An enlightened approach to budgeting and targets for Energy Manager

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ABSTRACT

Every year energy managers need to report how much energy they saved and whether or not they met their energy savings targets. Most energy managers present reports comparing their current year's usage to energy usage from a previous year. Savings targets are typically a percentage of that previous year's usage. Setting targets and comparing in this manner usually does not generate an accurate estimate of energy actually saved. Inconsistencies arise from year-to-year fluctuations in weather, occupancy, production or other factors, which can interfere with savings results. Instead, an energy manager's performance should be determined by comparing current year usage to a normalized baseline, which represents how much energy the building would have used given current year weather conditions, production, occupancy, and base year usage patterns. Energy savings targets should be set based upon this dynamic baseline. Using dynamic targeting, variations in weather conditions, occupancy, production or other factors will not hinder the accurate measure of how much the energy manager saved, and whether energy savings goals were indeed met. This paper explains, with an example, the differences between using a static and dynamic (weather normalized) targeting to demonstrate energy savings.

INTRODUCTION

Every year, the typical energy manager will generate, or receive from management, a fixed energy budget. Throughout the rest of the year, he will be held to this static standard. He may also be asked to save 5% from prior year's energy usage, or may simply be provided with less money than the prior year. This method is simple for management to understand. Either the energy manager is meeting savings targets or he is not. Unfortunately, this method does not produce a realistic picture of how much energy the energy manager is saving.

Even though the building envelope, the building equipment, and the control set points themselves may not change from year to year in a static building, the energy usage may change drastically. This change may have nothing to do with the energy manager's performance -- there are simply other factors involved. A year with a hotter summer may require more air conditioning than a normal year. A year with a colder winter may require more heating than a normal year. Doubling the production in a manufacturing plant will increase the usage.

Energy Managers that allow themselves to be held to a static budget may succeed or fail as a result of factors that have nothing to do with their energy conservation efforts: weather, production or occupancy, for example. If even one of these factors is not in his favor, the energy manager may not meet his savings goal. How fair is that? There is nothing worse than knowing that you are doing a good job, but coming up short in the eyes of management because you are being judged by an arbitrary and inaccurate standard.

BUDGETING VS. TARGETING

It is understood that management needs to create budgets, and needs to make best predictions of future energy costs. No one can predict the future, but best guesses must still be made. The important point though, is that energy managers do not need to be held to these budgets when assessing and reporting on their job performance. The energy budget is a financial tool, not an energy management tool. Energy managers should set separate targets to which they can aspire. The Static Target, treated below, is just like a budget -- a number that is fixed, and has no relation to the unmanageable factors which actually do affect energy usage, such as weather, occupancy, hours of operation and production.

AN EXAMPLE OF THE FAILURE OF STATIC TARGETING

As an example, consider Joe, a first year facility manager in Lincoln, Nebraska who tried to meet his 5% energy reduction target by shutting off his air handlers during unoccupied hours during the summer months. He started the strategy in July and continued through October. During the cold months, he did not shut off the equipment.

Imagine his surprise when viewing the bills and finding that there were no energy savings in the summer months. "How could that be?" Joe wondered.

Unfortunately for our energy manager, the summer of 2005 was a hot summer for the Upper Midwest. As a result, energy managers might have had a difficult time meeting their energy savings targets, as the higher temperatures led to increased air conditioning usage, overriding any real energy savings gained. There was a large increase in cooling degree days for Lincoln in 2005 as compared to 2004.

What Joe perhaps didn't realize is that the hotter summer likely resulted in increased air conditioning usage, which obscured the savings he actually did achieve by shutting off the air handlers. But that doesn't really matter, since he was being held to a Static Target. His manager took the prior year's monthly usage, subtracted 5%, and held Joe to that standard. That sounded simple when Joe started the job. Unfortunately, due to the hot summer, Joe would have to save a lot more than 5% to offset the increased usage due to additional air conditioning.

