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Weather trending for energy performance: don't let extreme seasons rain on your efficiency parade.

How do your energy bills for this summer compare to last year? Finding higher energy usage than you expected is disappointing, especially if you were hoping to find savings from an efficiency project. A recent heat wave may have something to do with it, but without data to back up your sneaking suspicion, there's not much you can do to explain your energy anomaly.

However, the data you need is just a few clicks away. A copy of Microsoft Excel and a couple years of utility data are all you need to start normalizing your energy data, allowing you to examine your true energy usage independent of weather.

The Case for Normalization

Removing weather's effects from your energy consumption allows you to develop a more accurate picture of your facility's performance. Ganesh Ayer, adviser for energy management consultancy Energy Efficiency and Demand Management, Inc., suggests four ways to leverage weather-normalized utility data:

* Track fuel or energy use

* Verify savings from energy efficiency measures

* Participate in peak pricing or demand response programs

* Identify exceptional consumption that could indicate malfunctioning equipment

A record of consumption adjusted for weather can also help clarify that higher-than-normal usage is the fault of weather phenomena, not poor operation or malfunctioning equipment, adds Martin Bromley, founder of BizEE Software, which provides free worldwide climate data at

"When heating is a major part of your energy use, if this year is 10 to 20% colder than last year, you'd expect your heating bill to jump by 10 to 20% as well. That can totally negate any progress you've made in reducing your energy consumption," explains Bromley. "Normalization is an attempt to write the weather out of the equation so you can see whether your energy performance has improved."

You can also engineer the data to ensure you get the full story out of your utility bill. Utilities don't always send billing statements at the exact same time every month, which can throw off your calculations.

"It could just be that your utility billing period changed this month, because a lot of times utilities don't bill on the first day of every month and it's not always the same day of the week, so a couple of days off on the billing cycle can skew the results," says Josh Duncan, vice president of product management for Noesis, a provider of project financing and energy efficiency software. "It's about the health of your building--being able to track and report on the success of your projects and proactively identify when something isn't going as planned."

One school district quantified recent retrofits by gathering utility bill data from before and after the project and had Noesis verify the data, Duncan says.

"That's a great selling tool for anyone who's trying to convince management to do another project," adds Duncan. "If you're looking at case study data and it only has raw numbers--'Last year we used this many kWh and this year we used this many kWh'--you're not getting the full picture of what changed."

How to Get Started

Ready to take the plunge? Start by gathering your data. You can first measure energy use by month using the numbers on your utility bills. However, utility bills may also contain some estimates that can throw off your calculations, Bromley warns.

"Be careful not to use the estimated readings--you'd be analyzing what the utility company forecasted before they got a proper reading," says Bromley. "A lot of people read the meter themselves. Weekly is a good time scale to do that if you have the patience for it."

You'll also need to determine your building's base temperature in order to determine the number of heating or cooling degree days your building experienced. The base temperature is the temperature at which your building doesn't need conditioning and the fewest number of people complain about being too hot or too cold. It depends on the temperature you're heating or cooling the building to and the internal heat gain from all of the people and equipment inside the building. Most people start around 55-65 degrees F., but if you have good records, Bromley recommends experimenting with the estimated base temperature to obtain the most accurate figure. (See for an easy tutorial.)

"Let's say the thermostat is set to 65 degrees. Drop it down from there a few degrees to compensate for internal heat gain from computers and lighting," Bromley explains. "The entire idea of a base temperature is an approximation, but the point is that it's usually lower than 65, so bear that in mind and see how different temperatures fit. Try a low base temperature and a high one and see how they work."

Next, create a new spreadsheet with columns for the starting day of the measurement period (typically a week or a month), degree days for that period, and kWh usage (or whatever other units your records of energy consumption are provided in). Heating and cooling degree days are measured separately, so if you're tracking both, you'll need separate spreadsheets for each. Plug in the information you already know--the starting day of each measurement period and the corresponding kWh usage--and determine the heating or cooling degree days as appropriate.

