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Methods Three Powerful Utility Bill Analysis for the Energy Manager

ABSTRACT
Utility bill systems monitoring are at the heart of a program of effective energy management. However, some organizations spend time and money preparing a bill tracking system public service and do not get any value. This paper presents three techniques utility bill analysis that energy managers can use to make management decisions and achieve energy cost savings.

INTRODUCTION
Utility bill tracking and analysis is at the center of rigorous energy management practice. reliable power management decisions can be made based on analysis of a bill effective public services tracking system. Since your utility bills can determine:

– If you are saving energy and increasing consumption,
– That the buildings are too energy
– Whether their efforts in energy management are succeeding
– If there are billing for services public or measurement errors, and
– When the use or measurement anomalies occur (ie, when the use of patterns of change)

Any program of energy management is incomplete if it does not track utility bills. Similarly, any energy management program that is less effective when your system monitor utility is difficult to use or provide valuable information. In any case, successful energy saving opportunities lost.

Many managers practical energy the smart choice and invest in the utility bill tracking software, but then can not recoup its initial investment in savings opportunities. How could it be?

This paper presents three simple and useful procedures that can be performed with the utility bill tracking software. Just make and act on the first two types of analysis will probably save enough money to pay your utility bill public monitoring system in the first year. The three themes are Benchmarking, Load Factor Analysis and Standardization of time as shown in Table 1.

REFERENCE
Suppose you were the manager of the new energy by a portfolio of school buildings in a district. Due to lack of resources, you can not spend attention to all schools, at the same time. You must select a handful of schools for review. To identify the schools that most need your attention, one of the first things you could do is find out which schools were using too much energy. A simple comparison of total annual utility costs rose, identify those buildings that spend most on energy, but not why.

As shown in Figure 1, the Santa Rosa Elementary School (ES), San Simeon and San Gabriel ES EN cost more to operate, while in San Luis Obispo ES and ES Creston minimal cost. But these three schools can not be the best schools to work first. More likely is that the buildings are the most energy are the largest buildings in the portfolio. It would be wiser to find buildings that spend more per square foot per year. This process is known as benchmarking, and is presented in Figure 2.

Figure 2 shows the same schools, but costs are divided by square feet (square). Santa Rosa and San Simeon is still the best targets, but San Gabriel ES is truly one of the most efficient schools. Instead of San Luis Obispo is the third school is more lavish in a $ / square foot basis. From this, we can also see that most schools will cost inefficient about 30% more schools to operate more efficiently.

Benchmarking of different categories of buildings
When evaluation comparison is also useful to compare only similar facilities. For example, if you looked at a school district and compared all the buildings in $ / square foot may find that the technology centers of the administration buildings were in the top of the list, as administration buildings and technology centers often have more computers and are more energy intensive than primary schools and preschools. These results are expected and not necessarily useful. For this reason, it may be wise to break the buildings into categories and then refer to only one category at a time.

Different databases
You can compare your buildings to each Portfolio (as we did in the example) or against public databases of similar buildings in your area. Energy Star Manager allows you can compare your buildings against others in their region. Perhaps these buildings in their portfolio that saw the most prodigal are still in the 50th percentile of all the main similar buildings in your area. This useful to know.

Sometimes, the administration decides that your organization needs to save a percentage arbitrary (5%, 10%, etc) in utility costs each year. Depending on the goal, this can be quite difficult, if not impossible. Energy managers Use benchmarks to guide the management in setting realistic goals for energy management, as shown in Figure 3. For example, our district school energy manager might decide to create a goal that most schools in the energy they consume three to use only $ 0.80/SQFT. Since this is both as the lowest energy consuming schools are currently using, this could be an attainable goal.

If you can find a set of data may also be able to reference their buildings with a series of similar buildings in your area and see the range of possibilities for their buildings. In any case, the assessment comparative focus their energy management efforts and to obtain realistic goals for the future.

Rules of thumb
New managers often energy finding a "golden rule" to use for benchmarking. An example might be: "If the building uses more than $ 2/SQFT/Year then you have a problem. "Unfortunately, this will not work. Different types of buildings have different energy intensities. Moreover, different construction sites require different amounts of energy for heating and cooling. In San Francisco, where temperatures are consistently in the years 60, there is almost no cooling requirement for the construction of many types, while in Miami, buildings often require refrigeration. Different construction types, with their characteristic energy intensities in different places of time and different levels of utility combine to make it difficult have general rules for benchmarking. However, energy managers whose portfolios are all very close, they can develop their own rules of thumb. These standards will most likely not be transferred to other energy managers in different places with different types of buildings, or using different configurations utilities.

