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Measuring industrial energy savings

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  • Kelly Kissock, J.
  • Eger, Carl

Abstract

Accurate measurement of energy savings from industrial energy efficiency projects can reduce uncertainty about the efficacy of the projects, guide the selection of future projects, improve future estimates of expected savings, promote financing of energy efficiency projects through shared-savings agreements, and improve utilization of capital resources. Many efforts to measure industrial energy savings, or simply track progress toward efficiency goals, have had difficulty incorporating changing weather and production, which are frequently major drivers of plant energy use. This paper presents a general method for measuring plant-wide industrial energy savings that takes into account changing weather and production between the pre and post-retrofit periods. In addition, the method can disaggregate savings into components, which provides additional resolution for understanding the effectiveness of individual projects when several projects are implemented together. The method uses multivariable piece-wise regression models to characterize baseline energy use, and disaggregates savings by taking the total derivative of the energy use equation. Although the method incorporates search techniques, multi-variable least-squares regression and calculus, it is easily implemented using data analysis software, and can use readily available temperature, production and utility billing data. This is important, since more complicated methods may be too complex for widespread use. The method is demonstrated using case studies of actual energy assessments. The case studies demonstrate the importance of adjusting for weather and production between the pre- and post-retrofit periods, how plant-wide savings can be disaggregated to evaluate the effectiveness of individual retrofits, how the method can identify the time-dependence of savings, and limitations of engineering models when used to estimate future savings.

Suggested Citation

  • Kelly Kissock, J. & Eger, Carl, 2008. "Measuring industrial energy savings," Applied Energy, Elsevier, vol. 85(5), pages 347-361, May.
    • Handle: RePEc:eee:appene:v:85:y:2008:i:5:p:347-361
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    References listed on IDEAS

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    Cited by:

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    15. Mahmoud, Mohamed A. & Alajmi, Ali F., 2010. "Quantitative assessment of energy conservation due to public awareness campaigns using neural networks," Applied Energy, Elsevier, vol. 87(1), pages 220-228, January.
    16. 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).
    17. Pasquali, Andrea & Klinge Jacobsen, Henrik, 2019. "Construction of energy savings cost curves: An application for Denmark," MPRA Paper 93076, University Library of Munich, Germany.
    18. Aste, Niccolò & Leonforte, Fabrizio & Manfren, Massimiliano & Mazzon, Manlio, 2015. "Thermal inertia and energy efficiency – Parametric simulation assessment on a calibrated case study," Applied Energy, Elsevier, vol. 145(C), pages 111-123.
    19. 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.
    20. 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.
    21. Qiu, Yueming & Kahn, Matthew E., 2019. "Impact of voluntary green certification on building energy performance," Energy Economics, Elsevier, vol. 80(C), pages 461-475.
    22. Salahi, Niloofar & Jafari, Mohsen A., 2016. "Energy-Performance as a driver for optimal production planning," Applied Energy, Elsevier, vol. 174(C), pages 88-100.
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