Degree days--the number of degrees by which the outside temperature varies from your base temperature and requires heating or cooling--can be determined with a few simple formulas if you're inclined, as can your baseload, the portion of your consumption that doesn't rely on the weather (see page 71 for how to calculate this). However, if you're reasonably confident in your estimated base temperature, has a free tool allowing you to search for nearby weather stations (which will have roughly the same climate as your building) and retrieve up to three years of weather data.

Download heating or cooling degree days as needed in the same measurement periods you're using in the first column--monthly, weekly, or daily--and insert these into the middle column. If your figures aren't neatly organized like this, just add daily degree days for a week or a month together.

Create a new column for kWh per degree days. For each week's worth of data, insert a formula dividing energy consumption by degree days.

"If you're using utility bills, be aware that some utilities can have different lengths of periods. In those cases, it's very important to correct for the length of each period," explains Bromley. "Using monthly energy data involves a little fudging because February is 28 days and other months are 30 or 31, so that introduces a bit of inconsistency. What you should do then is work out the length of each period in days, and instead of correlating degree days for each period against kWh for each period, correlate degree days per day against kWh per day instead."

Once you've determined how much extra energy your building consumes for each degree day, start comparing seasons and years against each other. For best results, look at full years or seasons next to each other instead of trying to draw conclusions between two months in the same year, for example.

"Yearly comparisons are much more likely to be reliable," says Bromley. "Normalize figures for two years that encompass the full heating or cooling season. This will write out many of the problems you'll get when you try to compare energy consumption in January vs. April."

Set Yourself Up for Success

The initial setup may require some time investment, especially when entering a year or more worth of utility data. To get the most benefit out of your work, keep these four tips in mind:

Know your end goal. What are your reasons for analyzing your own weather and energy data? "Ask yourself what you're really looking to do," recommends Bromley. "It makes sense if heating and/or cooling are a significant proportion of your metered energy consumption. If you have a building with a lot of energy used by things that aren't affected by the weather, you can still do the analysis, but it might not be very meaningful if the weather-dependent portion is dwarfed by everything else." Buildings that meter heating and cooling separately are ideal, he adds.

Hit the books. "The best bet is to read the relevant standards to learn about the math behind this," says Duncan. "If you're an engineer who wants to get under the hood, I recommend ASHRAE Guideline 14 to really understand the regression models that go into producing these things. This is an important tool for you to use to improve the efficacy of your energy programs and build on those efforts."

Beware of unusual usage patterns. Think about periods when your occupancy might differ from the norm, such as holidays or large events. These can skew your results, Bromley says: "There's no real prescription for how to deal with this kind of problem, so you evaluate it on a case-by-case basis. You can do things like ignore holidays from your analysis because some holidays are a complete anomaly--if the whole building is closed, that will affect your correlation in strange ways, so write that period out."

Leverage your findings. Ayer recommends that FMs who have gained a good understanding of their data look for opportunities to use it for more than monitoring. "Can you participate in a peak pricing or demand response program? Can you use the information to obtain lower procurement costs? Can you add on combined heat and power or microgeneration within your facility?" Ayer asks. "With the big data that's available, the sky is the limit. Think outside the box."


1) To gain a full understanding of your metering data, you'll need to figure out your baseload, the portion of your consumption that doesn't depend on the weather. First, add in the number of days in each month and determine how many degree days and kWh are actually used per day--this compensates for how months vary in length.

MONTH            HEATING                         DEGREE    KWH PER
STARTING          DEGREE             DAYS IN    DAYS PER     DAY
                   DAYS      KWH      MONTH       DAY

10/1/2012          163       593        31        5.26      19.13
11/1/2012          228       676        30        7.60      22.53
12/1/2012          343       1335       31       11.06      43.06
1/1/2013           373       1149       31       12.03      37.06
2/1/2013           301       1127       28       10.75      40.25
3/1/2013           238       892        31        7.68      28.77
4/1/2013           137       538        30        4.57      17.93
5/1/2013            84       289        31        2.71       9.32
6/1/2013            38       172        30        1.27       5.73
7/1/2013            15       131        31        0.48       4.23
8/1/2013            14       134        31        0.45       4.32
9/1/2013            34       134        30        1.13       4.47
TOTAL              1968      7170
HEATING            1783      6310      212

2) Next, use Excel's trending capabilities to add a trendline. Right-click one of the data points and select "Add Trendline," then select "Linear" for the type. Check the boxes for "Display equation on chart" and "Display R-squared value on chart." It should look something like the graph at left.