Benchmarking buildings in different locations
There are some complications associated with benchmarking. Suppose you were the manager of energy from a chain of stores, buildings, and has had at different national locations. After benchmarking could be useful not in the same direction. Is it fair to compare a store in San Diego to Chicago store, where temperature is always on the outskirts of San Diego, and always too hot or too cold in Chicago? The Chicago store is constantly heating and cooling, while the San Diego store may not have heating or cooling many needs. Comparing $ / SqFt could help decide which store locations are more expensive to operate due to high electricity rates and different needs heating and cooling.

Some energy analysts KBTU reference using / SqFt to eliminate the effect of profit rates (replacing of $ with KBTU). Some it takes a step further by using KBTU / sq.ft. / HDD to eliminate the effect of climate (HDD), but HDD (or CDD) is not a fair measure, it assumes that all use is associated with heating. This measure also takes no account of cooling (or heating) needs. Many energy managers shy away from reflective benchmarking which involves CDD or HDD.

Different units of benchmarking
Another popular method is to use benchmarking KBTU / SqFt (per year) instead of $ / SqFt (per year). Using energy units rather than cost, "golden rules" can be created that are invalidated by any increase in rates. In addition, the varying costs of different service rates will not interfere with the comparison.
Sum Benchmarking
Benchmarking is a simple and practical practice, which allows energy managers to quickly assess the energy efficiency of their buildings, just against each other using a relative (and relevant) criteria. Most buildings in need of energy management practices are easily identified. reasonable energy use targets are easy to determine for buildings problem.

Load analysis FACTOR
Once you have identified that buildings you want to make more efficient, you can use Load Factor Analysis to focus their attention on the reduction of energy management and reduce energy demand.

What is load factor
Load common factor is calculated by billing period, and is the ratio between the average and peak (or measures) demand. Average demand is the average hourly draw for the billing period.

What average load factor
High load factors (higher than 0.75) meters representing almost constant loads. The team is probably not at night and off peak use (in connection with the use of peak) is low.

Factors low power (less than 0.25) belong to meters that have very high peak power points in relation to the rest of the sample. These meters may be associated with refrigerators electrical or heating equipment is turned off for most of the day. Low load factors can also be associated with the buildings that shut down most teams during the hours of operation, such as primary schools.

Load factors greater than 1 are theoretically impossible, but occasionally appear on invoices utilities. Isolated instances of load factors are too high or low is usually an indicator of measurement error.

Using the load factors to analyze buildings portfolio
Once you have calculated load factor, you can begin collecting useful information. Figure 5 shows real data from a school district in Georgia. Note that in May 2003 bill Houston MS is greater than 100% – this is obviously a measurement error or data entry.

The line thick lines in Figure 5 represents the average load factor. Note that the average load factor of all schools tends to increase in winter, and drop during the cold season. This is reasonable, as all the days grow loadshapes "with peaks" during the cold season in the afternoon in response to loads cooling, while during the heating season, since schools are heated with gas, the daily loadshapes tend to flatten out.

A school, Tyler MS, always has a much lower load factor than the others (always hovering around 20%). Low load factors can be attributed to any of the peak load too high or too low load during the hours of others. In this case, we can not blame the problem on Load Factor "with peak" cooling loads, because the problem exists throughout the year. A probable cause may be that Tyler MS is doing a better job of turning off all lights and other equipment at night that the other schools. A school (Jackson MS) typically has higher Load factors than other schools. One reason may be that the lighting, HVAC and other equipment running longer hours than the Member States in Tyler.

A good energy manager who will investigate what behavior operational building is contributing to the low load factor values (and hence the relatively high demand) Tyler MS, and to investigate whether the demand could be decreased. Asked if Jackson MS is shutting down at night is also recommended.

Figure 6 load factors for some elementary schools in California. Since load factors are so low, it seems that lighting and air conditioning equipment is turned off at night.

