| -- End Ad Box ---> | | | | to the energy used for cooling a building, while |
| UTILITY BILL TRACKING: THE REPORT CARD FOR | | | | Heating Degree Days, (HDD) are roughly proportional |
| ALTERNATIVE ENERGY CONTRACTORS | | | | to the energy used for heating a building. Degree |
| More and more, alternative energy contractors want | | | | Days, although simply calculated, are quite useful in |
| to prove to customers the savings they expect. | | | | energy calculations. They are calculated for each day, |
| Customers often want to know that they have | | | | and then are summed over some period of time |
| saved the energy and costs they were originally | | | | (months, a year, etc.).(8) |
| promised. From the customers’ viewpoint, the | | | | Figure 1.7: Determining the balance point using a kWh |
| simplest and most understandable proof of energy | | | | day vs. Outdoor Temperature graph ( |
| savings comes from a simple comparison of | | | | In general, daily degree days are the difference |
| electricity bills. Did the system save on electricity | | | | between the building’s balance point and the |
| costs or not?(1) In theory, a simple comparison of | | | | average outside temperature. To understand degree |
| pre-installation bills to post-installation bills, and you will | | | | days, then, we first need to understand the concept |
| see if you have saved. | | | | of Balance Points. |
| But if it is so easy, why write a paper on this? Well, | | | | Buildings have their own set of Balance Points for |
| it isn’t so easy. Let’s find out why. | | | | heating and for cooling – and they may not be |
| Figure 1.1: Expected Pre and Post-Retrofit usage for | | | | the same. The Heating Balance Point can be defined |
| chilled water system retrofit. ( | | | | as the outdoor temperature at which the building |
| Suppose a solar energy contractor installed a new | | | | starts to heat. In other words, when the outdoor |
| solar electric system for a building. One likely would | | | | temperature drops below the Heating Balance Point, |
| expect to see energy and cost savings from this | | | | the building’s heating system kicks in. |
| retrofit. Figure 1.1 presents results our alternative | | | | Conversely, when the outdoor temperature rises |
| energy contractor might expect. | | | | above the Cooling Balance Point, the building starts to |
| But what if, instead, the bills presented the disaster | | | | cool.(9) A building’s balance point is |
| shown in Figure 1.2? | | | | determined by nearly everything associated with it, |
| Figure 1.2: A disaster of a project? Comparison of | | | | since nearly every component associated with a |
| Pre-Retrofit and Post-Retrofit data ( | | | | building has some effect on the heating of the |
| Imagine showing a customer these results after they | | | | building: building envelope construction (insulation |
| have invested hundreds of thousands of dollars in | | | | values, shading, windows, etc.), temperature set |
| your system. It is hard to inspire confidence in your | | | | points, thermostat set back schedules if any, the |
| abilities with results like this. | | | | amount of heat producing equipment (and people) in |
| How should the solar energy contractor present this | | | | the building, lighting intensity, ventilation, HVAC |
| data to customer? Do you think the contractor | | | | system type, HVAC system schedule, lighting and |
| would be feeling confident about the job, and about | | | | miscellaneous equipment schedules, among other |
| getting referrals for future solar projects? Probably | | | | factors. |
| not. The customer might simply look at the figures | | | | In the past, before energy professionals used |
| and, since figures don’t lie, conclude they | | | | computers and utility manager software in their |
| have hired the wrong contractor, and that the solar | | | | everyday tasks, degree day analysis was simplified |
| system doesn’t work very well! | | | | by assuming balance points of 65°F for both |
| There are many reasons the system may not have | | | | heating and cooling. As a result, it was easy to publish |
| delivered the expected savings. A possibility is that | | | | and distribute degree days, since everyone calculated |
| the project is delivering savings, but the summer | | | | them using the same standard (that is, using 65°F |
| after the installation was much hotter than the | | | | as the balance point). It is more accurate, though, to |
| summer before the installation. Hotter summers | | | | recognize that every building has its own balance |
| translate into higher air conditioning loads, which could | | | | points, and to calculate degree days accordingly. |
| result in higher utility bills. | | | | Consequently, you are less likely to see degree days |
| Hotter Summer >> Higher Air Conditioning Load >> | | | | available, as more sophisticated analysis requires you |
| Higher Summer Utility Bills | | | | to calculate your own degree days based upon your |
