An Energy Manager’s Intro to Weather Normalization of Utility Bills

July 12, 2012

Utility Bill Tracking: The Report card for Facilities and Energy Managers

Energy Managers all too often have to justify their existence to management. They may be asked: “How much did we save last year?”, “Did your recommendations give reasonable paybacks?”, “Since the last project didn’t save any money, why would we expect the next one to?”

Since over the reign of an energy manager, many energy conservation projects, control strategies, and operation and maintenance procedures may be employed, the simplest method to report on the energy manager’s complete performance is to look at the utility bills. Management often sees it quite simply-it is all about the utility bills, since the bills reflect how much you are paying. Did the energy manager save us money or not?[1]

Since most energy managers are already tracking their utility bills, it should only take an additional step to see whether you have saved any energy and costs from your energy management program. In theory, you could just compare the prior year’s bills to the current year’s bills and see if you have saved.

But if it is so easy, why write a chapter on this? Well, it isn’t so easy. Let’s find out why.

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 1.1 presents results the energy manager might expect.

Figure 1.1: Expected Pre and Post-Retrofit usage for chilled water system retrofit

But what if, instead, the bills presented the disaster shown in Figure 1.2?

Figure 1.2: A disaster of a project? Comparison of Pre-Retrofit and Post-Retrofit data

Imagine showing management these results after you have invested a quarter-million dollars. It is hard to inspire confidence in your abilities with results like this.

How should the energy manager present this data to management? Do you think the energy manager is feeling confident about his decisions and about getting funding for future energy savings projects? Probably not. 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 our example, we are claiming that because the post-retrofit weather was hotter, the chiller project looked like it didn’t save any energy, even though it really did. Imagine explaining that to management!

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 90°F or above compared to the usual 12 days. In addition, the average temperature in Detroit was 74.8°F compared to the normal 71.4 °F. At first thought, 3 degrees doesn’t seem like all that much, however, if you convert the temperatures to cooling degree days[2], as shown in Figure 1.3, 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!

Figure 1.3: Cooling Degree Days in Detroit, Michigan for 2004 and 2005

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 somehow 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. This process has many names: weather correction, weather normalization, tuning to weather, tuning or weather regression.

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 Figure 1.2, and a disastrous project. We used the equation:

Savings = (Last year’s usage) – (This year’s usage)

When we normalize for weather, the same data results in Figure 1.4 and uses the equation:

Savings = (How much energy we would have used this year) – (This year’s usage)

Figure 1.4: Comparison of Baseline and Actual (Post-Retrofit) data with Weather Correction

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[3] 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, the year before you, the new energy manager, were hired, or just 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[4].

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 (not Heating Degree Days). A section on calculating Degree Days follows later in the chapter. For now, recognize that only Cooling Degree Days need to be gathered at this step.[5] Figure 1.5 presents Cooling Degree Days over two years.

Figure 1.5 Cooling Degree Days

Base Year bills and Cooling Degree Days are then normalized by number of days, as shown in Figure 1.6. 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.

Figure 1.6: Finding the relationship between usage and weather data. The dots represent the utility bills. The line is the best fit line.

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.[6]

This Best Fit Line has an equation, which we call the Fit Line Equation, or in this case the Baseline Equation[7]. The Fit Line Equation from Figure 1.6 might be:

Baseline kWh = (5 kWh/Day * #Days ) + ( 417 kWh/CDD * #CDD )[8]

Once we have this equation, we are done with this regression process.

Let’s recap what we have done:

  1. We normalized Base Year utility bills and weather data for number of days in the bill.
  2. We graphed normalized Base Year utility data versus normalized weather data.
  3. We found a Best Fit Line through the data. The Best Fit Line then represents the utility bills for the Base Year.
  4. The Best Fit Line Equation represents the Best Fit Line, which in turn represents the Base Year of utility data.

Base Year bills ┰ˆ 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 (or your software) 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. Both equations immediately preceding are the same, as Baseline represents “How much energy we would have used this year”, and Actual represents “How much energy we did use this year.”

