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An Introduction To Climate Normalization Of Utility Bills For Option Energy Constractors

UTILITY BILL TRACKING: THE REPORT CARD FOR Alternative Power CONTRACTORS
A lot more and more, alternative energy contractors want to prove to consumers the savings they count on. Clients usually need to understand that they've saved the power and costs they were originally promised. From the customers' viewpoint, the simplest and most understandable proof of power savings comes from a simple comparison of electrical energy bills. Did the method save on electrical energy expenses or not? In theory, a basic comparison of pre-installation bills to post-installation bills, and you will see if you have saved.

But if it really is so simple, why create a paper on this? Effectively, it is not so effortless. Let's locate out why.

Suppose a solar energy contractor installed a brand new solar electric method for a building. One particular most likely would expect to find out energy and cost savings from this retrofit. Figure 1.1 presents results our alternative energy contractor may well anticipate.


Figure 1.1: Anticipated Pre and Post-Retrofit usage for chilled water method retrofit.

But what if, rather, the bills presented the disaster shown in Figure 1.two?



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

Envision displaying a consumer these results after they've invested hundreds of a large number of dollars within your technique. It truly is difficult to inspire self-assurance in your abilities with results like this.

How ought to the solar energy contractor present this information to consumer? Do you consider the contractor could be feeling confident in regards to the job, and about getting referrals for future solar projects? Almost certainly not. The buyer may possibly basically take a look at the figures and, considering that figures do not lie, conclude they have hired the incorrect contractor, and that the solar program doesn't perform really well!

You will find numerous reasons the system may possibly not have delivered the expected savings. A possibility is the fact that the project is delivering savings, however the summer season following the installation was a lot hotter than the summer time ahead of the installation. Hotter summers translate into higher air conditioning loads, which could result in higher utility bills.

Hotter Summer season -> Higher Air Conditioning Load -> Larger Summer season Utility Bills

In our example, we're claiming that since the post-installation weather was hotter, the solar electric project looked like it did not save any energy, although it genuinely did. Envision explaining that to buyers!

If the weather actually was the reason for the greater usage, then how could you ever use utility bills to measure savings from solar energy projects? Your savings numbers could be at the mercy of the climate. Savings numbers could be of no worth at all (unless the weather was the identical year soon after year).

Our example may possibly appear a bit exaggerated, however it begs the query: Could weather truly have such an influence on savings numbers?

It could, but normally not to this intense. The summer season of 2005 was the hottest summer season within a century of record-keeping in Detroit, Michigan. There were 18 days at 90F or above, compared to the usual 12 days. In addition, the average temperature in Detroit was 74.8F in comparison to the standard 71.four F. Initially glance, three degrees doesn't seem important, nonetheless, in the event you convert the temperatures to cooling degree days , as shown in Figure 1.3, the results appear dramatic. Just comparing the June via August period, there were 909 cooling degree days in 2005 as in comparison to 442 cooling degree days in 2004. That is certainly greater than double! Cooling Degree Days are roughly proportional to relative constructing cooling requirements. For Detroit then, a single can infer that an average creating needed (and possibly consumed) more than twice the level of energy for cooling in the summer season of 2005 than the summer time of 2004. It is likely that inside the Upper Midwestern United states of america there have been many solar contractors who faced exactly this difficulty!

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

How is a solar energy contractor going to show savings from a solar electric system beneath these circumstances? A simple comparison of utility bills won't perform, because the anticipated savings will get buried beneath the elevated cooling load. The answer would be to somehow apply the same weather data to the pre- and post-installation bills. Then there will be no penalty for extreme weather. This can be exactly what weather normalization does. To show savings from a retrofit (or good alternative energy practice), and to avoid our disastrous instance, an alternative power contractor need to normalize the utility bills for weather, in order that alterations in weather conditions is not going to compromise the savings numbers.

The practice of normalizing power bills to weather is catching on, with more and much more energy managers, energy engineers, and contractors correcting their bills for weather since they need to be capable of prove that they are actually saving power from their efforts. This process has many names: climate correction, weather normalization, tuning to climate, tuning, or climate regression.

HOW Weather NORMALIZATION Works
Rather than evaluate final year's usage to this year's usage, when we use weather normalization, we examine just how much energy we would have utilised this year to just how much energy we did use this year. Numerous in our industry usually do not contact the outcome of this comparison, 'Savings', but rather 'Usage Avoidance' or 'Cost Avoidance' (if comparing expenses). But, considering that we are trying to maintain this chapter at an introductory level, we will merely make use of the word Savings.

When we tried to evaluate final year's usage to this year's usage, we saw Figure 1.two, plus a disastrous project. We utilised the equation:
Savings = Final year's usage - This year's usage

When we normalize for weather, precisely the same information benefits in Figure 1.four, and utilizes the equation:

Savings = How much energy we would have utilised this year - This year's usage


Figure 1.four: Comparison of Baseline and Actual (Post-Retrofit) data with Weather Correction

The following query is, how do we figure out how much power we would have utilised this year? That's where weather normalization comes in.