This is only an example, but this is a real phenomenon that affects all energy managers whose buildings have space conditioning mechanical equipment. When using a static measure such as simple utility bill comparison, weather does affect how much savings you report. Hot summers and cold winters will eat into real savings. Conversely, cool summers and warm winters will exaggerate any true savings. For this reason, it doesn't make sense to set energy usage targets based upon a prior year's usage. A hot summer or a cold winter could preclude you from reaching your savings goal.

Since the bills already have the hot summer (and consequent additional air conditioning usage) already added into them, then it would be best if the target did as well. Then the affect of weather could be cancelled out from both the target and actual bills, leaving only savings. This is exactly what Dynamic Targeting does.

...AND THE SUCCESS OF DYNAMIC TARGETING

If Joe had used Dynamic Targeting, he would have seen savings. Shutting off the air handlers actually did save energy, and he was able to save 5.9% for the year. He (and his manager) just didn't know it.

STATIC TARGETING VS. DYNAMIC TARGETING

It is best to allow your savings target to fluctuate with changes in weather. This involves weather normalizing your utility bills, and is called Dynamic Targeting. Using this method, you compare what your target usage should be (based upon current weather conditions) with how much the building actually did use.

There are a few steps involved in this process. Implementing the steps is not that difficult, but the concepts may be difficult to grasp at first because it is so different from simply comparing current year usage to a past year's usage and applying a percentage.

This extra work is worth the effort, because the dynamic method results in a more accurate portrayal of true energy savings, and can save the energy manager from reporting failure when, in fact, he succeeded. In some cases, not using dynamic targeting could cost an energy manager his job.

The steps for Static Targeting are:

- Select a Base Year with which to compare future usage

- Set savings targets as a percent of usage

Then, each month...

- Compare current year usage to the target usage amount to see if you are meeting your goal.

- Convert the actual, target and deviance amounts into Dollars.

The steps for using Dynamic Targeting are:

- Select a Base Year with which to compare future usage

- Set savings target percentage

- Determine the relationship between weather data and utility usage during the Base Year

Then, each month...

- Determine how much energy your facility would have used during the current year given current year weather conditions

- Determine target usage amounts

- Compare actual usage to the target usage amount to see if you are meeting your goal.

- Convert the actual, target and deviance amounts into Dollars.

Dynamic Targeting can be done using spreadsheets, although utility bill analysis software greatly simplifies the task.

The remainder of this paper will detail the procedure involved in using Dynamic Targeting.

HOW TO PERFORM DYNAMIC TARGETING

The steps for Dynamic Targeting as presented above are now detailed in this section:

Step 1. Select a Base Year

Whether you are using static or dynamic targeting, you still have to choose a Base Year to compare to. A common approach is to use the prior year. Sometimes, a specific year is chosen, such as 1990 was chosen as the Base Year for the Kyoto Protocol. If significant changes or retrofits were made to the facility and you wish to see the effects of those changes on your budget, you should choose a year prior those alterations. In any case, you will need a full year of utility bills (12 months) for your Base Year.

Step 2. Set Savings Target Percentage

Rather than have your supervisor give you a static list of usage or cost targets you need to beat, find out what percentage savings is expected of you. In our example, Joe, the energy manager, only had to save 5%. We call this percentage our Target Savings Percentage. The actual Savings Target can only be calculated each month after it happens, as the Savings Target varies with changes in weather (production, occupancy, etc.).

Step 3. Determine the Relationship Between Weather and Usage

This is the hard part. If your building has air conditioning equipment or heating equipment, then your building likely has a relationship to weather. In this step, all we are looking for is an equation that represents how your building uses energy. An example equation might be:

Baseline kWh =

(2064 kWh/Day * #Days )

+ ( 72 kWh/CDD * #CDDs )

The equation tells us that the building uses 2064 kWh for every day in the billing period, and for every Cooling Degree Day (CDD), the building uses 72 kWh. In other words, short billing periods, will have fewer days, and thus less usage, while hot months will have more CDDs, and thus more usage. The Baseline Equation was generated using Base Year billing data and Base Year weather data, and represents how the building uses energy based upon Base Year usage patterns.