| In our example, we are claiming that because the | | | | own building’s balance points.(10) |
| post-installation weather was hotter, the solar electric | | | | Figure 1.8: kWh /day vs Average Outdoor |
| project looked like it didn’t save any energy, | | | | Temperature ( |
| even though it really did. Imagine explaining that to | | | | To find the balance point temperature of a building, |
| customers! | | | | graph the Usage/Day against Average Outdoor |
| If the weather really was the cause of the higher | | | | Temperature (of the billing period) as shown in Figure |
| usage, then how could you ever use utility bills to | | | | 1.7. Notice that Figure 1.7 presents two trends. One |
| measure savings from solar energy projects? Your | | | | trend is flat, and the other trend slopes up and to |
| savings numbers would be at the mercy of the | | | | the right. We have drawn red lines signifying the two |
| weather. Savings numbers would be of no value at all | | | | trends in Figure 1.8. (Ignore the vertical red line for |
| (unless the weather was the same year after year). | | | | now.) The flat trend represents Non-Temperature |
| Our example may appear a bit exaggerated, but it | | | | Sensitive Consumption, which is electrical consumption |
| begs the question: Could weather really have such an | | | | that is not related to weather. In Figure 1.7, |
| impact on savings numbers? | | | | Non-Temperature Sensitive Consumption is roughly |
| It can, but usually not to this extreme. The summer | | | | the same every month, about 2450 kWh per day. |
| of 2005 was the hottest summer in a century of | | | | Examples of Non-Temperature Sensitive Consumption |
| record-keeping in Detroit, Michigan. There were 18 | | | | include lighting, computers, miscellaneous plug load, |
| days at 90°F or above, compared to the usual 12 | | | | industrial equipment and well pumps. Any usage |
| days. In addition, the average temperature in Detroit | | | | above the horizontal red line is called Temperature |
| was 74.8°F compared to the normal 71.4 °F. At | | | | Sensitive Consumption, which represents electrical |
| first glance, 3 degrees doesn’t appear | | | | usage associated with the building’s cooling |
| significant, however, if you convert the temperatures | | | | system. Notice that in Figure 1.8, the Temperature |
| to cooling degree days(2), as shown in Figure 1.3, the | | | | Sensitive Consumption only occurs at temperatures |
| results look dramatic. Just comparing the June | | | | greater than 61°F. The intersection of the two |
| through August period, there were 909 cooling | | | | trends is called the Balance Point, or Balance Point |
| degree days in 2005 as compared to 442 cooling | | | | Temperature, which is 61°F in this example. |
| degree days in 2004. | | | | Notice also that, in Figure 1.8, as the outdoor |
| That is more than double! Cooling Degree Days are | | | | temperature increases, consumption increases. As it |
| roughly proportional to relative building cooling | | | | gets hotter outside, the building uses more energy, |
| requirements. For Detroit then, one can infer that an | | | | thus the meter is used for cooling, but not heating. |
| average building required (and possibly consumed) | | | | The Balance Point Temperature we found is the |
| more than twice the amount of energy for cooling in | | | | Cooling Balance Point Temperature (not the Heating |
| the summer of 2005 than the summer of 2004. It is | | | | Balance Point Temperature). |
| likely that in the Upper Midwestern United States | | | | Figure 1.9: kWh/day vs Average Outdoor |
| there were several solar contractors who faced | | | | Temperature ( |
| exactly this problem! | | | | We can view the same type of graph for heating |
| Figure 1.3: Cooling Degree Days in Detroit, Michigan | | | | usage in Figure 1.9. Notice that the major difference |
| for 2004 and 2005 ( | | | | between the two graphs, is that the Temperature |
| How is a solar energy contractor going to show | | | | Sensitive trend slopes up and to the left (rather than |
| savings from a solar electric system under these | | | | up and the right). As the outdoor temperature drops, |
| circumstances? A simple comparison of utility bills will | | | | the building use more electricity to heat the building. |
| not work, as the expected savings will get buried | | | | Now that we have established our balance point |
| beneath the increased cooling load. The solution | | | | temperature, we have all the information required to |
| would be to somehow apply the same weather data | | | | calculate Degree Days. If your graph resembles |
| to the pre- and post-installation bills. Then there would | | | | Figures 1.9, you will be using Heating Degree Days. If |
| be no penalty for extreme weather. This is exactly | | | | your graph resembles Figure 1.8, you will be using |
| what weather normalization does. To show savings | | | | Cooling Degree Days. |
| from a retrofit (or good alternative energy practice), | | | | Figure 1.10: Daily Usage Normalized to Production and |
