6 Ways You Can Normalize Your Energy Data

Data has a nasty way of representing the wrong idea when inaccurately reported. Now, you can fix this problem once and for all.

By: Tony Rovano

When comparing facility data, stacking data sets side by side won’t offer an accurate picture of your facility. Comparing energy use for a facility housing 250 people to one housing 50 is like comparing the caloric intake of a toddler to that of a linebacker. Of course the numbers won’t add up!

You can use normalization to gather accurate data. There are a few types of normalization you should explore.

Area or Volume:

When comparing energy use in buildings of different sizes, factor the size of the buildings. This is helpful when determining usage and cost by square foot. Building size has a major impact on utility consumption. Normalization provides a way to know if the consumption is due to building size or inefficiencies.


Cooling a room of 300 people costs far more than air conditioning a room for four. Added bodies drive the need for more cooling. Plus, if people are in and out of doors, more air will escape, taking a greater toll on your HVAC system. Unless you’ve normalized the data to account for occupants, you won’t see accurate results.

Operational status:

Naturally, a building will consume less energy when it’s not operational. Normalization can take use into account – noting times when class is in session or out of session, or when building is not occupied.


Productivity equals energy consumption. In times of higher productivity, you’ll require more energy than periods with lower productivity. Normalization allows you to see how production drives consumption.


Climate normalization allows for the comparison of climate from one region to another. After all, you can’t expect a facility in Portland to consume the same amount of electricity in the summer as one in Phoenix. Climate normalization will allow you to compare the two facilities as if they operated in the same climate.


Comparing property to itself under different weather conditions. Year over year analysis demonstrates changes in consumption based on weather changes. Weather normalization allows you to show the effectiveness of energy saving measures by offering apples-to-apples comparisons.

Normalization allows you to accurately report the expenses (and savings!) of your facility. Its data you can’t afford to ignore.

To learn more about data normalization, check out our other blog posts on the topic!

To learn more about our suite of energy management products, visit our Energy Management Software solutions page.

Photo Credit: Sebastiaan ter Burg – Creative Commons