1st, we choose a year of utility bills to which we need to compare future usage. This would usually be the year before you started your option energy system, the year before you decide to installed a retrofit, or the year before you decide to, the new power contractor, have been hired, or just some year within the past that you wish to examine existing usage to. In this instance, we would select the year of utility data before the installation in the solar electric technique. We'll call this year the Base Year .

Then we calculate degree days for the Base Year billing periods. Since this instance is only concerned with cooling, we
require only collect Cooling Degree Days (not Heating Degree Days). A section on calculating Degree Days follows later within the chapter. For now, recognize only that Cooling Degree Days have to be gathered at this step. Figure 1.5 presents Cooling Degree Days more than two years.

Figure 1.five 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 quantity of days (in this case, merely, dividing by number of days) removes any noise related with different bill period lengths. This can be done automatically by canned computer software, and would need to be performed by hand if other indicates were employed.

Figure 1.6: Finding the connection between usage and climate data. The blue dots represent the utility bills. The red line is the very best match line.

To establish the partnership in between usage and weather, we find the line that comes closest to all the bills. This line, the most effective Match Line, is located using statistical regression tactics offered in canned utility bill tracking software and in spreadsheets.

The next step is always to ensure that the top Match Line is great sufficient to make use of. The good quality from the ideal fit line is represented by statistical indicators, one of the most frequent of which, will be the R2 value. The R2 worth represents the goodness of match, and in energy engineering circles, an R2 > 0.75 is deemed an acceptable match. Some meters have tiny or no sensitivity to weather or may have other unknown variables which have a higher influence on usage than climate. These meters might possess a low R2 worth. You are able to produce R2 values for the match line in Excel or other canned utility bill tracking computer software.

This Best Fit Line has an equation, which we contact the Fit Line Equation, or in this case the Baseline Equation. The Match Line Equation from Figure 1.6 may be:

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

When we've got this equation, we are carried out with this regression procedure.

Let's recap what we have accomplished:
1.We normalized Base Year utility bills and climate information for number of days in the bill.
2.We graphed normalized Base Year utility information versus normalized climate information.
three.We discovered a Ideal Fit Line by means of the information. The very best Fit Line then represents the utility bills for the Base Year.
four.The most effective Fit Line Equation represents the top Fit Line, which in turn represents the Base Year of utility information.

Base Year bills (roughly)= Greatest Match Line = Match Line Equation

The Match Line Equation represents how your client used energy throughout the Base Year, and would continue to use power in the future (in response to changing climate conditions) assuming no considerable alterations occurred in developing consumption patterns.

After you have the Baseline Equation, you'll be able to establish if you saved any power.

How? You take a bill from some billing period right after the Base Year. You (or your application) plug within the number of days out of your bill as well as the number of Cooling Degree Days in the billing period into your Baseline Equation.

Suppose for a present month's bill, there have been 30 days and 100 CDD associated using the billing period.

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

Baseline kWh = ( five kWh/Day * 30 ) + ( 417 kWh/CDD * 100 )

Baseline kWh = 41,850 kWh

Don't forget, the Baseline Equation represents how your consumer used energy inside the Base Year. So, with all the new inputs of number of days and number of degree days, the Baseline Equation will inform you just how much power the developing would have employed this year based upon Base Year usage patterns and this year's conditions (climate and quantity of days). We call this usage that's determined by the Baseline Equation, Baseline Usage.

Now, to acquire a fair estimate of energy savings, we compare:

Savings = Just how much energy we would have used this year - How much power we did use this year

or if we change the terminology a bit:

Savings = Baseline Power Usage - Actual Energy Usage

where Baseline Energy Usage is calculated by the Baseline Equation, utilizing existing month's climate and number of days, and Actual Energy Usage would be the present month's bill. Both equations instantly preceding would be the same, as Baseline = 'How much energy we would have used this year', and Actual represents 'How considerably power we did use this year.'

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

Savings = Baseline Energy Usage - Actual Power Usage

Savings = 41,850 kWh - 30,000 kWh

Savings = 11,850 kWh

CALCULATING DEGREE DAYS AND Obtaining THE BALANCE POINT

Cooling Degree Days (CDD) are roughly proportional towards the energy used for cooling a creating, even though Heating Degree Days, (HDD) are roughly proportional for the power utilised for heating a building. Degree Days, even though basically calculated, are really valuable in power calculations. They may be calculated for each day, and after that are summed more than some time frame (months, a year, and so on.).

In general, everyday degree days will be the difference between the building's balance point along with the typical outdoors temperature. To understand degree days, then, we very first need to comprehend the notion of Balance Points.