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

Calculating Degree Days and Finding the Balance Point

Cooling Degree Days (CDD) are roughly proportional to the energy used for cooling a building, while Heating Degree Days, (HDD) are roughly proportional to the energy used for heating a building. Degree Days, although simply calculated, are quite useful in energy calculations. They are calculated for each day, and are then summed over some period of time (months, a year, etc.).[9]

In general, daily degree days are the difference between the building’s balance point and the average outside temperature. To understand degree days then, we first need to understand the concept of Balance Points.

Figure 1.7: Determining the balance point using a kWh/day vs. Outdoor Temperature graph

Buildings have their own set of Balance Points for heating and for cooling – and they may not be the same. The Heating Balance Point can be defined as the outdoor temperature at which the building starts to heat. In other words, when the outdoor temperature drops below the Heating Balance Point, the building’s heating system kicks in. Conversely, when the outdoor temperature rises above the Cooling Balance Point, the building’s cooling system starts to cool.[10] A building’s balance point is determined by nearly everything associated with it, since nearly every component associated with a building has some effect on the heating of the building: building envelope construction (insulation values, shading, windows, etc.), temperature set points, thermostat set back schedules if any, the amount of heat producing equipment (and people) in the building, lighting intensity, ventilation, HVAC system type, HVAC system schedule, lighting and miscellaneous equipment schedules among other factors.

In the past, before energy professionals used computers in their everyday tasks, degree day analysis was simplified by assuming balance points of 65°F for both heating and cooling. As a result, it was easy to publish and distribute degree days, since everyone calculated them using that same standard. It is more accurate, however, to recognize that every building has its own balance points and to calculate degree days accordingly. Consequently, you are less likely to see degree days available, as more sophisticated analysis requires you to calculate your own degree days based upon your own building’s balance points.[11]

A way to find the balance point temperature of a building is to graph the Usage/Day against Average Outdoor Temperature (of the billing period) as shown in Figure 1.7. Notice that Figure 1.7 presents two trends. One trend is flat, and the other trend slopes up and to the right. We have drawn lines signifying the two trends in Figure 1.8. (Ignore the vertical line for now.) The flat trend represents Non-Temperature Sensitive Consumption, which is electrical consumption that is not related to weather. In Figure 1.7, Non-Temperature Sensitive Consumption is roughly the same every month, about 2450 kWh per day. Examples of Non-Temperature Sensitive Consumption include lighting, computers, miscellaneous plug load, industrial equipment and well pumps. Any usage above the horizontal line is called Temperature Sensitive Consumption, which represents electrical usage associated with the building’s cooling system. Notice in Figure 1.8, the Temperature Sensitive Consumption only occurs at temperatures greater than 61°F. The intersection of the two trends is called the Balance Point or Balance Point Temperature, which is 61°F in this example.

Figure 1.8: kWh /day vs. Average Outdoor Temperature

Notice also that, in Figure 1.8, as the outdoor temperature increases, consumption increases. As it gets hotter outside, the building uses more energy, thus the meter is used for cooling, but not heating. The Balance Point Temperature we found is the Cooling Balance Point Temperature (not the Heating Balance Point Temperature).

Figure 1.9: Therm/day vs. Average Outdoor Temperature

We can view the same type of graph for natural gas usage in Figure 1.9. Notice that the major difference between the two graphs (electric and gas), is that the Temperature Sensitive trend slopes up and to the left (rather than up and the right). As it gets cooler outside, they use more gas, therefore, they use gas to heat the building.

Now that we have established our balance point temperature, we have all the information required to calculate Degree Days. If your graph resembles Figures 1.9, you will be using Heating Degree Days. If your graph resembles Figure 1.8, you will be using Cooling Degree Days. If you calculate degree days by hand, or using a spreadsheet, you would use the following formulae for your calculations. Of course, commercially available software that performs weather normalization handles this automatically.

For each day,

HDDi = [ TBP – ( Thi + Tlo ) / 2 ] x 1 Day+
CDDi = [ ( Thi + Tlo ) / 2 – TBP ] x 1 Day+


  • HDDi = Heating Degree Days for one day
  • CDDi = Cooling Degree Days for one day
  • TBP = Balance Point Temperature,
  • Thi = Daily High Temperature
  • Tlo = Daily Low Temperature
  • + signifies that you can never have negative degree days. If the HDDi or CDDi calculation yields a negative number, then the result is 0 degree days for that day.

