Interval Data Granularity: 5-min vs 15-min

Modern commercial meters do not just tally a single number at the end of the month. They record usage in a stream of fixed time buckets, producing a detailed picture of how a facility consumes electricity hour by hour and minute by minute. The length of those buckets, known as interval granularity, shapes what you can see in that picture and, in some cases, how demand-based charges are calculated. Understanding the difference between finer and coarser intervals helps a business read its own load and judge where finer data is worth having.

What Interval Data Is

An interval meter records energy usage in fixed time buckets, such as 5, 15, 30, or 60 minutes. Instead of one monthly total, you get a sequence of readings, each representing the energy consumed during one window. Laid end to end, these readings form a load profile: a curve that rises when equipment turns on, holds through steady operation, and falls when the facility winds down. This profile is the raw material for almost every kind of demand analysis.

The load profile reveals things a monthly total cannot. It shows when your facility peaks, how sharp those peaks are, whether usage is steady or spiky, and how consumption lines up with your operating schedule. For any business trying to understand or manage demand-related charges, the interval profile is the document that matters, because demand is fundamentally about timing rather than total volume. Our overview of commercial demand charges explains why that timing carries cost.

What Granularity Changes

Granularity is simply the length of each interval bucket. A 5-minute interval divides an hour into twelve readings; a 15-minute interval divides it into four; a 60-minute interval gives one per hour. Finer granularity produces more data points and captures more detail. Coarser granularity produces fewer points and smooths the picture out.

The practical consequence is what each level of detail can and cannot show. A brief, sharp load spike, say a large motor drawing heavily for a couple of minutes during startup, stands out clearly in 5-minute data. In 15-minute data, that same spike is averaged together with the calmer minutes around it, so it appears smaller and less distinct. In 60-minute data, it may nearly disappear into the hourly average. None of these views is wrong; they are simply different resolutions of the same underlying behavior.

This is why granularity is not just an academic detail. If your facility's costs are driven by short, intense bursts of demand, the resolution you look at determines whether you can even see the events that matter. If your load is smooth and slow-moving, the resolution matters far less, because there are no sharp features for finer data to reveal.

Granularity and Billing Determinants

The connection between interval granularity and your bill runs through the demand billing determinant. Demand charges are based on a measured peak, but that peak is always defined against a specific averaging interval written into the rate schedule. A tariff might set demand as the highest average usage over any 15-minute or 30-minute window in the billing period. The interval named in the tariff is the one that governs the charge.

This distinction matters because the finest data your meter can produce is not automatically the interval your bill uses. A meter might record 5-minute data while the tariff calculates demand on a longer averaging window. A short spike that looks dramatic in 5-minute data may be softened when averaged over the tariff's window, or it may still drive the determinant, depending on how brief and how large it is. The only way to know how your peaks translate into billed demand is to read the averaging interval your specific rate schedule defines.

Because these mechanics sit on the delivery and demand side of the bill, they are set by the utility tariff filed with the Illinois Commerce Commission, not by a competitive supplier. A supplier affects the supply portion; the demand determinant and its averaging interval are part of regulated delivery service. For a fuller sense of how demand and other components divide up, our breakdown of capacity, energy, and transmission as three buckets provides the wider frame, and our note on reading the ComEd delivery and supply split shows where these charges land on the statement.

Peak Identification and Demand Analysis

For demand analysis, interval data is where the real work happens. Identifying a facility's peak means finding the moment of highest demand and understanding what caused it. Finer granularity sharpens that search. With 5-minute data, you can often pinpoint the specific equipment cycle or operational event behind a peak, because the spike is isolated in its own bucket rather than blended into a longer average.

Coarser data still identifies peaks, but with less precision about their cause. If your peak is a broad, sustained period of high usage, a 15-minute view captures it well. If your peak is a momentary surge, a 15-minute average may understate it and obscure exactly when it happened. The practical question for demand management is whether the events driving your peak are long or short. Long, steady peaks are visible at almost any resolution; short, sharp peaks reward finer data.

This has direct bearing on load-shifting decisions. If analysis shows that a peak comes from a brief, controllable event, such as several machines starting simultaneously, a business can consider staggering that startup. But you can only make that call if the data resolution lets you see the event in the first place. This is where granularity converts from a technical property into an operational tool. Businesses working through this alongside broader cost planning may find our overview of commercial electricity useful for connecting the pieces.

When Finer Granularity Is Worth It

Not every facility needs the finest available data. The value of granularity scales with two things: how spiky your load is, and how much control you have over the equipment causing the spikes. A facility with sharp, short peaks from cycling machinery, and the ability to reschedule or stagger that machinery, gets real analytical value from 5-minute data because it can act on what the data reveals. A facility with smooth, continuous load, or one whose peaks are unavoidable, gains little from finer resolution because there is nothing hidden for it to expose and little to change.

The sensible approach is to start by looking at whatever interval data your utility makes available, understand the shape of your load, and check the averaging interval your tariff uses for demand. Interval data is generally accessible from the utility, often through an online account, with the available granularity depending on the meter and the utility's systems. If your profile turns out to be spiky and your operations flexible, finer data becomes a genuine asset. If it is smooth and fixed, coarser data tells you nearly everything you need. The right granularity, in other words, is the one that matches your load and your ability to act on what it shows.

Sources

• Illinois Commerce Commission
• ComEd
• Ameren Illinois

This article is general educational information and does not promise any specific savings or outcome for your business.