The Baseline Equation can be simply generated using utility bill analysis software, and can be generated using spreadsheets as well (with a bit more effort). This normalization method is presented in more detail in papers and books cited in the References section of this paper.

Step 4. Determine How Much your Facility Would Have Used

Once you have the Baseline Equation, everything is simple from here.

For each month in your reporting period, you will need to get a current-year bill, and determine the number of days and Cooling Degree Days (CDDs) associated with the billing period. If you are normalizing for other variables like production, schedule or occupancy, then collect this data as well.

We know the Baseline Equation represents how the building uses energy based upon Base Year building usage patterns. How much would the building use given current weather conditions? To find this out, we plug into the Baseline Equation the current bill's number of days and CDDs, and/or other variable data. In this example, the billing period was 7/16/06 to 8/15/06, which is 31 days, 91,000 kWh usage, and 495 CDDs.

Baseline kWh =

(2064 kWh/Day * #Days )

+ ( 72 kWh/CDD * #CDD )

= (2064 kWh/Day * 31 Days)

+ ( 72 kWh/CDD * 495 CDDs)

= 99,624 kWh

Baseline kWh then represents what the building would have used based upon Base Year usage patterns, and Current Year weather conditions (and number of days).

This is a Dynamic Baseline. If it were much cooler, (i.e., there were only 100 CDDs), the Baseline kWh would have been much less.

Step 5. Determine Current Month Target

In our example, we expected a 5% savings Target. So we take Baseline kWh and multiply it by 95% to get what our target usage amount was.

Target kWh =

Baseline kWh * (1 - Savings %)

= 99,624 kWh * 95%

= 94,643 kWh

Step 6. Determine if you are Meeting Your Target

First, let's calculate energy savings.

Savings =

Baseline kWh - Actual kWh

= 99,624 kWh - 91,000 kWh

= 8,624 kWh

Now for Targets -- compare the current month bill to the current month target usage. If we are meeting the target, then Actual kWh will equal or be less than the Target kWh. Deviation is the difference between the two.

Target kWh = 94,643 kWh

Actual kWh = 91,000 kWh

Deviation = Target kWh - Actual kWh = 3,643 kWh.

For this month, our energy manager did indeed manage to meet his Target. However, if we just used a static target, then we would have seen,

Target kWh =

Base Year kWh *95%

= 81,800 kWh * 95%

= 77,710 kWh

Actual kWh = 91,000 kWh

Deviation = Target kWh - Actual kWh = -13,290 kWh.

Using Static Targeting, the energy manager would not have met the Target at all, but would have been showing a large increase in usage. August 2005 was hot compared to August 2004 -- it follows that there was an increase in usage from August 2004 to August 2005.

Step 7. Convert Energy Units into Dollars

Since management is usually interested in Dollars, you will need to convert your energy numbers into Dollars. There are many ways to handle this. The simplest way is just to use blended rates. From the current month's bill, divide total cost by total usage, and apply this ratio to your savings.

Current Bill kWh = 91,000

Current Bill $ = $7,587

Blended Rate = $ 7,587 / 91,000 kWh = $0.083 / kWh

Now convert the energy numbers into Dollars.

Savings $ = 8624 kWh * $0.083/kWh = $719

Target $ = 94,643 kWh * $0.083/kWh = $7891

Actual $ = $7587 (from bills)

CONCLUSION

In order for energy managers to provide reliable metrics for setting and meeting energy savings targets, a dynamic energy usage target should be established. Static Targets are typically a percentage of a prior year's usage. Variations in weather can drastically affect energy usage of HVAC equipment in current year bills, thereby rendering the comparison of current year bills to Static Targets useless. Dynamic Targets modulate in response to variations in weather (or other variables), thereby allowing the affect of fluctuations in weather (or other variables) to be removed from the energy savings equation. Dynamic Targets are established as a percentage of Baseline energy usage, which is determined using linear regression analysis. This process is described in the International Performance Measurement and Verification Protocol, and implemented in utility bill analysis software, and can be reproduced in spreadsheet applications.

