Putting Green Button interval data to work: demand management and cost allocation

The monthly bill tells you what you used. It does not tell you when, and when is where most of the cost and most of the opportunity live. Interval data, the meter's record of consumption in short, regular steps, is the when. Green Button is what makes that record portable: a standard format you can pull from a growing number of utilities instead of a proprietary export you have to reverse-engineer for each provider.

Two jobs benefit immediately from interval data: managing demand charges, and allocating shared energy costs fairly. Both are hard to do from a monthly total and become straightforward once you have the shape of the load rather than just its sum.

What Green Button interval data actually is

Green Button comes in two forms. Download My Data lets a customer export usage as standard XML, and Connect My Data lets an authorized application pull it automatically, at 5-minute, hourly, daily or monthly granularity. It covers electricity, gas and water, and it is built on the North American Energy Standards Board's Energy Services Provider Interface, first released in 2011. Utilities in nearly all 50 US states, two Canadian provinces and the Republic of Korea offer some form of it. The value for an analyst is that the format is the same regardless of which utility produced it, so one process handles the whole portfolio.

Use one: managing demand charges

For most commercial and industrial accounts, the demand charge is the line most worth watching. It is billed on your highest short-interval power draw in the period, typically a 15-minute average, not on your total energy. At common rates of $10 to $25 per kW per month it can be 30 to 50 percent of a multi-site electric bill, and by some analyses half or more. A monthly bill shows you the demand charge but not the moment that caused it. Interval data shows you the exact window.

Find the intervals that set the peak

With interval data you can see the single 15-minute window that set the month's demand, and reason about what was running during it. That turns a vague instruction to reduce peak demand into a specific action: stagger equipment that started together, shift a process off the coincident window, or pre-cool a space before the peak arrives. You cannot make that call from a monthly total, because the total has already averaged the peak away.

See the ratchet coming

Many tariffs add a ratchet that sets your minimum billable demand at 70 to 80 percent of your highest peak over the prior 11 to 12 months. A single spike can raise your floor for a year. Interval data lets you catch that spike in the period it happens, while there is still time to explain or correct it, rather than discovering it months later as an unexplained raised floor on every subsequent bill.

Use two: allocating shared energy costs

In multi-tenant buildings, campuses and shared facilities, one master meter often serves many occupants. Splitting that bill by floor area is simple and usually wrong: a tenant running servers overnight uses far more than one running a nine-to-five office of the same size. Interval data lets you allocate by actual load rather than by a proxy that ignores how each occupant actually consumes.

With interval data per tenant or submeter, you can allocate energy by real consumption and, where the tariff carries a demand charge, allocate that charge to whoever was actually drawing power during the peak window. That is the difference between a chargeback tenants dispute and one they accept, because it is defensible down to the interval.

What makes interval data usable

Interval data is only useful once it is clean, and three things trip teams up. Alignment: interval timestamps have to line up with billing periods and with each other across meters, or a peak lands in the wrong month. Units: a 15-minute energy reading in kilowatt-hours is not the same as a 15-minute average power in kilowatts, and mixing the two corrupts any demand analysis. Gaps: meters drop readings, and a silent gap distorts both a peak and an allocation. Green Button gives you a consistent container, but the readings inside still have to be validated before you build a demand strategy or a chargeback on them.

The monthly bill will keep arriving, and it will keep hiding the two things that cost you most: when your peak happened, and who caused it. Interval data answers both, and Green Button makes it portable enough to use across a whole portfolio. The work that remains is making the readings clean enough to trust.