So, using our example, suppose this months bill was for 30,000 kWh: Cooling Degree Days (CDD) are roughly proportional to the energy used for cooling a building, while Heating Degree Days, (HDD) are roughly proportional to the energy used for heating a building. A nonparametric rank is literally the value's position in an ordered set of your data. But, since we are trying to keep this chapter at an introductory level, we will simply use the word Savings. To understand degree days, then, we first need to understand the concept of Balance Points. And Wednesday is 4 heating degree days (60 F 56F).

We can view the same type of graph for heating usage in Figure 1.9. To establish the relationship between usage and weather, we find the line that comes closest to all the bills. Notice that the major difference between the two graphs, is that the Temperature Sensitive trend slopes up and to the left (rather than up and the right). What are the heating and cooling degree days for the Monday through Wednesday period? The next question is, how do we figure out how much energy we would have used this year? Linear regression usually provides an equation that fairly represents this type of data. a separate article that discussed the link between weather-normalized electricity consumption and GDP. These meters may have a low R2 value. You can use this process to assemble similar population-weighted data of your own for the United States, or any other country or region, or for the world as a whole. [10] Some analysts had separate tables of degree days based upon a range of balance points (65, 60, 55, etc. Many of the online services actually do this using hourly weather data from airports or other reliable weather stations. Heating and Cooling Degree Days can be summed, respectively, over several days, a month, a billing period, a year, or any interval greater than a day. Thanks for taking the time to give us some feedback. Figure 1.2: A disaster of a project?

The average temperature over the 24-hour period on Monday is 66 F, The averagetemperature for Tuesday is 68 F, Then on Wednesday, a cold front comes in and the average temperature is only 56 F. ). This is usually done with a normalization equation and allows you to compare different sets of data. Click on it to download the CSV (comma separated values) file. and that the solar system doesnt work very well! In a 24-hour period, the temperature might stay high for much longer than it stays low, offsetting the average by producing more cooling degree days. The practice of normalizing energy bills to weather is catching on, with more and more energy managers, energy engineers, and contractors correcting their bills for weather because they want to be able to prove that they are actually saving energy from their efforts.

A base year can be 1 year or 5 years ago, it doesnt matter. Once we have this equation, we are done with this regression process. Base Year is a time period, from which bills were used to determine the buildings energy usage patterns with respect to weather data, whereas Baseline, as will be described later, represents how much energy we would have used this month, based upon Base Year energy usage patterns, and current month conditions (i. e. weather and number of days in the bill). Choose either heating or cooling for the type of degree days youre interested in. Got that important point? It is entered into the equation as a constant. [1] What are the alternatives? We saw the need to find an apple-to-apple method to compare the two periods. Heating and cooling degree days are always positive numbers. John Avina, C. E. M. It's one of many reasons to choose Degree Days.net over alternative data sources. Customers often want to know that they have saved the energy and costs they were originally promised. [1] In theory, a simple comparison of pre-installation bills to post-installation bills, and you will see if you have saved. The Weather Normalization Report is designed to give users only a general estimate of the effect of weather on utility consumption from year to year and a general estimate of any possible utility usage avoided from year to year. Click on the cell labeled B1 in the column B and type the following: STANDARDIZE()A1,C$1,C$2). Remember not to include the quotation marks when you type into the cell. If anything changes (like building additions, new or added equipment, more people, etc.)

The Balance Point Temperature we found is the Cooling Balance Point Temperature (not the Heating Balance Point Temperature). Degree Days, although simply calculated, are quite useful in energy calculations. Most energy professionals use specialized software to do these calculations. As it gets hotter outside, the building uses more energy, thus the meter is used for cooling, but not heating. Any signs of global warming?

Now that we have established our balance point temperature, we have all the information required to calculate Degree Days. This Best Fit Line has an equation, which we call the Fit Line Equation, or in this case the Baseline Equation. Of course, commercially available software that performs weather nomalization handles this automatically. For more information on these statistical concepts, consult any college statistics textbook. There were 18 days at 90F or above, compared to the usual 12 days. Then you come back and tell me that last August was much hotter than this August, so I didnt use as much air conditioning this year as last year. A possibility is that the project is delivering savings, but the summer after the installation was much hotter than the summer before the installation. So each figure covers consumption for Sunday, Monday, Tuesday, Wednesday, Thursday, Friday, and Saturday. One cooling degree day means that the average temperature for one day is one degree above the base temperature. [3] Some energy professionals select 2 years of bills rather than one. Normally youll want to leave the base temperature at 65 F. Click the Generate Degree Days button. Baseline kWh = ( 5 kWh/Day * 30 ) + ( 417 kWh/CDD * 100 ). Any usage above the horizontal red line is called Temperature Sensitive Consumption, which represents electrical usage associated with the buildings cooling system. [11]. (If you don't see "Data Analysis" in the "Data" tab, go to "File" -> "Options" -> "Add-Ins", click "Go" to manage "Excel Add-Ins", and enable the "Analysis ToolPak" there. Select the cell C2 and type in the following: STDEV.S(A1:AX). Remember not to include the quotation marks and change AX to the last cell with data in the column A. For most buildings, the base temperature falls in the range of 55-65 F. Notice that Units of Production (UPr) as well as Cooling Degree Days (CDD) are included in the equation, meaning that this normalization included weather data and production data. For example, a computer assembly plant can track the number of computers produced. Figure 1.6: Finding the relationship between usage and weather data. This line, the Best Fit Line, is found using statistical regression techniques available in canned utility bill tracking software and in spreadsheets.

