Purpose of this article
This article helps you understand the Energy analytics "View" dropdown in Scaler—what each view represents, how it affects displayed data, and which view to use for different analysis goals.
Analytics Portal → Portfolio → Analytics → Energy
What are Energy analytics views?
Energy analytics views control how energy data is transformed and displayed in Scaler's Analytics Portal.
All views use the same underlying meter data, but apply different logic to:
- Adjust for incomplete time periods
- Normalize for operational factors
- Estimate missing data
- Segment consumption by responsibility
You can switch between views using the View dropdown.
Available views
Default (all reported data)
What it shows
- Energy metrics based on reported meter data only
- Includes values marked as
EstimationunderMonitoring method
- Excludes meters marked as
Include in calculations=false
When to use it
- Reviewing raw, as-reported consumption
- Validating source data
- Identifying gaps or inconsistencies in meter coverage
Note
This view does not adjust for partial-year data, occupancy, or weather.
Normalized views
Normalized views adjust energy data to improve like-for-like comparability across assets and time periods.
Available normalization views include:
- Time-based normalization
- Occupancy-based normalization
- Time + occupancy-based normalization
- Weather-based normalization
What normalization does
- Adjusts reported consumption to account for structural differences
- Enables fair year-over-year and portfolio-level comparisons
- Aligns with GRESB outlier and comparability methodologies (Time + occupancy-based normalization)
When to use normalized views
- Portfolio benchmarking
- Year-over-year performance tracking
- Comparing assets with different occupancy levels or data coverage
Learn more
See Normalization methodologies in Scaler for detailed definitions, formulas, and reporting alignment.
Estimated view (Scaler algorithm)
The Estimated (Scaler algorithm) view fills gaps where meter data is missing or incomplete.
What it shows
- Estimated energy values for missing periods
- Actual and estimated values displayed together
- Confidence scores available in exports and meter-level downloads
How estimation is used
- Detects incomplete months at the meter level
- Uses recent historical consumption to estimate missing values
- Applies weighted averages with decay logic to prioritize recent data
When to use it
- Portfolio analysis requiring continuous datasets
- Benchmarking and scoring that require uninterrupted data
Important
Estimated values are modelled data, not measured consumption.
Learn more
See How Scaler Estimates Missing Energy Meter Data for full methodology, limitations, and confidence scoring.
Landlord–tenant split view
The Landlord–Tenant Split view separates energy data based on meter responsibility.
What it shows
- Landlord-controlled consumption
- Tenant-controlled consumption
- Separate visualizations for:
- Energy use intensity (EUI)
- Total energy consumption
- Data coverage
How the split is determined
Based on meters' Area type:
- Landlord-controlled
- Tenant-controlled
When to use it
- Understanding responsibility for energy consumption
- Supporting tenant engagement strategies
- Prioritizing efficiency measures by control type
Note
This view depends on accurate meter classification in the Data Collection Portal.
Choosing the right view
Goal | Recommended view |
Review raw data | Default |
Compare assets fairly | Normalized views |
Handle missing data | Estimated (Scaler algorithm) |
Understand responsibility | