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 degreedays.net.
“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 degreedays.net/regression-analysis 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, degreedays.net 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.”