The blue dots represent the utility bills. You need degree days to find out if your energy projects actually save energy or not. When heat is required to keep a building at a constant desired temperature, we experience heating degree days. This is done automatically by canned software, and would need to be performed by hand if other means were employed. ), In Excel 2003 and below go to "Tools" -> "Data Analysis" and select "Regression". Considering how fast temperature changes, hourly data is excellent, and using the average of high and low temperatures is quite satisfactory. You'd probably need a custom-built program in order to test many thousands of different regressions like we did.

Call us at (805) 547 2050 to schedule a no-obligation consultation or to learn more ways Abraxas Energy Consulting can help you improve the energy efficiency of your entire organization. Degree days is handy because it is a single number we can use in place of calculating energy usage each day and summing it over the period. Select an empty column and label it "Normalized Data." Obviously, every building is different. To begin, you need to open a spreadsheet and import the data into it. Lets start by defining degree days.

To weather-normalize this US aggregate electricity-consumption data we wanted US population-weighted degree days. We found a Best Fit Line through the data. Play with it and perhaps compare a month last year to a month this year. This report should not be used to make any type of energy conservation decision as there are many variables beyond the scope of this report that must be considered for such a decision. In statistics, "normalization" refers to the transformation of arbitrary data into a standard distribution, typically a normal distribution with a mean of 0 and variance of 1. But that complicates the calculations dramatically and increases the cost of finding reliable data. You (or your software) plug in the number of days from your bill and the number of Cooling Degree Days from the billing period into your Baseline Equation. Enter ="STANDARDIZE(A1:A100,B1,C1)". Building utilization is also a big factor. Click on the first cell labeled A1 and enter the value you would like normalized down that first column. Conveniently this list includes a longitude/latitude position for each city, which meant that, using the Degree Days.net API (or the desktop app for non-programmers), we could get our system to choose the most appropriate weather station to represent each city automatically. If your graph resembles Figure 1.8, you will be using Cooling Degree Days. If you're looking for additional data, data for lots of locations, or automated access to data (in large or small quantities), our products can help! (check all that apply), All Rights Reserved 2022 by Brightly Software, Inc. |, (Evaluation Year TDD Base Year TDD)/ Base Year TDD. Then, for HDD and CDD, we combined the figures for all the cities using the following simple formula for population-weighting: This gave us weekly US population-weighted HDD, and weekly US population-weighted CDD, in a wide range of base temperatures. In Figure 1.7, Non-Temperature Sensitive Consumption is roughly the same every month, about 2450 kWh per day. But first, we need to define a term called base temperature. Cooling Degree Days are a rough measure of how much a periods weather should result in a buildings cooling requirements. [6] The statistical calculations behind the R2 value, and a treatment of three other useful indicators, T-Statistic, Mean Bias Error, and CVRMSE are not treated in this chapter. Consequently, you are less likely to see degree days available, as more sophisticated analysis requires you to calculate your own degree days based upon your own buildings balance points. It will automatically test your energy data against degree days in lots of different base temperatures, to find the ones that give the best statistical fit. [8] In general, daily degree days are the difference between the buildings balance point and the average outside temperature. To calculate the US population-weighted degree days we started with a list of US cities by population from Wikipedia (which is great for this sort of data). You can perform normalization in Excel using the STANDARDIZE function. We graphed normalized Base Year utility data versus normalized weather data. So, over the 3-day period, we have accumulated 14 cooling degree days and 4 heating degree days.

This effectively removes variations in weather from the comparison. But that average might not match with what nature presents to us. Remember, the Baseline Equation represents how your customer used energy in the Base Year. Think of it as heating and cooling degree days applied to plant growth instead of buildings. The STDEV.S function will find the standard deviation of the data you entered in the column A and will be useful when normalizing the data. We make the assumption that everything in the building stays the same as it was in the base year, so if the temperature this year was identical to the base year, the energy used would also be identical.

