Utility Bill Tracking systems are at the center of an effective energy management program. However, some organizations spend time and money putting together a utility bill tracking system and never reap any value. This paper presents three utility bill analysis techniques which energy managers can use to arrive at sound energy management decisions and achieve cost savings.
Utility bill tracking and analysis is at the center of rigorous energy management practice. Reliable energy management decisions can be made based upon analysis from an effective utility bill tracking system. From your utility bills you can determine:
– whether you are saving energy or increasing your consumption,
– which buildings are using too much energy,
– whether your energy management efforts are succeeding,
– whether there are utility billing or metering errors, and
– when usage or metering anomalies occur (ie. when usage patterns change)
Any energy management program is incomplete if it does not track utility bills. Equally, any energy management program is rendered less effective when its utility tracking system is difficult to use or does not yield valuable information. In either case, fruitful energy savings opportunities are lost.
Many practical energy managers make the smart choice and invest in utility bill tracking software, but then fail to recover their initial investment in energy savings opportunities. How could this be?
This paper introduces three simple and useful procedures that can be performed with utility bill tracking software. Just performing and acting upon the first two types of analysis will likely save you enough money to pay for your utility bill tracking system in the first year. The three topics are Benchmarking, Load Factor Analysis, and Weather Normalization as shown in Table 1.
Another important utility bill analysis method is to normalize utility bills to weather. Weather Normalization allows the energy manager to determine whether the facility is saving energy or increasing energy usage, without worrying about weather variation.
Suppose an energy manager replaced the existing chilled water system in a building with a more efficient system. He likely would expect to see energy and cost savings from this retrofit. Figure 7 presents results the energy manager might expect.
But what if, instead, the bills presented the disaster shown in Figure 8?
A quarter-million dollar retrofit is difficult to justify with results like this. And yet, the energy manager knows that everything in the retrofit went as planned. What caused these results?
Clearly the energy manager cannot present these results without some reason or justification. Management may simply look at the figures and, since figures don’t lie, conclude they have hired the wrong energy manager!
There are many reasons the retrofit may not have delivered the expected savings. One possibility is that the project is delivering savings, but the summer after the retrofit was much hotter than the summer before the retrofit. Hotter summers translate into higher air conditioning loads, which typically result in higher utility bills.
Hotter Summer -> Higher Air Conditioning Load -> Higher Summer Utility Bills
In other words, the new equipment really did save energy, because it was working more efficiently than the old equipment. The figures don’t show this because this summer was so much hotter than last summer.
If the weather really was the cause of the higher usage, then how could you ever use utility bills to measure savings from energy efficiency projects (especially when you can make excuses for poor performance, like we just did)? Your savings numbers would be at the mercy of the weather. Savings numbers would be of no value at all (unless the weather was the same year after year).
Our example may appear a bit exaggerated, but it begs the question: Could weather really have such an impact on savings numbers?
It can, but usually not to this extreme. The summer of 2005 was the hottest summer in a century of record-keeping in Detroit, Michigan. There were 18 days at 90degF or above compared to the usual 12 days. In addition, the average temperature in Detroit was 74.8degF compared to the normal 71.4 degF. At first thought, 3 degrees doesn’t seem like all that much; however, if you convert the temperatures to cooling degree days , as shown in Figure 9, the results look dramatic. Just comparing the June through August period, there were 909 cooling degree days in 2005 as compared to 442 cooling degree days in 2004. That is more than double! Cooling degree days are roughly proportional to relative building cooling requirements. For Detroit then, one can infer that an average building required (and possibly consumed) more than twice the amount of energy for cooling in the summer of 2005 than the summer of 2004. It is likely that in the Upper Midwestern United States there were several energy managers who faced exactly this problem!
How is an energy manager going to show savings from a chilled water system retrofit under these circumstances? A simple comparison of utility bills will not work, as the expected savings will get buried beneath the increased cooling load. The solution would be to apply the same weather data to the pre- and post-retrofit bills, and then there would be no penalty for extreme weather. This is exactly what weather normalization does. To show savings from a retrofit (or other energy management practice), and to avoid our disastrous example, an energy manager should normalize the utility bills for weather so that changes in weather conditions will not compromise the savings numbers.
More and more energy managers are now normalizing their utility bills for weather because they want to be able to prove that they are actually saving energy from their energy management efforts.
In many software packages, you can establish the relationship between weather and usage in just one click. Because the one-click “tunings” that the software gives you are not always acceptable, it does help to understand the underlying theory and methodology so that you can identify the problem tunings and make the necessary adjustments. The more you know about the topic the better. The section that follows explains in a little more detail the basic elements of weather normalization.