| and to avoid our disastrous example, an alternative | | | | Weather. The Baseline Equation is Shown at the |
| energy contractor should normalize the utility bills for | | | | Bottom of the Figure ( |
| weather, so that changes in weather conditions will | | | | NORMALIZING FOR OTHER VARIABLES |
| not compromise the savings numbers. | | | | More and more energy professionals are coming to |
| The practice of normalizing energy bills to weather | | | | understand the value of normalizing utility data for |
| with energy software is catching on, with more and | | | | production in addition to (or instead of) weather. This |
| more energy managers, energy engineers, and | | | | works if you have a simple variable that quantifies |
| contractors correcting their bills for weather because | | | | your production. For example, a computer assembly |
| they want to be able to prove that they are actually | | | | plant can track the number of computers produced. |
| saving energy from their efforts. This process has | | | | If a factory manufactures several different products, |
| many names: weather correction, weather | | | | for example, disk drives, desktop computers, and |
| normalization, tuning to weather, tuning, or weather | | | | printers, it may be difficult to come up with a single |
| regression. | | | | variable that could be used to represent production |
| HOW WEATHER NORMALIZATION WORKS | | | | for the entire plant (i.e. tons of product). However, |
| Rather than compare last year’s usage to | | | | since analysis is performed on a meter level rather |
| this year’s usage, when we use weather | | | | than a plant level, if you have meters (or submeters) |
| normalization, we compare how much energy we | | | | that serve just one production line, then you can |
| would have used this year to how much energy we | | | | normalize usage from one meter with the product |
| did use this year. Many in our industry do not call the | | | | produced from that production line. |
| result of this comparison, “Savings”, | | | | Figure 1.10 presents normalized daily usage versus |
| but rather “Usage Avoidance” or | | | | production for a widget factory. The baseline |
| “Cost Avoidance” (if comparing costs). | | | | equation for this normalization is shown at the |
| But, since we are trying to keep this chapter at an | | | | bottom of the figure. Notice that Units of Production |
| introductory level, we will simply use the word | | | | (UPr) as well as Cooling Degree Days (CDD) are |
| Savings. | | | | included in the equation, meaning that this |
| When we tried to compare last year’s usage | | | | normalization included weather data and production |
| to this year’s usage, we saw Figure 1.2, and | | | | data. |
| a disastrous project. We used the equation: | | | | School districts, colleges, and universities often |
| Savings = Last year’s usage – This | | | | normalize for the school calendar. Real estate |
| year’s usage | | | | concerns, hotels and prisons normalize for occupancy. |
| When we normalize for weather, the same data | | | | Essentially any variable can be used for normalization, |
| results in Figure 1.4, and uses the equation: | | | | as long as it is an accurate, consistent predictor of |
| Savings = How much energy we would have used | | | | energy usage patterns. Again, these normalizations |
| this year – This year’s usage | | | | can be performed by specialized utility bill tracking |
| Figure 1.4: Comparison of Baseline and Actual | | | | software, or using spreadsheets. |
| (Post-Retrofit) data with Weather Correction ( | | | | CONCLUSION |
| The next question is, how do we figure out how | | | | Weather varies from year to year. As a result, it |
| much energy we would have used this year? That is | | | | becomes difficult to know whether the change in |
| where weather normalization comes in. | | | | your utility bills is due to fluctuations in weather, or |
| First, we select a year of utility bills(3) to which we | | | | due to your alternative energy system, or both. If |
| want to compare future usage. This would typically | | | | you wish to use utility bills to determine energy |
| be the year before you started your alternative | | | | savings from your alternative energy system with |
| energy program, the year before you installed a | | | | any degree of accuracy, it is vital that you remove |
| retrofit, or the year before you, the new energy | | | | the variability of weather from your energy savings |
| contractor, were hired, or just some year in the past | | | | equation. This is done using the weather normalization |
| that you want to compare current usage to. In this | | | | techniques described in this paper. You may adjust |
| example, we would select the year of utility data | | | | your usage for other variables as well, such as |
| before the installation of the solar electric system. | | | | occupancy or production. |
| We will call this year the Base Year(4). | | | | 1) What are the alternatives? The most common |