We call this usage that is determined by the Baseline Equation, Baseline Usage. If the weather really was the cause of the higher usage, then how could you ever use utility bills to measure savings from solar energy projects? In this example, we would select the year of utility data before the installation of the solar electric system. For the purposes of saving energy, we deal with two types of degree days: heating degree days, and cooling degree days. For our analysis we were more interested in short-term trends than longer-term trends, so this is why we used a different baseline regression for each calendar year.

For comparisons in any given year we used the previous calendar year as the baseline. Click on it and hold. I hope youre getting a feel for the concept of weather normalization. [2] Cooling degree days are defined in detail later in the chapter, however a rough meaning is given here. A best-fit straight line must be used and linear regression analysis is used to create the best fit. There are many reasons the system may not have delivered the expected savings. Therefore, the degree days relationship portion of the curve doesnt start at zero, it starts at the base load energy usage and goes up from there. How to Calculate an Interquartile Mean in Excel, How to Use Excel to Find the Mean, Median & Mode Ranges, How to Generate a Random Variable With Normal Distribution in Excel. That way, you will be able to use the STANDARDIZE formula you just wrote in any cell without having to worry about changing the cell references C1 and C2. Cooling Degree Days are roughly proportional to relative building cooling requirements. There is no reference. Failing that you could just estimate the optimal base temperatures rather than determining them statistically. Imagine showing a customer these results after they have invested hundreds of thousands of dollars in your system. All Rights Reserved 2022 by Brightly Software, Inc. | Terms Of Use | Privacy Statement, Why wasn't this helpful? In the past, before energy professionals used computers in their everyday tasks, degree day analysis was simplified by assuming balance points of 65F for both heating and cooling. The red line is the best fit line. This activates the AVERAGE function, which will return the arithmetic mean for use in normalization. A Positive Number is bad. January 2000 right through to the present day. How is a solar energy contractor going to show savings from a solar electric system under these circumstances? eh [Content_Types].xml ( N0o \r Om}iAUiTwNbW.

So if hourly is good, how about 15-minute data? An avid cyclist, weightlifter and swimmer, Daniels has experienced the journey of fitness in the role of both an athlete and coach. Cooling Degree Days over a month or billing period, are merely a summation of the Cooling Degree Days of the individual days. Base years are chosen to match each individual situation. Base temperature, sometimes call balance point, is simply the outsidetemperature at which a building needs no heating or air conditioning to maintain a constant desired internal temperature. Our example is a house with a base temperature of 60 F. Abraxas Energy Consulting specializes in helping their clients find the right utility bill tracking program, setting up clients utility bill tracking databases, and providing energy analysis and mentoring. It won't do this with population-weighted data like what we used for the analysis described in this article, but it is ideal for the much-more-common case of analyzing energy data from a building using degree days from a nearby weather station.

In addition, the average temperature in Detroit was 74. the energy consumption according to the 2014 baseline). . Even with a spreadsheet, it is quite tedious. You take a bill from some billing period after the Base Year. Notice that Figure 1.7 presents two trends. Base temperature is a very important factor in any regression analysis involving degree days. The Heating Balance Point can be defined as the outdoor temperature at which the building starts to heat. This would typically be the year before you started your alternative energy program, the year before you installed a retrofit, or the year before you, the new energy contractor, were hired, or just some year in the past that you want to compare current usage to. When we normalize for weather, the same data results in Figure 1.4, and uses the equation: Savings = (How much energy we would have used this year) (This years usage), Figure 1.4: Comparison of Baseline and Actual (Post-Retrofit) data with Weather Correction. Our example may appear a bit exaggerated, but it begs the question: Could weather really have such an impact on savings numbers? In Microsoft Excel, normalization involves a few simple calculations. com) sells all of the major desktop utility bill tracking software programs, including EnergyCAP Desktop, Metrix, and Stark Essentials. Base Year bills and Cooling Degree Days are then normalized by number of days, as shown in Figure 1.6. Lets find out why. Real estate concerns, hotels and prisons normalize for occupancy. Aggregate energy-consumption data is similar, and it is important to choose appropriate heating and cooling base temperatures for its regression analysis. But if it is so easy, why write a paper on this? However, you can sum degree days, and the result remains useful, as it is proportional to the heating or cooling requirements of a building. Next, head to the cell C1 and type the following into it: =AVERAGE(A1:AX). Remember not to include the quotation marks and change AX to the number of the last cell in column A. A hotter day will result in more Cooling Degree Days, whereas a colder day may have no Cooling Degree Days. You can then compare that figure with the actual consumption over the period, and use the comparison as an indication of whether normalized energy consumption is increasing or decreasing. Normalizing by number of days (in this case, merely, dividing by number of days) removes any noise associated with different bill period lengths.

