Measuring Energy Consumption

For many people, the beginning is the time of year for resolutions and target-setting. So what better time to consider how we measure how well we are doing?

As energy managers, we face this problem all year round when it comes to measuring energy consumption. Our first challenge is to understand how much energy we are using, and our second challenge is to establish whether that energy consumption is reasonable or not. We do this through the regular practice of Monitoring and Targeting (M&T).

M&T relies on the comparison of actual energy consumption to a reference consumption (or baseline) for the same time period. When these values are compared, any differences between the actual consumption and reference consumption are highlighted. These differences can help identify equipment failure, poor control, or other causes of energy waste. But the quality of outputs from an M&T programme are heavily reliant on having an accurate baseline to compare against. So where do these baselines come from?

Two Models for Monitoring and Measuring Energy Consumption

ASHRAE (American Society of Heating, Refrigeration and Air-Conditioning) distinguishes two basic ways to create a baseline when measuring energy consumption: “Forward Models” are based on known users of energy, such as a schedule of lighting loads, or a building’s heat-loss characteristics; “Inverse Models” meanwhile, have no interest in what is using the energy, but instead, rely on patterns of historic consumption to predict future energy use.

“Inverse” or historic-based models are certainly the easiest to create. As a first iteration, you can simply compare one week’s data to the next, to see if it is higher or lower. You might compare day-to-day or year-to-year, but whatever the time period, your baseline is the historic data. The increasing prevalence of energy management software is making historic-based comparison ever more popular, since it’s a relatively simple function to provide. When energy software packages offer targeting and alert functionality, this is nearly always based on whether the data is better or worse than a comparable period in the past.

More sophisticated inverse models use regression analysis to identify how energy use is influenced by factors such as air temperature, occupancy, or production volumes. This can be very useful as a way to use historic performance as a baseline, even when conditions for both periods are not completely identical. For example, an energy manager might use Heating Degree Days to create an energy baseline that is lower in warm weather and higher in cold weather. Machine-learning models do this very well, and can “predict” expected energy use to a high level of accuracy.

But all historic-based models have one major flaw. All they can tell you is whether you are better or worse than you used to be. If your energy consumption has always been higher than it should be, you are likely to perpetuate that poor performance. As Shirley Bassey would say, “It’s all just a little bit of history repeating”.

Implementing a Forward Model

The best way to avoid this problem is to consider the “forward model” approach. Forward models don’t need any historical data to run. Instead, they use information about the building fabric, lighting, and HVAC to compute the energy flows in and out of the building, and predict the energy requirements. The dynamic building simulation software required to create a detailed forward model is highly complex and can demand thousands of inputs. But for many buildings, even a rough estimate of full-load energy based on a simple asset inventory can give some really valuable insights.

As part of a recent study into a group of near-identical supermarkets, ETS created three different baselines to investigate which one was most helpful for energy management. The first was based on a historic baseline, using regression techniques to adapt to changes in outdoor temperature and occupancy. The second was a simple average of the consumption across all of the sites. The final baseline was a detailed dynamic building simulation.

The historic baseline performed best at predicting actual consumption. It remains the simplest and best approach for highlighting occasional deviations from normal operating patterns, particularly where the variables driving energy use are well understood.

However, the building simulation baseline and the peer-group baseline showed up a number of energy management issues that were not apparent from the historic view. These findings included a long-term problem with control of lighting overnight, excessive (and unexplained) exterior lighting loads, and an unusual sensitivity to outdoor air temperature for refrigeration at one of the sites.

On their own, none of the baseline approaches were able to highlight all of the energy-saving opportunities that were present. So, if your new year resolution is to achieve more energy savings, consider whether a new baseline approach might just show you a new way to measure energy consumption and highlight areas for improvement.


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