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Three methods for analysis of Bill a powerful tool for Energy Manager

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ABSTRACT

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.

INTRODUCTION

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.

BENCHMARKING

Let's suppose you were the new energy manager in charge of a portfolio of school buildings for a district. Due to a lack of resources, you cannot devote your attention to all the schools at the same time. You must select a handful of schools to overhaul. To identify those schools most in need of your attention, one of the first things you might do is find out which schools were using too much energy. A simple comparison of Total Annual Utility Costs spent would identify those buildings that spend the most on energy, but not why.

Benchmarking Different Categories of Buildings

When benchmarking, it is also useful to only compare similar facilities. For example, if you looked at a school district and compared all buildings by $/SQFT, you might find that the technology centers administration buildings were at the top of the list, since administration buildings and technology centers often have more computers and are more energy intensive than elementary schools and preschools. These results are expected and not necessarily useful. For this reason, it might be wise to break your buildings into categories, and then benchmark just one category at a time.

Different Datasets

You can benchmark your buildings against each other (as we did in our example) or against publicly available databases of similar buildings in your area. Energy Star's Portfolio Manager allows you to compare your buildings against others in your region. Perhaps those buildings in your portfolios that looked the most wasteful are still in the top 50th percentile of all similar buildings in your area. This would be useful to know.

Occasionally, management decides that their organization needs to save some arbitrary percentage (5%, 10%, etc.) on utility costs each year. Depending upon the goal, this can be quite challenging, if not impossible. Energy managers can use benchmarking to guide management in setting realistic energy management goals. For example, our school district energy manager might decide to create a goal that the three most energy consuming schools use only $0.80/SQFT. Since this is about as much as the lowest energy consuming schools are currently using, this could be an attainable goal.

If you can find a dataset, you may also be able to benchmark your buildings against a set of similar buildings in your area and see the range of possibilities for your buildings. In any case, benchmarking will focus your energy management efforts and provide realistic goals for the future.

Rules of Thumb

New energy managers often search for a "rule of thumb" to use for benchmarking. An example could be: "If your building uses more than $2/SQFT/Year then you have a problem." Unfortunately, this won't work. Different types of buildings have different energy intensities. Moreover, different building locations will require differing amounts of energy for heating and cooling. In San Francisco, where temperatures are consistently in the 60s, there is almost no cooling requirement for many building types; whereas in Miami, buildings will almost always require cooling. Different building types, with their characteristic energy intensities, different weather sites, and different utility rates all combine to make it hard to have rules of thumb for benchmarking. However, energy managers whose portfolios are all close by, can develop their own rules of thumb. These rules will most likely not be transferable to other energy managers in different locations, with different building types, or using different utility configurations.

Benchmarking Buildings in Different Locations

There are some complications associated with benchmarking. Suppose you were the energy manager of a chain store, and you had buildings in different national locations. Then benchmarking might not be useful in the same sense. Would it be fair to compare a San Diego store to a Chicago store, when it is always the right temperature outside in San Diego, and always too hot or too cold in Chicago? The Chicago store will constantly be heating or cooling, while the San Diego store might not have many heating or cooling needs. Comparing at $/SQFT might help decide which store locations are most expensive to operate due to high utility rates and different heating and cooling needs.

Some energy analysts benchmark using kBtu/SQFT to remove the effect of utility rates (replacing $ with kBtu). Some will take it a step further using kBtu/SQFT/HDD to remove the effect of weather (adding HDD), but adding HDD (or CDD) is not a fair measurement, as it assumes that all usage is associated with heating. This measurement also does not take into account cooling (or heating) needs. Many thoughtful energy managers shy away from benchmarking that involves CDD or HDD.