Heating and Cooling Degree Days can be summed, respectively, over several days, a month, a billing period, a year, or any interval greater than a day. For a billing period (or any period greater than a day),

  • HDD = SHDDi
  • CDD = SCDDi

Now, let’s take a look back to Figure 1.3, where you may have noticed that there are more than twice as many Cooling Degree Days (CDD) in August 2005 than in August 2004. Because Cooling Degree Days are roughly proportional to a building’s cooling energy usage, one could rightly assume that the cooling requirements of the building would be roughly double as well.

Normalizing for Other Variables

More and more manufacturing energy managers are coming to understand the value of normalizing utility data for production in addition to (or instead of) weather. This works if you have a simple variable that quantifies your production. For example, a computer assembly plant can track number of computers produced. If your factory manufactures several different products, for example, disk drives, desktop computers and printers, it may be difficult to come up with a single variable that could be used to represent production for the entire plant (i.e. tons of product). However, since analysis is performed on a meter level, rather than a plant level, if you have meters (or submeters) that serve just one production line, then you can normalize usage from one meter with the product produced from that production line.

School districts, colleges, and universities often normalize for the school calendar. Real estate concerns, hotels and prisons normalize for occupancy. Essentially any variable can be used for normalization, as long as it is an accurate, consistent predictor of energy usage patterns.

Figure 1.10 presents normalized daily usage versus school calendar for meter in a California school. The number of school days were tallied from a school calendar. For this meter, there is a strong relationship to number of school days, but no relationship to weather. It is easy to state, then, that this meter is controlling lighting and/or plug load, but not HVAC equipment.

Some meter’s may have a significant relationship to more than one independent variable, such as production and CDD. Good energy accounting software can handle these multi-variable situations as well.

Figure 1.10 Daily Usage Normalized to School Calendar

The Baseline Equation is kWh = (96.3 * #Days) + (102.4 * #School Days).

Managing Unexpected Changes in Energy Usage Patterns

The greatest difficulty involved in using utility bills to track savings occurs when there are large, unexpected and unrelated changes to a facility. For example, suppose an energy manager was normalizing usage to weather for a building in order to successfully determine energy savings, and then the building was enlarged by several thousand square feet. The comparison of Baseline and Actual usage would no longer make any sense, as the Baseline number would continue to be determined based upon usage patterns before the building addition, whereas the Actual bills would include the addition. Now we would be comparing two different facilities, one with the addition and one without. If there were any energy savings, they might be buried in the additional usage from the new addition. Figure 1.11 presents our hypothetical case in which the new addition came online in August.

Figure 1.11 Example of increase in energy usage due to increase in square footage starting in August

Notice that in Figure 1.11 the Actual usage has increased while the Baseline did not. As a result, savings are hidden by the increase in usage from the building addition. Here, the energy manager would need to make a Baseline Adjustment (also known as Baseline Modification) to handle the increase in usage due to the building addition (since the Actual bills already include it). The energy manager would make a reasonable estimate of the additional usage and add that onto the Baseline. Our earlier equation, then, becomes:

Baseline kWh = ( 5 kWh/Day * #Days ) + ( 417 kWh/CDD * #CDD ) + Adjustment

where the Adjustment represents the additional usage due to the building addition. Figure 1.12 presents an example with the addition of the Baseline Adjustment.

Figure 1.12 Baseline now adjusted to account for increase in usage due to building addition

Baseline adjustments are the most troublesome part of using utility bills to analyze building usage. Buildings continue to change their usage patterns regardless of the energy managers’ efforts. To maintain usefulness, baseline adjustments must be added to the analysis.

Applying Costs to the Savings Equation

Energy managers often need to present their savings numbers to management in a form that managers can comprehend, which means showing savings in cost, rather than energy or demand units. Transforming energy savings into cost savings can be done quite simply, and there are several methods by which this can be done. As in most things, the simplest methods yield the most inaccurate results. The methods investigated here are blended rates and modeled rates. There are some variations on these themes, but they will not be covered here.

