I am an engineer and President of Integrated Renewable Energy in Seattle, WA, USA. After 30 years doing systems engineering for space programs, I decided to transition to renewable energy systems and energy efficiency strategies. I am working to develop and implement energy strategies for industrial and commercial users in the Pacific Northwest of the United States....
Harnessing the Tiger: The Energy Performance Indicator
This is the fourth in a series of posts I am writing to discuss how a firm can both take control of its energy use, AND engage and empower its workforce in the process. In my last post, Getting to Work, I outlined the Energy Leader's four fundamental tasks in implementing a Strategic Energy Management System. In this post, I am going to explain and discuss the Energy Performance Indicator.
The Energy Performance Indicator, EnPI for short, is a bit technical. But it is also critical. Having an analytically determined benchmark for the energy program enables it to be taken seriously and to be discussed all the way up to the Boardroom. Don't shy away and substitute a less well justified baseline because the EnPI requires some math skills. This is a great chance to engage some people in engineering or some other department if you don't have the skills. Alternatively, call your local utility for help, and get them to be part of the program. If all else fails, some temporary consulting help can get you over the tough spots.
The most critical thing to understand about your EnPI is what affects your energy use. You are looking for things that are for the most part outside of your control or that are undesirable to limit (such as production). There are usually several things, and for that reason you will end up with a "multivariate regression" for your EnPI, a line, not a single number. Let's take a look at some of those variables.
Production Volume For most facilities, the production volume is the driver - how many cases of wine, how many can openers, how many trucks, bottles, tires, printers did you produce over the period you were tracking energy use? Be a bit careful how you choose. I went to a military facility once, and they told me that they tried an energy management approach, but it didn't show good results. I asked what performance indicator they were using, and they told me the total square footage of their buildings. The problem is that total square footage doesn't change much. It doesn't reflect activity very effectively. At that facility, a better measure would have been number of soldiers deployed or number of aircraft landing, or even the number of visitor passes written since that rises with increased activity.
The moral here is to choose your EnPi thoughtfully. It should reflect activity rather than size.
Weather One factor that affects nearly everyone is the weather. You will use energy in proportion to the extent you have to heat or cool your facility. It also affects processes IN your facility. If you have, for example, a paint operation that must be at 80 degrees F for proper application, then a cold day compounds your heating need. And, of course, a hot summer day will lower that need. If you are in a place susceptible to high winds, they will affect your energy use as well, so you may want to take them into account.
Materials Quality If you are manufacturing something - say paper - you use energy in inverse proportion to the quality of your raw materials. The more processing they need, the more energy you use. So some way of tying raw materials into your EnPI may be valuable.
Incoming Water Temperature If your water comes from a reservoir, and you heat a lot of water, you may notice that the incoming water temperature varies with the time of year. This may be useful in modeling your energy baseline since you will use more energy to heat that water in winter than in summer.
Once you have gathered all your energy data and determined your EnPI variables, you can put all of it into a spreadsheet and conduct a multivariate linear regression to come up with the coefficients that describe your energy use under all conditions. All commercially available spreadsheets have linear regression functions built into them, as do many accounting software packages and technical analysis packages.
This process takes a while, but the results are generally pretty enlightening. They serve as great discussion material for meetings with personnel and management, and may even reveal, right away, some potential energy savings that no one had considered.
Okay, so the geekiness is behind us now. Next we're going to look at how to think about your potential energy saving projects.
Paul Birkeland lives in Seattle, WA, US, and develops Strategic Energy Management Systems for government, commercial, and industrial organizations through Integrated Renewable Energy.