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


Buildings have their very own set of Balance Points for heating and for cooling -- and they might not be the same. The Heating Balance Point might be defined because the outside temperature at which the constructing begins to heat. In other words, when the outside temperature drops beneath the Heating Balance Point, the building's heating method kicks in. Conversely, when the outdoor temperature rises above the Cooling Balance Point, the developing starts to cool. A building's balance point is determined by nearly every thing associated with it, considering that practically every single component linked having a developing has some effect on the heating on the creating: building envelope building (insulation values, shading, windows, etc.), temperature set
points, thermostat set back schedules if any, the level of heat generating equipment (and men and women) in the developing, lighting intensity, ventilation, HVAC program variety, HVAC program schedule, lighting and miscellaneous equipment schedules, among other variables.

Inside the previous, prior to energy professionals employed computers in their daily tasks, degree day evaluation was simplified by assuming balance points of 65F for each heating and cooling. As a result, it was effortless to publish and distribute degree days, because every person calculated them utilizing the same standard (that is, utilizing 65F because the balance point). It's much more precise, though, to recognize that every developing has its personal balance points, and to calculate degree days accordingly. Consequently, you happen to be much less probably to determine degree days offered, as more sophisticated analysis requires you to calculate your own personal degree days based upon your very own building's balance points.

To seek out the balance point temperature of a constructing, graph the Usage/Day against Typical Outdoor Temperature (of the billing period) as shown in Figure 1.7. Notice that Figure 1.7 presents two trends. One particular trend is flat, along with the other trend slopes up and for the correct. We have drawn red lines signifying the two trends in Figure 1.eight. (Ignore the vertical red line for now.) The flat trend represents Non-Temperature Sensitive Consumption, that is electrical consumption that is certainly not associated to weather. In Figure 1.7, Non-Temperature Sensitive Consumption is roughly the identical each month, about 2450 kWh per day. Examples of Non-Temperature Sensitive Consumption consist of lighting, computers, miscellaneous plug load, industrial gear and properly pumps. Any usage above the horizontal red line is known as Temperature Sensitive Consumption, which represents electrical usage related with all the building's cooling system. Notice that in Figure 1.8, the Temperature Sensitive Consumption only occurs at temperatures higher than 61F. The intersection from the two trends is named the Balance Point, or Balance Point Temperature, which is 61F within this example.


Figure 1.8: kWh /day vs Typical Outside Temperature

Notice also that, in Figure 1.eight, because the outside temperature increases, consumption increases. Since it gets hotter outdoors, the creating makes use of more energy, as a result the meter is employed for cooling, but not heating. The Balance Point Temperature we found may be the Cooling Balance Point Temperature (not the Heating Balance Point Temperature).


Figure 1.9: kWh/day vs Typical Outside Temperature

We can view the identical variety of graph for heating usage in Figure 1.9. Notice that the main difference among the two graphs, is the fact that the Temperature Sensitive trend slopes up and towards the left (as opposed to up and also the proper). Because the outside temperature drops, the constructing use much more electrical energy to heat the creating.

Now that we have established our balance point temperature, we have all the data needed to calculate Degree Days. If your graph resembles Figures 1.9, you will be using Heating Degree Days. In case your graph resembles Figure 1.eight, you will be making use of Cooling Degree Days.


NORMALIZING FOR OTHER VARIABLES
Far more and much more power specialists are coming to know the value of normalizing utility data for production along with (or as an alternative of) climate. This performs if you possess a simple variable that quantifies your production. As an example, a computer assembly plant can track the number of computers created. If a factory manufactures several various goods, for example, disk drives, desktop computers, and printers, it might be challenging to come up having a single variable that could be employed to represent production for the entire plant (i.e. tons of item). Nevertheless, given that analysis is performed on a meter level instead of a plant level, in case you have meters (or submeters) that serve just one production line, then you definitely can normalize usage from 1 meter with the item made from that production line.

Figure 1.10 Day-to-day Usage Normalized to Production and Weather. The Baseline Equation is Shown at the Bottom of the Figure

Figure 1.10 presents normalized every day usage versus production for any widget factory. The baseline equation for this normalization is shown at the bottom on the figure. Notice that Units of Production (UPr) as well as Cooling Degree Days (CDD) are integrated inside the equation, which means that this normalization incorporated climate data and production data.

College districts, colleges, and universities usually normalize for the school calendar. Real estate concerns, hotels and prisons normalize for occupancy. Basically any variable could be employed for normalization, as long as it really is an precise, consistent predictor of energy usage patterns. Again, these normalizations might be performed by specialized utility bill tracking software, or employing spreadsheets.

CONCLUSION
Climate varies from year to year. Because of this, it becomes difficult to understand whether or not the alter inside your utility bills is on account of fluctuations in weather, or on account of your alternative power method, or both. In case you wish to use utility bills to figure out power savings out of your alternative power system with any degree of accuracy, it really is important that you get rid of the variability of weather from your power savings equation. This is completed using the climate normalization methods described within this paper. You could adjust your usage for other variables as well, for example occupancy or production.