In many areas, utility rates may be difficult to understand and model. Once the energy manager understands the rate, he might have to explain it to management, which can be even more difficult. Simplicity is always worth striving for, as many energy managers don’t have the time to learn their rates and model them explicitly.

Blended rates (also called Average Costs) are the simplest way to apply costs to energy units. Suppose for a billing period that Baseline usage was 10,000 kWh and the current usage was 8,000 kWh, and current total cost was $800. It doesn’t matter how complex the rate is, to apply blended rates, we just consider total cost. The simplest application of blended rates would be to determine the average $/kWh of the current bill. In this case, we have $800 / 8000 kWh = $0.10/kWh. So, the blended rate ($0.10/kWh) would be applied to both the Baseline usage and the Actual or current usage, as shown in Table 1.1.

Table 1.1 Blended Rates Example

This may seem like the best solution, and many energy managers use blended rates as it does simplify what could be unnecessarily complex. However, there could be some problems associated with blended rates. Two examples follow.

Suppose you installed a thermal energy storage (TES) system on your premises. TES systems run the chillers at night when electricity is inexpensive and stores the cooling energy as either ice or chilled water in large storage containers. Then during the day when electricity is expensive, the chillers either don’t run at all, or run much less than they normally would. This strategy saves money, but it doesn’t usually save energy. In fact it often uses more energy, as some of that cooling energy that is stored in the storage container is lost through the walls of the container, and the extra pump that runs the system consumes energy. If you applied a blended rate to the TES system you might see the numbers in Table 1.2.

Table 1.2 Where Blended Rates Can Go Wrong

If you modeled the rates, you would see that even though you used more energy, you saved on electricity costs. On the other hand, if you used blended rates, you might see a net increase in energy costs. Blended rates would deliver a dramatically incorrect representation of cost savings.

Most energy managers don’t employ thermal energy storage, but they may shift demand to the evening. Suppose a facility is on a Time of Use rate and there is a small net increase in energy usage, combined with a significant shift in energy usage to off peak (less expensive) periods. What happens then? Since less energy is consumed during the more expensive on peak period and more is consumed during the less expensive off peak period, the total costs might decline (in real life). But if the usage increases, your blended rate strategy will show a net increase in costs (in your analysis), which is exactly wrong. Again you can refer to Table 1.2.

Another example demonstrates a weakness in the blended rate approach. Suppose you installed a new energy efficient boiler and boiler controls in a building that is mostly vacant in the summer. Suppose the utility charges a $25 monthly charge plus $1.30/therm.

A January bill, with 100 therms usage, is presented in Table 1.3.

Table 1.3 A Hypothetical Winter Gas Heating Bill

If our Baseline usage for January was 120 therms, then savings would be calculated using the blended rate, as shown in Table 1.4.

Table 1.4 Savings Calculations Using a Blended Gas Rate

That seems to work well. Now try July, in which the current bill might have had 1 therm usage, the bill is presented in Table 1.5.

Table 1.5 Problematic Hypothetical Summer Gas Heating Bill

And supposed Baseline usage in July was 4 therms, then savings would be calculated as shown in Table 1.6.

Table 1.6 Hypothetical Gas Heating Savings Problems

The blended rate calculation told us that the customer saved $78.90, whereas the actual rate calculation would have told us that the customer saved 3 therms * $1.30/therm = $3.90. This problem is not unusual. Often, this type of overstatement of savings occurs without anyone noticing. Blended rates can simplify the calculations and on the surface may return seemingly correct savings numbers. However, upon further analysis, it can usually be found that using blended rates introduces inaccuracies that can at times prove embarrassing (such as this example). The whole point of weather normalization was to reduce the error (due to weather and other factors) in the savings calculations. What is the point of going through the weather normalization procedure if you are only going to reintroduce a potentially even greater error when you apply costs to the savings equation?

If you want to get more accurate cost savings numbers then you would elect to model the rates, which unfortunately means that you will have to understand them.