Different Benchmarking Units

Another popular benchmarking method is to use kBtu/SQFT (per year), rather than $/SQFT (per year). By using energy units rather than costs, "rules of thumb" can be created that are not invalidated with each rate increase. In addition, the varying costs of different utility rates does not interfere with the comparison.

Benchmarking Summation

Benchmarking is a simple and convenient practice that allows energy managers to quickly assess the energy performance of their buildings by simply comparing them against each other using a relative (and relevant) yardstick. Buildings most in need of energy management practice are easily singled out. Reasonable energy usage targets are easily determined for problem buildings.

LOAD FACTOR ANALYSIS

Once you have identified which buildings you want to make more efficient, you can use Load Factor Analysis to concentrate your energy management focus towards reducing energy or reducing demand.

What Load Factor is

Load Factor is commonly calculated by billing period, and is the ratio between average demand and peak (or metered) demand. Average demand is the average hourly draw during the billing period.

What Load Factor Means

High Load Factors (greater than 0.75) represent meters that have nearly constant loads. Equipment is likely not turned off at night and peak usage (relative to off peak usage) is low.

Low Load Factors (less than 0.25) belong to meters that have very high peak power draws relative to the remainder of the sample. These meters could be associated with chillers or electric heating equipment that is turned off for much of the day. Low Load Factors can also be associated with buildings that shut off nearly all equipment during non-running hours, such as elementary schools.

Load Factors greater than 1 are theoretically impossible , but appear occasionally on utility bills. Isolated instances of very high or low Load Factors are usually an indicator of metering errors.

One school, Tyler MS, consistently has a much lower Load Factor than the others (hovering consistently around 20%). Low Load Factors can be ascribed to either very high peak loads or very low loads during other hours. In this case, we cannot blame the Load Factor problem on "peaky" cooling loads, as the problem exists all year. A likely cause can be that Tyler MS is doing a better job at shutting off all lighting and other equipment at night than the other schools. One school (Jackson MS) typically has higher Load Factors than the other schools. One reason may be that lighting, HVAC and other equipment is running longer hours than at Tyler MS.

A good energy manager would investigate what building operational behavior is contributing to the low Load Factor values (and consequently relatively high demand) for Tyler MS, and would investigate whether the demand could be decreased. Inquiring about whether Jackson MS is turning off equipment at night is also advisable.

Load Factor Rules of Thumb

Load Factor analysis is an art, not a science. Different building types (i.e. schools, offices, hospitals, etc.) will have different Load Factor ranges. Since hospitals run many areas 24 hours a day, one might expect higher Load Factors than for schools, which can turn off virtually everything at night. Also many things contribute to a particular building's Load Factor. A building left on 24 hours a day can still have a low Load Factor if there are large peaks each month - for example, a 20 bed hospital that has a scheduled MRI truck visit once each month. The MRI demand is large, and can greatly impact the Load Factor of a small facility.

Like Benchmarking, you can determine your own rules of thumb for your buildings, however, your range of acceptable Load Factors will vary based upon building type and climate. Rules of Thumb may not be that helpful though. Like Benchmarking, just identifying the buildings with unusually high and low Load Factors, relative to the other buildings in the portfolio, should be sufficient.

Load Factor Summation

Load Factor can be used to identify billing and metering errors, buildings that are not turning off equipment, and buildings with suspiciously high demands. While Benchmarking can identify buildings most likely to yield large energy efficiency payoffs, Load Factor Analysis can point to easily resolved scheduling and metering issues.

WEATHER NORMALIZATION

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.

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, 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 disastrous results. We used the equation:

Savings = Last year's usage - This year's usage

When we normalize for weather, we use 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. 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 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

SUMMARY

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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