measurement and Verification Analysis of Building Energy Retrofits," Energies, MDPI, vol. 15(21), pages 1-30, October.
    2. Carstens, Herman & Xia, Xiaohua & Yadavalli, Sarma, 2018. "Measurement uncertainty in energy monitoring: Present state of the art," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2791-2805.
    3. Fu, Hongxiang & Baltazar, Juan-Carlos & Claridge, David E., 2021. "Review of developments in whole-building statistical energy consumption models for commercial buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 147(C).
    4. Fu, Chun & Miller, Clayton, 2022. "Using Google Trends as a proxy for occupant behavior to predict building energy consumption," Applied Energy, Elsevier, vol. 310(C).
    5. Jeong Soo Shin & Jong Woo Park & Sean Hay Kim, 2020. "Measurement and Verification of Integrated Ground Source Heat Pumps on a Shared Ground Loop," Energies, MDPI, vol. 13(7), pages 1-24, April.
    6. Díaz, Julián Arco & Ramos, José Sánchez & Delgado, M. Carmen Guerrero & García, David Hidalgo & Montoya, Francisco Gil & Domínguez, Servando Álvarez, 2018. "A daily baseline model based on transfer functions for the verification of energy saving. A case study of the administration room at the Palacio de la Madraza, Granada," Applied Energy, Elsevier, vol. 224(C), pages 538-549.
    7. Granderson, Jessica & Fernandes, Samuel & Touzani, Samir & Lee, Chih-Cheng & Crowe, Eliot & Sheridan, Margaret, 2020. "Spatio-temporal impacts of a utility’s efficiency portfolio on the distribution grid," Energy, Elsevier, vol. 212(C).
    8. Grillone, Benedetto & Mor, Gerard & Danov, Stoyan & Cipriano, Jordi & Sumper, Andreas, 2021. "A data-driven methodology for enhanced measurement and verification of energy efficiency savings in commercial buildings," Applied Energy, Elsevier, vol. 301(C).
    9. Töppel, Jannick & Tränkler, Timm, 2019. "Modeling energy efficiency insurances and energy performance contracts for a quantitative comparison of risk mitigation potential," Energy Economics, Elsevier, vol. 80(C), pages 842-859.
    10. Cory Mosiman & Gregor Henze & Herbert Els, 2021. "Development and Application of Schema Based Occupant-Centric Building Performance Metrics," Energies, MDPI, vol. 14(12), pages 1-16, June.
    11. 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).
    12. Carstens, Herman & Xia, Xiaohua & Yadavalli, Sarma, 2017. "Low-cost energy meter calibration method for measurement and verification," Applied Energy, Elsevier, vol. 188(C), pages 563-575.
    13. 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.
    14. Baltuttis, Dennik & Töppel, Jannick & Tränkler, Timm & Wiethe, Christian, 2020. "Managing the risks of energy efficiency insurances in a portfolio context: An actuarial diversification approach," International Review of Financial Analysis, Elsevier, vol. 68(C).
    15. Simon Rouchier, 2022. "Bayesian Workflow and Hidden Markov Energy-Signature Model for Measurement and Verification," Energies, MDPI, vol. 15(10), pages 1-19, May.
    16. Herman Carstens & Xiaohua Xia & Sarma Yadavalli, 2018. "Bayesian Energy Measurement and Verification Analysis," Energies, MDPI, vol. 11(2), pages 1-20, February.

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