Load Factor rules of thumb
The load factor analysis is an art, not a science. Different types of buildings (ie schools, offices, hospitals, etc) have different ranges of load factor. Since many areas hospitals 24 hours a day, one would expect higher load factors for schools, which can turn virtually everything in the night. Also many things contribute to a load factor of the building. A building left on 24 hours a day can still have a load low factor if there are large peaks of each month – for example, a 20-bed hospital is scheduled MRI truck visits once every month. RM demand is large, and may affect Load Factor greatly from a small installation.

As a benchmark, you can determine its own rules of thumb for their buildings, however, their range of acceptable load factors will vary depending on building type and climate. rules of thumb may not be as useful, however. Like Benchmarking, only the identification buildings with unusually high load factors and low, in relation to other buildings in the portfolio, should be sufficient.

Load Factor Sum
Factor occupation can be used to identify billing errors and measurement, the buildings are not shut down the computer, and buildings with the demands suspiciously high. Although benchmarking can identify the buildings most likely to produce large gains of energy efficiency, load factor target analysis can easily resolve programming problems and measurement.

WEATHER NORMALIZATION
Another important method of utility bill analysis is normalization utility bill to the weather. Weather Normalization allows the power manager to determine whether the installation of energy saving or increased energy consumption, without worrying about climate change.

Suppose an energy manager will replace the current chilled water system in a building with a more efficient system. He is likely to wait to see the energy and cost savings from this change. Figure 7 presents the results of energy manager might expect.

But what if, instead, the projects presented the disaster is shown in Figure 8?

A quarter of a million dollar adjustment is difficult to justify with results like this. And yet, the energy manager knows that everything in the conversion was as expected. What made these results?

Clearly, the energy manager can not present these results without any reason or justification. Management may just look at the figures and, since the numbers do not lie, the conclusion that they have hired the wrong energy manager!

There are many reasons why adaptation had not delivered the expected savings. One possibility is that the project is the delivery of savings, but the summer after the conversion was much warmer than the summer before the upgrade. warmer summers result in higher air conditioning loads, which often results in utility bills higher.

Hottest Summer -> Top Loading Air Conditioning -> higher summer bills

In other words, the new equipment really save energy, as was working more efficiently than the old. The figures do not show this because this summer was much hotter than last summer.

If the weather was really the cause of increased consumption, then how could you ever use utility bills to measure the savings of energy efficiency projects (especially when you can make excuses for poor performance, as we do)? His numbers savings would be at the mercy of time. Saving numbers would be of no value (Unless the weather was the same year after year).

Our example may seem a bit exaggerated, but it begs the question: Would the weather really have such an impact on the numbers of savings?

You can, but usually not this extreme. The summer of 2005 was the hottest summer in a century of records in Detroit, Michigan. 90degF There were 18 days or more compared to the normal 12 days. In addition, the average temperature in Detroit was 74.8degF compared with 71.4 DegF normal. At first glance, 3 degrees does not seem all that much, however, if you convert the temperatures of cooling degree days, as shown in Figure 9, results are dramatic. Just comparing the period from June to August, there were 909 days Cooling degree in 2005 compared with 442 cooling degree days in 2004. This is more than double! Cooling degree days are roughly proportional to the building on cooling needs. For Detroit then one can infer that an average building required (and possibly consumption) more than twice the amount of energy for cooling in the summer of 2005 to the summer of 2004. It is likely in the upper midwestern United States had been a number of energy managers, faced exactly this problem!

What is a energy manager will show the savings of a chilled water system conversion in these circumstances? A simple comparison of utility bills do not work because the expected savings are achieved buried beneath the cooling load increased. The solution would be to apply the information in time to the same before and after adaptation accounts, and then there would be no penalty for extreme weather conditions. This is exactly what the standards of the time it does. To demonstrate the savings modernization (or other energy management practice), and to prevent such a disaster, an energy manager must normalize the utility bills for the time for the changes in weather conditions that do not jeopardize the savings numbers.

More and more managers of energy, and normalize their utility bills public to the climate because they want to be able to demonstrate that they are actually saving energy in their energy management efforts.

In many packages software, can establish the relationship between time and use in a single click. Because clicking on "tunings" software that gives not always acceptable, yes helps to understand the underlying theory and methodology that can identify the problem and make tuning adjustments. The more you know about the topic, the better. The following section elaborates the basic elements of weather normalization.

