In Measurement and Verification (M&V), accurately tracking energy savings requires more than just looking at raw data. Adjustments must be made to the baseline or reporting period to account for normal or random variations in energy consumption driven by independent variables such as weather, production output, or facility occupancy.

To keep your M&V models accurate, there are two types of adjustments: routine and non-routine.

What is a Routine Adjustment?

A routine adjustment is a regular, expected change in an independent variable. Because these changes are predictable, they are predefined when creating your initial M&V plan.

Examples of Routine Adjustments

• A normal, seasonal fluctuation in temperature between winter and summer.
• A school facility where occupancy predictably decreases during the summer break.

Because these factors are expected to occur, your regression model naturally accounts for them without requiring you to intervene manually or recalculate your baseline.

What is a Non-Routine Adjustment (NRA)?

A non-routine adjustment (NRA) is an unexpected or unpredictable change to an independent variable. These are events that were not pre-defined in the M&V plan.

When an unpredictable change occurs, it must be carefully logged to highlight the specific shift in energy consumption that resulted directly from the non-routine event.

Common Triggers for Non-Routine Adjustments

Examples of non-routine events that require adjustments include:

• An unseasonably extreme warm day occurring in the middle of a winter month.
• A manufacturing facility adding an unexpected third shift to their schedule, significantly increasing building occupancy and operating hours.
• A change in the building’s primary use type, or an expansion where another building is added to the facility footprint.
• Sudden production schedule changes due to an unusually high volume of orders.
• A new ECM is installed that has reduced energy consumption. 

How to Handle Unexpected Variables in Your M&V Plan

Essentially, the rule of thumb is this: if the independent variable was not expected to change, then any resulting shifts in energy use must be logged as a non-routine adjustment. Once logged, the baseline or reporting period (depending on when the non-routine event occurred) should be recalculated in full to ensure your energy savings data remains accurate and verifiable.

How to Calculate the Change in Energy Consumption from a Non-Routine Event.

There are two IPMVP-approved approaches to handling non-routine adjustments.

• IPMVP Option 1= data omission
• IPMVP Option 3 = additional indicator variable 

Data Omission:

This approach can be used in situations where no consumption is reported, or there is an error in the data (e.g., an extended power outage, a utility meter connectivity issue). In this approach, the data is simply excluded from the calculation with no change to the adjusted baseline. 

Indicator Variable:

An indicator variable, or change variable, allows you to use an additional regressor to calculate the adjusted baseline. The indicator is 1 when the event occurred and 0 when it did not. 

For example, let’s say your building was only partially occupied for two weeks. This partial occupancy does not match the baseline occupancy, so you want to adjust the model to account for the drop in energy consumption. When the regression model was initially built, your energy savings calculation was:

energy = 1,500 kWh + 6(CDDs)

This means your baseload energy consumption is 1,500 kWh and increases by 6 kWh for each additional cooling degree day.

When we include an indicator variable (1=partially occupied and 0= full occupancy), the calculation changes to:

energy = 1,500 kWh + 6(CDDs) – 200 kWh (indicator)

Therefore, when the indicator variable equals 1, kWh consumption is reduced by 200 kWh per day while the building is partially occupied. When the indicator = 0, the 200 kWh is multiplied by 0, effectively reverting to the initial equation.