Strategic Energy Management. Keys to Behavioral and Operational Change.
Ryan M. Kroll, P. A. Willems, Dan Rubado, Kathleen Belkhayat
Abstract
Open-access reader
Ryan M. Kroll, P. A. Willems, Dan Rubado, Kathleen Belkhayat
Abstract
Open-access reader
This paper presents the results of an evaluation of Energy Trust of Oregon’s Strategic Energy Management (SEM) initiative. Commercial SEM is designed to deliver comprehensive energy services to large, typically multi-site, customers through behavioral and operational changes, while also identifying potential capital projects. This paper addresses both process findings and an analysis of savings estimation techniques. The evaluation sought to ensure that the initiative was achieving claimed savings at a reasonable cost and to provide feedback on program design and implementation. This meant both investigating the methods used to calculate savings and understanding how participants had incorporated energy saving policies and practices into their standard operating procedures. Key operational findings included: SEM takes time if organizational changes are to be implemented. A cohort approach, using multiple workshops with representatives from 5-10 organizations, appears to be effective in engaging participants and encouraging behavioral and operational change. Findings relating to the savings methodology included: Standardized regression analysis techniques are an acceptable means to determine savings. When regression analyses are used, savings should be calculated using a baseline of the operation immediately preceding program participation, whenever possible. Regression analyses should be clearly organized and link operational changes to reduced energy use. Linear extrapolation from limited data to project annual savings often induces significant error. Therefore, savings claims should be limited to observed savings to improve accuracy. If savings claims must be extrapolated based on limited data, these analyses can be supplemented with heating or cooling models to improve accuracy.
OpenAlex reports 1 citations for this work. Citation counts describe recorded attention and do not establish research quality.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
This paper presents the results of an evaluation of Energy Trust of Oregon’s Strategic Energy Management (SEM) initiative. Commercial SEM is designed to deliver comprehensive energy services to large, typically multi-site, customers through behavioral and operational changes, while also identifying potential capital projects. This paper addresses both process findings and an analysis of savings estimation techniques. The evaluation sought to ensure that the initiative was achieving claimed savings at a reasonable cost and to provide feedback on program design and implementation. This meant both investigating the methods used to calculate savings and understanding how participants had incorporated energy saving policies and practices into their standard operating procedures. Key operational findings included: SEM takes time if organizational changes are to be implemented. A cohort approach, using multiple workshops with representatives from 5-10 organizations, appears to be effective in engaging participants and encouraging behavioral and operational change. Findings relating to the savings methodology included: Standardized regression analysis techniques are an acceptable means to determine savings. When regression analyses are used, savings should be calculated using a baseline of the operation immediately preceding program participation, whenever possible. Regression analyses should be clearly organized and link operational changes to reduced energy use. Linear extrapolation from limited data to project annual savings often induces significant error. Therefore, savings claims should be limited to observed savings to improve accuracy. If savings claims must be extrapolated based on limited data, these analyses can be supplemented with heating or cooling models to improve accuracy.
Key concepts: Regression analysis, Computer science, Process management, Operations management, Process (computing), Energy (signal processing), Operations research, Risk analysis (engineering)