Purpose of this article
This article explains the normalization methodologies used in Scaler, including what each normalization adjusts for, when normalization is applied, and how normalized values should be interpreted in analytics and reporting.
What is normalization in Scaler?
Normalization in Scaler adjusts reported (energy) consumption to a common basis so that performance can be compared fairly across assets and time periods.
Normalization:
- Does not create new consumption data
- Scales existing, reported values
- Accounts for structural differences such as time coverage, occupancy, and weather
Normalization is primarily used in:
- Portfolio and year-over-year analysis
- Benchmarking and scoring
- GRESB-aligned analytics
Important: Normalization adjusts reported data. It does not estimate missing consumption.
Types of normalization in Scaler
Scaler uses two categories of normalization:
- Temporal alignment - Adjusts consumption to match standard time periods
- All resources: Energy, water, waste
- Structural normalization - Adjusts for operational differences (occupancy, weather)
- Currently only energy
Temporal alignment
Purpose: Converts consumption from non-standard time periods into standard calendar periods (months, quarters, years).
How it works:
When meter readings don't align with calendar boundaries (e.g., billing periods from January 15 to February 15), Scaler:
- Calculates average daily consumption over the interval
- Allocates this to each month proportionally based on number of days
Example:
A meter reading covers December 15, 2024 to January 15, 2025 (32 days total).
DAILY_AVERAGE = REPORTED_CONSUMPTION / 32
DECEMBER_CONSUMPTION = DAILY_AVERAGE × 17 (days in December)
JANUARY_CONSUMPTION = DAILY_AVERAGE × 15 (days in January)
Where temporal alignment is applied:
- Analytics Portal graphs (when viewing monthly data or annual reporting years)
- Generated reports (GRESB, GRI, etc.)
- Data exports (Monthly Meter Consumption export)
Structural normalizations
Structural normalizations adjust consumption to account for operational differences between assets or time periods.
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 EUI:
Normalized EUI = Reported Consumption / % of Year with Data / Active Area
- For Total Consumption:
Normalized Value = Reported Consumption / % of Year with Data
Alignment: GRESB outlier methodology (resource-level coverage).
Occupancy-based normalization
Purpose: Adjusts consumption to reflect 100% occupancy.
Where used: Portfolio-level Energy Use Intensity and Total Consumption graphs.
Method:
- For EUI:
Normalized EUI = Reported Consumption / Average Occupancy Rate / Active Area
- For Total Consumption:
Normalized Value = Reported Consumption / Average Occupancy Rate
Alignment: GRESB outlier methodology.
Time + occupancy-based normalization
Purpose: Adjusts for both time and occupancy.
Where used: Portfolio-level energy graphs.
Method:
- For EUI:
Reported Consumption / % of Year with Data / Average Occupancy Rate / Active Area
- For Total Consumption:
Reported Consumption / % of Year with Data / Average Occupancy Rate
Alignment: GRESB outlier methodology.
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.
Enabling weather-based normalization
- Go Data Collection Portal → Portfolio → Settings → Normalization.
- Toggle to
Enable weather normalization
- Create necessary Weather stations
- Populate HDD and CDD values
- Assign an
Allocated weather stationto each asset in Asset Details.
Where normalization is applied
Context | Temporal alignment | Occupancy | Time | Weather |
Analytics Portal - Default view | Yes* | No | No | No |
Analytics Portal - Normalized views | Yes* | ✓ | ✓ | ✓ |
Analytics Portal - Estimated view | Yes* | No | No | No |
Reports (GRESB, GRI, etc.) | Yes | Depends on report | Depends on report | No |
Data exports | Yes | No | No | No |
*Analytics Portal applies temporal alignment when viewing monthly graphs or when meter reading periods don't align with the selected reporting year.
Important notes:
- Default/Normalized/Estimated views = dropdown in Analytics Portal graphs only
- Structural normalizations (occupancy/time/weather) are only available through the Analytics Portal View dropdown, not in exports or raw data downloads
Normalization in reports and exports
Normalization is also applied in generated reports and downloadable exports to ensure alignment with the timeframe of the report or reporting framework.
Monthly Meter Consumption export
File: Found in Data Collection Portal → Portfolio → 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 normalization differs from estimation
Normalization | Estimation |
Adjusts reported consumption | Fills missing or unreported consumption |
Scales existing values | Creates modelled values where data is missing |
Uses structural factors (time, occupancy, weather) | Uses historical consumption patterns |
Focused on comparability | Focused on data continuity |
Results in normalized values | Results in estimated values |
See How Scaler estimates missing energy meter data for details on estimation logic and limitations.
Why normalization matters
Normalization enables apples-to-apples comparison across:
- Assets with partial-year data
- Assets with variable occupancy
- Assets in different climates
- Periods impacted by abnormal weather
By controlling for these variables, Scaler provides clearer insights into operational efficiency and performance trends.