So I tell you that the bill was $75 last August. She also studied business in college. Doesnt mean much does it?

This would be difficult in Excel, but you may be able to do it in R, or with custom-programmed software. To do that, select the cell B1.

(Note that this is different to our system which labels weekly degree-day figures with the first day of the 7-day period that they cover.). Weather normalization typically only considers one variable temperature. Mr. Avina managed the development of new analytical software that employed the weather regression algorithms found in Metrix to automatically calibrate building models. But for non-programmers we also came up with a simple process to calculate population-weighted degree days using our desktop app and Excel. That subject is beyond the scope of this article, so Ill leave it at that. So we can combine the two bullet points by inserting degree days into the equation. Indeed, temperature is the most significant variable, and over the long term, we get very good results from using it and ignoring all the other variables. The R2 value represents the goodness of fit, and in energy engineering circles, an R2 > 0.75 is considered an acceptable fit.

Our example uses last year as the base year. Enter "=AVERAGE(A1:A100)" and replace "A1:A100" with the range of the data you wish to normalize. An improved approach may have been to use the previous four quarters as the baseline for comparisons in any given quarter. (To simplify the presentation, we are speaking in terms of a building, as it is less abstract. The Baseline Equation is shown at the bottom of the figure. Specialized software performs weather normalization calculations in nearly all applications today.

Once you have the Baseline Equation, you can determine if you saved any energy. However, since analysis is performed on a meter level rather than a plant level, if you have meters (or submeters) that serve just one production line, then you can normalize usage from one meter with the product produced from that production line.

You may have noticed the use of the dollar sign in the formula. There are lots of resources for degree day data on the internet.

It is the balance point where the desired inside temperature is maintained without air conditioning or heat. We used the equation: Savings = (Last years usage) (This years usage). If you wish to use utility bills to determine energy savings from your alternative energy system with any degree of accuracy, it is vital that you remove the variability of weather from your energy savings equation. In this article, we looked at the issue of comparing energy used by a building for two different periods. On the other hand, some believe that all degree days are calculated assuming the standard balance point of 65F. Scattered data points create another complication in making graphs. Each 4-quarter period from 2000 onwards e.g. The year whose data makes up this graph, last year in our example, is called the base year (no relation to the base temperature). It is recommended that a Certified Energy Manager or Engineer be consulted for assistance with calculating utility and energy use savings and making energy conservation decisions or investments. The Best Fit Line Equation represents the Best Fit Line, which in turn represents the Base Year of utility data. With the electricity data and population-weighted HDD (with a base temperature of 55F) and CDD (with a base temperature of 69F), we ran a multiple regression for each calendar year from 2000 onwards.

(Ignore the vertical red line for now. For non-US data it would be better to determine the optimal base temperatures yourself. If your graph resembles Figures 1.9, you will be using Heating Degree Days. For this year, we can go to the internet and look up historical weather to determine the cooling and heating degree days for the period in question. November 22, 2012. To find the balance point temperature of a building, graph the Usage/Day against Average Outdoor Temperature (of the billing period) as shown in Figure 1.7. Open an existing spreadsheet in Microsoft Excel containing the data you wish to normalize. Once youre there, release the mouse button. The key tools used to do thisare degree days and weather normalization. US electricity-consumption data is published weekly, so could potentially provide a valuable early indication of US GDP, which is published only quarterly. Comparison of Pre-Retrofitand Post-Retrofit data. A straight line rarely connects the points perfectly. It can, but usually not to this extreme.

Using the software still requires a solid understanding of the concepts discussed in this article. but most buildingshave a base temperature that is fairly constant when averaged over long periods of time. Figure 1.1: Expected Pre and Post-Retrofit usage for chilled water system retrofit. Base Year bills Best Fit Line = Fit Line Equation.

If nothing changes in the building, that same relationship should hold true for this year. In the Report table above we will use July as the example month. Cooling Degree Days are calculated individually for each day. School districts, colleges, and universities often normalize for the school calendar. If a factory manufactures several different products, for example, disk drives, desktop computers, and printers, it may be difficult to come up with a single variable that could be used to represent production for the entire plant (i. e. tons of product). (For energy contractors, a combination of R2 values and T-Statistics is usually enough. When doing weather normalizationwe compare how much energy we would have used this year to how much energy we actually did use this year. More and more energy professionals are coming to understand the value of normalizing utility data for production in addition to (or instead of) weather.