This would involve retrieving the rate tariff from the utility (usually, they are on the utility’s website), and then entering all the different charges into your software or spreadsheet. There are a few difficulties associated with this approach:

  1. Many rates are very difficult to understand
  2. Some tariff sheets do not explain all the charges associated with a rate.
  3. Some software packages have limitations and can model most but not all of the different charges, or even worse, some packages don’t model rates at all.
  4. Rates change often, which means you will have to continually keep updating the rates. The good news on this front is that once the rate is modeled, the changes are usually very minor.

As mentioned before, if you are modeling rates, then usually the same rate is applied to both Baseline and Actual usage and demand. There are exceptions of course. If you changed your facility’s rate or changed utility providers, then you should apply your old rate to the Baseline, and your new rate to your Actual usage. To understand which rate should be used for the Baseline, answer the same question: “How much would we have spent if I had not run the energy management program?” The answer is, you would still be on the old rate, therefore, Baseline gets the old rate, and Actual gets the new rate.

Regardless of how you apply costs to your savings equation, good utility bill tracking software can handle all of these situations.

Weather Normalization in Excel VS. Specialized Utility Bill Tracking Software

Weather normalization can be done in Excel; however, it can be laborious and oftentimes may not be as rigorous as when done using specialized software. Excel will give regressions, fit line equations and statistical indicators which show how well your usage is represented by the fit line.

However, it is difficult to find the best balance point in Excel, as you can in specialized software.[12] If you use Excel, the steps we outlined in this paper will have to be done manually, whereas with canned software, most of it is done for you automatically. In addition, in Excel, if you want to achieve a good fit to your data, you may have to iterate these manual steps for different balance points. The most tedious process in Excel is matching up the daily weather to the billing periods. Try it and you will see. Assuming the weather and bill data is already present, it should take less than two minutes in canned software to perform weather normalization, versus at least 30 minutes in Excel.

Available Weather Normalization Desktop Software

All of the major desktop utility bill tracking software packages will now normalize for weather data. All of them will correct for your own variables as well; however, only some of them will normalize for weather in addition to your own variables. The major desktop programs are EnergyCAP®, and Metrix™ You can find information on all of them online.

Available Weather Normalization in Web Software

At the time of this writing, only one of the above desktop programs is also offered on a web platform, though a web front end is available from some of the other providers which allow users to enter bill data, perform diagnostic tests and make reports online.

Weather Normalization in Interval Data Web Software

There are some interval data programs that perform weather normalization as well, but for these packages, weather normalization is done primarily for forecasting applications, not for verifying energy savings. The method is more complex as the data is in finer increments. Weather forecasts are downloaded and then projected usage is then calculated. At least one of the programs uses weather normalization, or any of a handful of other techniques to forecast energy usage. Energy managers can use these forecasts to adjust their energy consuming activities to prevent high peak demands.


Weather varies from year to year. As a result, it becomes difficult to know whether the change in your utility bills is due to fluctuations in weather, your energy management program, or both. If you wish to use utility bills to determine energy savings from your energy management efforts with any degree of accuracy, it is vital that you remove the variability of weather from your energy savings equation. This is done using the weather normalization techniques described in this chapter. You may adjust your usage for other variables as well, such as occupancy or production. You may have to make baseline adjustments to further “correct” the energy savings equation for unexpected changes in energy usage patterns such as new additions. Finally, the method in which you apply costs to your energy savings calculations is very important. Blended rates, although simple, can result in inaccurate cost savings numbers, while more difficult modeling rates, are always right.

About The Author

John Avina, Director of Abraxas Energy Consulting, has worked in energy analysis and utility bill tracking for over a decade. Mr. Avina performed M&V for Performance Contracting at Johnson Controls. In later positions with SRC Systems, Silicon Energy and Abraxas Energy Consulting, he has taught well over 200 software classes, handled technical support for nearly a decade, assisted with product development, and written manuals for Metrix Utility Accounting System™. Mr. Avina managed the development of new analytical software that employed the weather regression algorithms found in Metrix™ to automatically calibrate building models. In October 2001, Mr. Avina, and others from the defunct SRC Systems founded Abraxas Energy Consulting. Mr. Avina has a MS in Mechanical Engineering from the University of Wisconsin-Madison, where he was a research assistant at the Solar Energy Lab. He is a Member of the American Society of Heating Refrigeration and Air-Conditioning Engineers (ASHRAE), the Association of Energy Engineers (AEE, and a Certified Energy Manager (CEM).