How the Weather Works Standards
Instead of comparing use last year using this year, when we use the normalization of the time, compares the amount of energy that would have used this year to form amount of energy that has used this year. Many in our industry do not call the result of this comparison, the savings, but rather "Use Prevention" or "cost reduction" (if the costs of comparing). Since we are trying to keep this treatment at an introductory level, we will only use the word savings.

When we try to compare the use of last year using this year we have seen the disastrous project in Figure 8. We used the equation:

Saving = use last year – this year using

When we normalize the weather, the same data results in figure 10 and used the equation:

Savings = How much energy you would have spent this year – this year using

The next question is how calculate the amount of energy you would have spent this year? This is where the normalization comes into play time

First, select one years utility bills to those who want to compare future use. This normally would be the year before starting their energy efficiency program, the year before installing a conversion, or in the past few years you want to compare the current use. In this example, we select the year of relevant data before installation of chilled water system. We will call this year the Base Year.

Then calculate degree days for the billing periods the base year. Because this example only applies to refrigeration, we need only collect Cooling Degree Days.

Base Year bills and Cooling Degree Days are then normalized by the number of days, as shown in Figure 11. Normalizing by the number of days (In this case, simply dividing by the number of days) removes any noise associated with different lengths invoice period. This is done automatically by the canned software should be available when other means were employed.

To establish the relationship between use and time, we find the line closest to all bills. This line, best fit line, is using statistical techniques of regression in the net income available canned software to monitor and bill spreadsheets.

The next step is to ensure that the best fit line is good enough for use. The quality of the line of best fit is represented by statistical indicators, the most common of which is the value of R2. The value of R2 represents the goodness of fit, and in the circles of power engineering, an R2> 0.75 is considered an acceptable fit. Some meters have a sensitivity little or no time or there may be other unknown variables that have a greater influence on the use of time. These meters can have a low value of R2. You can generate values of R2 for the fit line in Excel or other Bill canned utility tracking software.

The Best Fit Line has an equation, what we call the Fit Line Equation, or this case the baseline equation. The adjustment of the line equation in Figure 11 could be:

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

Once we have this equation, it makes the process of regression.

Base Year bills ~ = Fit Line = Fit Line Equation

The Fit the equation represents the line as its power plant used during the year base, and continue to use energy in the future (in response to changing weather conditions) assuming no significant changes in the construction consumption patterns.

Once you have the Baseline Equation, you can determine if you have saved no energy. How? You take a project legislation of some billing period after the Base Year. Then plug the number of days of your invoice and the number of Degree Days Cooling the billing period in the baseline equation.

Assume for a bill this month, 30 days and 100 were associated with periods CDD billing.

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

Baseline kWh =
(5 kWh / day * 30) + (417 kWh / CDD * 100)

kWh reference: 41 850 kWh

Remember that the base line represents Equation how to use the power of his building in the base year. Therefore, with the new entries of number of days and the number of degree days, the line equation base will tell you how much energy the building have been used this year based on usage patterns and conditions of the Base Year this year (weather and number of days). We call this usage that is determined by the baseline equation, use of baseline.

Now, to obtain a reasonable estimate energy savings are compared:

Savings = How much energy you would have spent this year – How much energy we used this year

Or if we change a bit of terminology:

Energy savings = Baseline – Actual energy use

baseline in the use of energy is calculated from the baseline equation, using the time of the current month and the number of days, and the real energy use is the current month's invoice.

So, using our example, suppose this month's bill was 30,000 kWh:

Saving energy = baseline – use of real power

Savings = 41,850 kWh – 30,000 kWh

Saving = 11 850 kWh

ABSTRACT
Utility bill tracking is in the center of an energy management system of success, but the bills must be used for the analysis of sound to any significant reduction of energy use. By applying the three methods of analysis presented here (benchmarking, load factor analysis, and standardization of the time), the manager of energy can develop insight that should lead to appropriate decisions in managing energy.

About the Author

Abraxas Energy Consulting performs commercial energy audits and provides utility bill tracking, energy auditing, measurement and verification, retro-commissioning, utility bill auditing and other energy management services for its clients world-wide. In addition, Abraxas Energy Consulting provides a selection of utility bill tracking and interval data software packages for its clients. Abraxas Energy Consulting’s clients are ESCOs, energy consultants, governments, universities, hospitals, school districts, private industry and building owners.

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