| Alternative Energy Contractors
An Introduction to Weather Normalization of Utility Bills for
Alternative Energy Contractors
by: John Avina
UTILITY BILL TRACKING: THE REPORT CARD FOR ALTERNATIVE ENERGY
CONTRACTORS
More and more, alternative energy contractors want to prove to customers
the savings they expect. Customers often want to know that they have
saved the energy and costs they were originally promised. From the
customers’ viewpoint, the simplest and most understandable proof of
energy savings comes from a simple comparison of electricity bills. Did
the system save on electricity costs or not?(1) In theory, a simple
comparison of pre-installation bills to post-installation bills, and you
will see if you have saved.
But if it is so easy, why write a paper on this? Well, it isn’t so easy.
Let’s find out why.
Suppose a solar energy contractor installed a new solar electric system
for a building. One likely would expect to see energy and cost savings
from this retrofit. Figure 1.1 presents results our alternative energy
contractor might expect.
But what if, instead, the bills presented the disaster shown in Figure
1.2?
Imagine showing a customer these results after they have invested
hundreds of thousands of dollars in your system. It is hard to inspire
confidence in your abilities with results like this.
How should the solar energy contractor present this data to customer? Do
you think the contractor would be feeling confident about the job, and
about getting referrals for future solar projects? Probably not. The
customer might simply look at the figures and, since figures don’t lie,
conclude they have hired the wrong contractor, and that the solar system
doesn’t work very well!
There are many reasons the system may not have delivered the expected
savings. A possibility is that the project is delivering savings, but
the summer after the installation was much hotter than the summer before
the installation. Hotter summers translate into higher air conditioning
loads, which could 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-installation
weather was hotter, the solar electric project looked like it didn’t
save any energy, even though it really did. Imagine explaining that to
customers!
If the weather really was the cause of the higher usage, then how could
you ever use utility bills to measure savings from solar energy
projects? 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 glance, 3 degrees doesn’t appear significant,
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 solar contractors who faced exactly this
problem!
How is a solar energy contractor going to show savings from a solar
electric system 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-installation bills.
Then there would be no penalty for extreme weather. This is exactly what
weather normalization does. To show savings from a retrofit (or good
alternative energy practice), and to avoid our disastrous example, an
alternative energy contractor should normalize the utility bills for
weather, so that changes in weather conditions will not compromise the
savings numbers.
The practice of normalizing energy bills to weather with energy software
is catching on, with more and more energy managers, energy engineers,
and contractors correcting their bills for weather because they want to
be able to prove that they are actually saving energy from their
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). But, since we are
trying to keep this chapter 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
The next question is, how do we figure out how much energy we would have
used this year? That 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
alternative energy program, the year before you installed a retrofit, or
the year before you, the new energy contractor, 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 solar electric system. We will call this year the
Base Year(4).
Then 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 only that Cooling
Degree Days need to be gathered at this step.(5) Figure 1.5 presents
Cooling Degree Days over two years.
Figure 1.6: Finding the relationship between usage and weather data. The
blue dots represent the utility bills. The red 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)
Once we have this equation, we are done with this regression process.
Let’s recap what we have done:
We normalized Base Year utility bills and weather data for number of
days in the bill.
We graphed normalized Base Year utility data versus normalized weather
data.
We found a Best Fit Line through the data. The Best Fit Line then
represents the utility bills for the Base Year.
The Best Fit Line Equation represents the Best Fit Line, which in turn
represents the Base Year of utility data.
Base Year bills ≈ Best Fit Line = Fit Line Equation
The Fit Line Equation represents how your customer 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 customer 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 = “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 then are summed over some period
of time (months, a year, etc.).(8)
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.
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 starts to cool.(9) 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 and utility
manager software 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 the same standard (that is, using
65°F as the balance point). It is more accurate, though, 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.(10)
To find the balance point temperature of a building, 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 red
lines signifying the two trends in Figure 1.8. (Ignore the vertical red
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 red line is called Temperature Sensitive
Consumption, which represents electrical usage associated with the
building’s cooling system. Notice that 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.
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).
We can view the same type of graph for heating usage in Figure 1.9.
Notice that the major difference between the two graphs, is that the
Temperature Sensitive trend slopes up and to the left (rather than up
and the right). As the outdoor temperature drops, the building use more
electricity 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.
NORMALIZING FOR OTHER VARIABLES
More and more energy professionals 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 the number
of computers produced. If a 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.
Figure 1.10 presents normalized daily usage versus production for a
widget factory. The baseline equation for this normalization is shown at
the bottom of the figure. Notice that Units of Production (UPr) as well
as Cooling Degree Days (CDD) are included in the equation, meaning that
this normalization included weather data and production data.
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. Again, these normalizations can be performed by specialized
utility bill tracking software, or using spreadsheets.
CONCLUSION
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, or due to your alternative energy system, or both. If you wish
to use utility bills to determine energy savings from your alternative
energy system 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 paper.
You may adjust your usage for other variables as well, such as occupancy
or production.
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1) What are the alternatives? The most common might involve determining
savings for each of the energy conservation activities using a
spreadsheet, or perhaps even a building model. Both of these alternative
strategies could require much additional work, as the alternative energy
contractor 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, and finally, spreadsheets are often
projections of energy savings, not measurements.
2) Cooling degree days are defined in detail later in the chapter,
however a rough 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 Degree Days 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 same is true for Heating Degree Days.
3) Some energy professionals select 2 years of bills rather than one.
Good reasons can be argued both for choosing one year or two years. 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 energy 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 contractors, 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) You would not sum or average high or low temperatures for a period of
time, as the result would not be useful. However, you can sum degree
days, and the result remains useful, as it is proportional to the
heating or cooling requirements of a building.
9) 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.
10) If you calculate degree days by hand, or using a spreadsheet, you
would use the following formulae for your calculations. Of course,
commercially available utility manager software that performs weather
nomalization handles this automatically.
For each day,
HDDi = [ TBP – ( Thi + Tlo ) / 2 ] x 1 Day+
CDDi = [ ( Thi + Tlo ) / 2 – TBP ] x 1 Day+
Where:
HDDi = Heating Degree Days for one day
CDDi = Heating 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 = ΣHDDi
CDD = ΣCDDi
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.
About The Author
John Avina is Director of Abraxas Energy Consulting and has worked in
energy analysis and utility bill tracking for over a decade. Learn more
about finding the right utility bill tracking program, energy savings,
and energy management at
http://www.abraxasenergy.com/ .
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