Overview of Normalization Types

Default View (Unnormalized Data)

• Definition: Metrics shown based on all consumption data entered at the meter level.

• Includes: Data marked as “Estimation” under Monitoring method.

• Excludes: Meters marked as Excluded from calculations in the Data Collection Portal.

• Note: Data is not normalized for time, area, occupancy, or weather.

1. Time-Based Normalization

• Purpose: Extrapolates consumption to represent a full calendar year.

• Where used: Portfolio-level Energy Use Intensity and Total Consumption graphs.

• Method:
• For Total Consumption:
• Normalized Value = Reported Consumption / % of Year with Data
• For EUI:
• Normalized EUI = Reported Consumption / % of Year with Data / Active Area

• Alignment: GRESB outlier methodology (resource-level coverage).

2. Occupancy-Based Normalization

• Purpose: Adjusts consumption to reflect 100% occupancy.

• Where used: Portfolio-level Energy Use Intensity and Total Consumption graphs.

• Method:
• For Total Consumption:
• Normalized Value = Reported Consumption / Average Occupancy Rate
• For EUI:
• Normalized EUI = Reported Consumption / Average Occupancy Rate / Active Area

• Alignment: GRESB outlier methodology.

3. Time + Occupancy-Based Normalization

• Purpose: Adjusts for both time and occupancy.

• Where used: Portfolio-level energy graphs.

• Method:
• For Total Consumption:
• Reported Consumption / % of Year with Data / Average Occupancy Rate
• For EUI:
• Reported Consumption / % of Year with Data / Average Occupancy Rate / Active Area

• Alignment: GRESB outlier methodology.

4. Weather-Based Normalization

• Purpose: Adjusts energy use to account for weather anomalies.

• Where used:
• Portfolio-level energy graphs (View > Weather-based normalization)
• When Weather Normalization is enabled in Portfolio Settings

• Method: Uses Heating Degree Days (HDD) and Cooling Degree Days (CDD) to normalize usage against long-term averages of the assigned weather station.

• Application: Applied at the asset level when allocated weather station is set under Asset Details.

• Use case: Helps isolate the impact of operational efficiency measures by filtering out weather-driven variability.

Normalization in Reports & Exports

Examples:

Monthly Meter Consumption Export

• File: Found in Data Collection Portal > Asset > Meter List > Download

• Column: actual_monthly_consumption

• Method:
• When consumption spans multiple months (e.g., quarterly or annual meter readings), Scaler:
• Calculates average daily consumption over the interval.
• Allocates this to each month proportionally based on number of days.

Report Generation Normalization

• Scenario: You’ve entered annual consumption but generate a quarterly report.

• Scaler’s Approach:
• Scaler uses the daily average from the interval and applies it proportionally to each sub-period in the report (e.g., quarters, months).

Ownership-Adjusted Normalization

• Use case: An asset’s data overlaps with the reporting year but is only owned for part of it.

• Scaler’s Approach:
• Consumption values are prorated to reflect only the days during the ownership window.
• This ensures only in-scope data is included for frameworks like GRESB.
• For like-for-like comparisons, clients may override ownership settings to retain values.

How to Enable Normalizations

Weather-Based Normalization Setup

1. Enable in Data Collection Portal > Portfolio Settings.

2. Assign weather stations to each asset in Asset Details.

3. Populate average HDD and CDD values.

Viewing Normalized Data

• Go to Analytics Portal > Portfolio View.

• On energy graphs (EUI or Total Consumption), select normalization method from the View dropdown.

• A green "Normalized Data" badge will indicate that a normalization method is active.

Why Normalization Matters

• Assets with partial-year data

• Assets with variable occupancy

• Assets in different climates

• Periods impacted by abnormal weather