| Figure 1.5: Cooling Degree Days ( | | | | might involve determining savings for each of the |
| Then we calculate degree days for the Base Year | | | | energy conservation activities using a spreadsheet, or |
| billing periods. Because this example is only concerned | | | | perhaps even a building model. Both of these |
| with cooling, we need only gather Cooling Degree | | | | alternative strategies could require much additional |
| Days (not Heating Degree Days). A section on | | | | work, as the alternative energy contractor likely has |
| calculating Degree Days follows later in the chapter. | | | | employed several strategies over his tenure. One |
| For now, recognize only that Cooling Degree Days | | | | other drawback of spreadsheets is that energy |
| need to be gathered at this step.(5) Figure 1.5 | | | | conservation strategies may interact with each other, |
| presents Cooling Degree Days over two years. | | | | so that total savings may not be the sum of the |
| Figure 1.6: Finding the relationship between usage and | | | | different strategies, and finally, spreadsheets are |
| weather data. The blue dots represent the utility bills. | | | | often projections of energy savings, not |
| The red line is the best fit line. ( | | | | measurements. |
| To establish the relationship between usage and | | | | 2) Cooling degree days are defined in detail later in |
| weather, we find the line that comes closest to all | | | | the chapter, however a rough meaning is given here. |
| the bills. This line, the Best Fit Line, is found using | | | | Cooling Degree Days are a rough measure of how |
| statistical regression techniques available in canned | | | | much a period's weather should result in a |
| utility bill tracking software and in spreadsheets. | | | | building’s cooling requirements. A hotter day |
| The next step is to ensure that the Best Fit Line is | | | | will result in more Cooling Degree Days, whereas a |
| good enough to use. The quality of the best fit line is | | | | colder day may have no Cooling Degree Days. Double |
| represented by statistical indicators, the most | | | | the amount of Cooling Degree Days should result in |
| common of which, is the R2 value. The R2 value | | | | roughly double the cooling requirements for a building. |
| represents the goodness of fit, and in energy | | | | Cooling Degree Days are calculated individually for |
| engineering circles, an R2 > 0.75 is considered an | | | | each day. Cooling Degree Days over a month or billing |
| acceptable fit. Some meters have little or no | | | | period, are merely a summation of the Cooling |
| sensitivity to weather or may have other unknown | | | | Degree Days of the individual days. The same is true |
| variables that have a greater influence on usage than | | | | for Heating Degree Days. |
| weather. These meters may have a low R2 value. | | | | 3) Some energy professionals select 2 years of bills |
| You can generate R2 values for the fit line in Excel or | | | | rather than one. Good reasons can be argued both |
| other canned utility bill tracking software.(6) | | | | for choosing one year or two years. Do not choose |
| This Best Fit Line has an equation, which we call the | | | | periods of time that are not in intervals of 12 months |
| Fit Line Equation, or in this case the Baseline | | | | (for example, 15 months, or 8 months could lead to |
| Equation.(7) The Fit Line Equation from Figure 1.6 | | | | inaccuracy). |
| might be: | | | | 4) Please do not confuse Base Year with Baseline. |
| Baseline kWh = (5 kWh/Day * #Days) + (417 kWh | | | | Base Year is a time period, from which bills were |
| CDD * #CDD) | | | | used to determine the building’s energy |
| Once we have this equation, we are done with this | | | | usage patterns with respect to weather data, |
| regression process. | | | | whereas Baseline, as will be described later, |
| Let’s recap what we have done: | | | | represents how much energy we would have used |
| We normalized Base Year utility bills and weather | | | | this month, based upon Base Year energy usage |
| data for number of days in the bill. | | | | patterns, and current month conditions (i.e. weather |
| We graphed normalized Base Year utility data versus | | | | and number of days in the bill). |
| normalized weather data. | | | | 5) Canned energy software does this automatically |
| We found a Best Fit Line through the data. The Best | | | | for you, while in spreadsheets, this step can be |
| Fit Line then represents the utility bills for the Base | | | | tedious. |
| Year. | | | | 6) The statistical calculations behind the R2 value, and |
| The Best Fit Line Equation represents the Best Fit | | | | a treatment of three other useful indicators, |
| Line, which in turn represents the Base Year of utility | | | | T-Statistic, Mean Bias Error, and CVRMSE are not |
| data. | | | | treated in this chapter. For more information on these |