Abraxas Energy Consulting ( sells all of the major desktop utility bill tracking software programs, including EnergyCAP® Desktop, Metrix™, and Stark Essentials™. Abraxas Energy Consulting specializes in helping their clients find the right utility bill tracking program, setting up clients’ utility bill tracking databases and providing energy analysis and mentoring. The company also provides engineering services such as measurement and verification for ESCOs, utility bill tracking, building energy audits, utility bill auditing, software and energy analysis training and related custom software applications.

[1] What are the alternatives? The most common might involve determining savings for each of the energy conservation activities using a spreadsheet or perhaps a building model. Both of these alternative strategies could require much additional work, as the energy manager likely has employed several strategies over his tenure. One other drawback of spreadsheets is that energy conservation strategies may interact with each other, so that total savings may not be the sum of the different strategies. Finally, spreadsheets are often projections of energy savings, not measurements.

[2] Cooling degree days are defined in detail later in the chapter; however, a simplified meaning is given here. Cooling Degree Days are a rough measure of how much a period’s weather should result in a building’s cooling requirements. A hotter day will result in more Cooling Degree Days; whereas a colder day may have no Cooling Degree Days. Double the amount of cooling degrees should result in roughly double the cooling requirements for a building. Cooling Degree Days are calculated individually for each day. Cooling Degree Days over a month or billing period are merely a summation of the Cooling Degree Days of the individual days. The inverse is true for Heating Degree Days.

[3] Some energy professionals select 2 years of bills rather than one. Good reasons can be argued for either case. Do not choose periods of time that are not in intervals of 12 months (for example, 15 months, or 8 months could lead to inaccuracy).

[4] Please do not confuse Base Year with Baseline. Base Year is a time period, from which bills were used to determine the building’s energy usage patterns with respect to weather data, whereas Baseline, as will be described later, represents how much energy we would have used this month, based upon Base Year energy usage patterns and current month conditions (i.e. weather and number of days in the bill).

[5] Canned software does this automatically for you, while in spreadsheets, this step can be tedious.

[6] The statistical calculations behind the R2 value and a treatment of three other useful indicators, T-Statistic, Mean Bias Error, and CVRMSE are not treated in this chapter. For more information on these statistical concepts, consult any college statistics textbook. (For energy managers, a combination of R2 values and T-Statistics is usually enough.)

[7] Baseline Equation = Fit Line Equation +/- Baseline Modifications. We cover Baseline Modifications later in this chapter.

[8] The generic form of the equation is:

Baseline kWh = (constant * #Days) + (coefficient * #CDD)

where the constant and coefficient (in our example) are 5 and 417.

[9] Summing or averaging high or low temperatures for a period of time is not very useful. (Remember the Detroit example mentioned earlier.) However, you can sum degree days, and the result remains useful, as it is proportional to the heating or cooling requirements of a building.

[10] If you think about it, you don’t have to treat this at the building level, but rather can view it at a meter level. (To simplify the presentation, we are speaking in terms of a building, as it is less abstract.) Some buildings have many meters, some of which may be associated with different central plants. In such a building, it is likely that the disparate central plants would have different balance points, as conditions associated with the different parts of the building may be different.

[11] Some analysts had separate tables of degree days based upon a range of balance points (65, 60, 55, etc.), and analyzed their data painstakingly with several balance points until they found the best balance point temperature for their building. On the other hand, other analysts believe that all degree days are calculated assuming the standard balance point of 65 °F.

[12] It is not necessary to find the best balance point and you might choose instead to use published tables of degree days, which are often based on a 65 degree balance point. Using these standard degree days will in most cases lead to decreased accuracy and poorer fits. Using the base 65 degree balance point, many meters will not have an acceptable fit (R2 > 75%) at all.