3) The full equation at the top (the "y") represents kWh consumption. The "x" corresponds to degree days, and the number multiplying it (here, 3.33) is the gradient--it describes the trend line above and allows you to predict future usage by substituting a known number of degree days for the "x." This way, you can later compare predicted energy usage with what you actually used to see if energy performance is getting better or worse.

The number after the plus sign tells you the baseload. In this case, it's 1.6813 kWh. The R2 number represents the strength of the correlation. Anything over 0.75 is acceptable, but the closer you can get to 1, the more accurate your results will be. This one is 0.96, so the correlation is very strong.

Now assume the FM in this scenario did the same monitoring the next year, from October 2013 to April 2014, and ended up with the results at above right.

Total metered kWh      7306
Total degree days      2143
Total days             212
kWh per day            34.46226
Degree days per day    10.10849

Using the formula from the chart, we end up with this:

Expected consumption per day
(following baseline performance)       35.37623
Expected total consumption
(following baseline performance)       7499.762
Actual consumption                     7306
Actual consumption as percentage of
baseline consumption for period        97.41643

As you can see from the last number, the performance improved slightly this period consumed less than the baseline for the previous year.

IN THIS HYPOTHETICAL SCENARIO, the FM in the previous example made some changes to the heating system after spring 2013 that resulted in increased efficiency. However, when the bill arrived, it appeared that the heating system consumed the same number of kWh as it did the previous year despite the upgrades. Why?

The answer lies in the data--graphing kWh alone (above right) shows the two years consumed the same amount of energy, but adding in degree days (below right) shows the post-upgrade heating system consumed significantly less energy for each heating degree day present. The 2013-14 season had 15% more heating degree days each month, so without the upgrades, the heating system's energy usage would likely have exceeded that of the previous year.


Excel's capabilities are expansive, but in some cases, you might prefer to turn to a software package that automates some or all of the analysis. Here are three occasions where specialized software might fit your situation better.

1) You need to farm out monitoring responsibilities to multiple people at different locations.

"With a Software as a Service (SAAS) model, your internal energy managers and FMs at each facility can all access the same software online to enter data," explains Josh Duncan, vice president of project management for Noesis. "You can also integrate with utilities to automate bill data entry, which can be a big advantage."

2) You're not comfortable determining heating and cooling degree days for a system where heating and cooling aren't metered separately.

Buildings that utilize both heating and cooling may be a little more difficult to analyze using Excel, though it's certainly doable. If you need an automated solution to tackle this for you, however, some software packages can perform multivariate linear regression analysis using both heating and cooling factors at the same time, explains Martin Bromley, founder of BizEE Software.

3) You're not sure which weather station to use for degree day data or can't find the time to obtain weather data on a regular basis.

With over 6,000 weather stations worldwide, you may have questions about which one most closely fits your facility's conditions. Specialized software integrates with those weather stations and pulls daily readings automatically, says Duncan.

Janelle Penny is senior editor of BUILDINGS.



         DEGREE              DEGREE
         DAYS                DAY

OCT      163        593      3.64
NOV      228        676      2.96
DEC      343        1335     3.89
JAN      373        1149     3.08
FEB      301        1127     3.74
MAR      238        892      3.75
APR      137        538      3.93


         DEGREE              DEGREE
         DAYS                DAY

OCT      187        593      3.17
NOV      262        676      2.58
DEC      395        1335     3.38
JAN      429        1149     2.68
FEB      346        1127     3.26
MAR      274        892      3.26
APR      158        538      3.41
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Comment:Weather trending for energy performance: don't let extreme seasons rain on your efficiency parade.
Author:Penny, Janelle
Geographic Code:4EUUK
Date:Sep 1, 2014
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