| Base Year bills ≈ Best Fit Line = Fit Line | | | | statistical concepts, consult any college statistics |
| Equation | | | | textbook. (For energy contractors, a combination of |
| The Fit Line Equation represents how your customer | | | | R2 values and T-Statistics is usually enough.) |
| used energy during the Base Year, and would | | | | 7) Baseline Equation = Fit Line Equation +/- Baseline |
| continue to use energy in the future (in response to | | | | Modifications. We cover Baseline Modifications later in |
| changing weather conditions) assuming no significant | | | | this chapter. |
| changes occurred in building consumption patterns. | | | | 8) You would not sum or average high or low |
| Once you have the Baseline Equation, you can | | | | temperatures for a period of time, as the result |
| determine if you saved any energy. | | | | would not be useful. However, you can sum degree |
| How? You take a bill from some billing period after | | | | days, and the result remains useful, as it is |
| the Base Year. You (or your software) plug in the | | | | proportional to the heating or cooling requirements of |
| number of days from your bill and the number of | | | | a building. |
| Cooling Degree Days from the billing period into your | | | | 9) If you think about it, you don’t have to |
| Baseline Equation. | | | | treat this at the building level, but rather can view it |
| Suppose for a current month’s bill, there | | | | at a meter level. (To simplify the presentation, we |
| were 30 days and 100 CDD associated with the billing | | | | are speaking in terms of a building, as it is less |
| period. | | | | abstract.) Some buildings have many meters, some |
| Baseline kWh = (5 kWh/Day * #Days) + (417 kWh | | | | of which may be associated with different central |
| CDD * #CDD) | | | | plants. In such a building, it is likely that the disparate |
| Baseline kWh = (5 kWh/Day * 30) + (417 kWh/CDD | | | | central plants would have different balance points, as |
| * 100) | | | | conditions associated with the different parts of the |
| Baseline kWh = 41,850 kWh | | | | building may be different. |
| Remember, the Baseline Equation represents how | | | | 10) If you calculate degree days by hand, or using a |
| your customer used energy in the Base Year. So, | | | | spreadsheet, you would use the following formulae |
| with the new inputs of number of days and number | | | | for your calculations. Of course, commercially available |
| of degree days, the Baseline Equation will tell you | | | | utility manager software that performs weather |
| how much energy the building would have used this | | | | nomalization handles this automatically. |
| year based upon Base Year usage patterns and this | | | | For each day, |
| year’s conditions (weather and number of | | | | HDDi = [ TBP – ( Thi + Tlo ) / 2 ] x 1 Day+ |
| days). We call this usage that is determined by the | | | | CDDi = [ ( Thi + Tlo ) / 2 – TBP ] x 1 Day+ |
| Baseline Equation, Baseline Usage. | | | | Where: |
| Now, to get a fair estimate of energy savings, we | | | | HDDi = Heating Degree Days for one day |
| compare: | | | | CDDi = Heating Degree Days for one day |
| Savings = How much energy we would have used | | | | TBP = Balance Point Temperature, |
| this year – How much energy we did use this | | | | Thi = Daily High Temperature |
| yearor if we change the terminology a bit: | | | | Tlo = Daily Low Temperature |
| Savings = Baseline Energy Usage – Actual | | | | + signifies that you can never have negative degree |
| Energy Usagewhere Baseline Energy Usage is | | | | days. If the HDDi or CDDi calculation yields a negative |
| calculated by the Baseline Equation, using current | | | | number, then the result is 0 degree days for that |
| month’s weather and number of days, and | | | | day. |
| Actual Energy Usage is the current month’s | | | | Heating and Cooling Degree Days can be summed, |
| bill. Both equations immediately preceding are the | | | | respectively, over several days, a month, a billing |
| same, as Baseline = “How much energy we | | | | period, a year, or any interval greater than a day. For |
| would have used this year”, and Actual | | | | a billing period (or any period greater than a day), |
| represents “How much energy we did use | | | | HDD = ΣHDDi |
| this year.” | | | | CDD = ΣCDDi |
| So, using our example, suppose this month’s | | | | Take a look back to Figure 1.3, where you may have |
| bill was for 30,000 kWh: | | | | noticed that there are more than twice as many |
| Savings = Baseline Energy Usage – Actual | | | | Cooling Degree Days (CDD) in August 2005 than in |
| Energy Usage | | | | August 2004. Because Cooling Degree Days are |
| Savings = 41,850 kWh – 30,000 kWh | | | | roughly proportional to a building’s cooling |
| Savings = 11,850 kWh | | | | energy usage, one could rightly assume that the |
| CALCULATING DEGREE DAYS AND FINDING THE | | | | cooling requirements of the building would be roughly |
| BALANCE POINT | | | | double as well. |
| Cooling Degree Days (CDD) are roughly proportional | | | | |