How Weather Normalization Works
Rather than compare last year’s usage to this year’s usage, when we use weather normalization, we compare how much energy we would have used this year to how much energy we did use this year. Many in our industry do not call the result of this comparison, “Savings”, but rather “Usage Avoidance” or “Cost Avoidance” (if comparing costs). Since we are trying to keep this treatment at an introductory level, we will simply use the word Savings.
When we tried to compare last year’s usage to this year’s usage, we saw the disastrous project in Figure 8. We used the equation:
Savings = Last year’s usage – This year’s usage
When we normalize for weather, the same data results in Figure 10 and uses the equation:
Savings = How much energy we would have used this year – This year’s usage
The next question is how to figure out how much energy we would have used this year? This is where weather normalization comes in.
First, we select a year of utility bills to which we want to compare future usage. This would typically be the year before you started your energy efficiency program, the year before you installed a retrofit, or some year in the past that you want to compare current usage to. In this example, we would select the year of utility data before the installation of the chilled water system. We will call this year the Base Year .
Next, we calculate degree days for the Base Year billing periods. Because this example is only concerned with cooling, we need only gather Cooling Degree Days.
Base Year bills and Cooling Degree Days are then normalized by number of days, as shown in Figure 11. Normalizing by number of days (in this case, merely, dividing by number of days) removes any noise associated with different bill period lengths. This is done automatically by canned software and would need to be performed by hand if other means were employed.
To establish the relationship between usage and weather, we find the line that comes closest to all the bills. This line, the Best Fit Line, is found using statistical regression techniques available in canned utility bill tracking software and in spreadsheets.
The next step is to ensure that the Best Fit Line is good enough to use. The quality of the best fit line is represented by statistical indicators, the most common of which, is the R2 value. The R2 value represents the goodness of fit, and in energy engineering circles, an R2 > 0.75 is considered an acceptable fit. Some meters have little or no sensitivity to weather or may have other unknown variables that have a greater influence on usage than weather. These meters may have a low R2 value. You can generate R2 values for the fit line in Excel or other canned utility bill tracking software.
This Best Fit Line has an equation, which we call the Fit Line Equation, or in this case the Baseline Equation. The Fit Line Equation from Figure 11 might be:
Baseline kWh =
(5 kWh/Day * #Days ) + ( 417 kWh/CDD * #CDD )
Once we have this equation, we are done with the regression process.
Base Year bills ~= Best Fit Line = Fit Line Equation
The Fit Line Equation represents how your facility used energy during the Base Year, and would continue to use energy in the future (in response to changing weather conditions) assuming no significant changes occurred in building consumption patterns.
Once you have the Baseline Equation, you can determine if you saved any energy. How? You take a bill from some billing period after the Base Year. You then plug in the number of days from your bill and the number of Cooling Degree Days from the billing period into your Baseline Equation.
Suppose for a current month’s bill, there were 30 days and 100 CDD associated with the billing period.
Baseline kWh =
( 5 kWh/Day * #Days ) + ( 417 kWh/CDD * #CDD )
Baseline kWh =
( 5 kWh/Day * 30 ) + ( 417 kWh/CDD * 100 )
Baseline kWh = 41,850 kWh
Remember, the Baseline Equation represents how your building used energy in the Base Year. So, with the new inputs of number of days and number of degree days, the Baseline Equation will tell you how much energy the building would have used this year based upon Base Year usage patterns and this year’s conditions (weather and number of days). We call this usage that is determined by the Baseline Equation, Baseline Usage.
Now, to get a fair estimate of energy savings, we compare:
Savings = How much energy we would have used this year – How much energy we did use this year
Or if we change the terminology a bit:
Savings = Baseline Energy Usage – Actual Energy Usage
where Baseline Energy Usage is calculated by the Baseline Equation, using current month’s weather and number of days, and Actual Energy Usage is the current month’s bill.
So, using our example, suppose this month’s bill was for 30,000 kWh:
Savings = Baseline Energy Usage – Actual Energy Usage
Savings = 41,850 kWh – 30,000 kWh
Savings = 11,850 kWh
Utility Bill Tracking is at the center of a successful energy management system, but the bills must be used for sound analysis for any meaningful reduction in energy usage. By applying three analysis methods presented here (Benchmarking, Load Factor Analysis, and Weather Normalization), the energy manager can develop insight which should lead to sound energy management decisions.
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