Title: Comparison of One- and Two-Variable Linear Regression Models and Classic Energy Intensity for Energy Performance Tracking of Two Manufacturing Sectors

Abstract

We report manufacturing facilities consumed about 32% of total domestic energy in the United States in 2016. To evaluate the energy savings achieved through the implementation of energy conservation projects and to establish powerful arguments for future projects, accurate energy performance tracking methods are necessary. The classic energy intensity method (i.e., the ratio of annual total energy over annual total production) is the means most widely used to measure savings because it can be understood and calculated easily. This method considers the variation in production rates to some extent; however, it fundamentally assumes facilities' base energy consumption (energy consumption at zero production) to be zero, which rarely holds true. Furthermore, this method does not consider variations in other relevant parameters, such as weather conditions. Therefore, the regression models approach is commonly recommended to track energy performance improvements. However, because it requires more data and statistical expertise, the regression models approach has been adopted by only a few facilities, and many others suspect it is not worth the effort for their specific cases. For this reason, the improved accuracy this approach offers needs to be demonstrated. By analyzing 477 monthly energy (electricity and natural gas) data sets, this study quantitatively comparedmore » the accuracy of classic energy intensity, one-variable (production only) linear regression models, and two-variable (i.e., production and weather) linear regression models for two manufacturing sectors (primary metal and transportation equipment manufacturing). Results showed that significant improvements in accuracy were achieved with one-variable regression models when compared with the classic energy intensity method and with two-variable regression models when compared with one-variable regression models. These improvements were demonstrated using the p values of intercept, cooling degree days, and heating degree days. Based on these results, to achieve a good balance between accuracy improvements and resources requirements, one-variable (production only) linear regression models for electricity consumption and two-variable (production and heating degree days) linear regression models for natural gas consumption are recommended for both sectors in the case of limited resources. Lastly, facilities can also use the results to decide which approaches fit better in their cases.« less

Authors:

Guo, Wei  ^[1];

• Search DOE PAGES for author "Guo, Wei"
• Search DOE PAGES for ORCID "0000-0002-3899-8174"

Wenning, Thomas J.  ^[1];

• Search DOE PAGES for author "Wenning, Thomas J."
• Search DOE PAGES for ORCID "0000-0002-7232-6822"

Nimbalkar, Sachin U.  ^[1];

• Search DOE PAGES for author "Nimbalkar, Sachin U."
• Search DOE PAGES for ORCID "0000-0002-0826-9720"

Thirumaran, Kiran ^[1]; Armstrong, Kristina O. ^[1]; Levine, Eli ^[2]

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+ Show Author Affiliations

1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
2. U.S. Department of Energy (DOE), Washington, DC (United States)

Publication Date:

Wed Aug 01 00:00:00 EDT 2018

Research Org.:

Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Sponsoring Org.:

USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Advanced Manufacturing Office

OSTI Identifier:

1474575

Grant/Contract Number:  

AC05-00OR22725

Resource Type:

Accepted Manuscript

Journal Name:

Energy Engineering

Additional Journal Information:

Journal Volume: 115; Journal Issue: 5; Journal ID: ISSN 0199-8595

Publisher:

Taylor & Francis

Country of Publication:

United States

Language:

English

Subject:

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; energy efficiency; energy intensity tracking; energy performance baselining; energy performance tracking; manufacturing facility; linear regression modeling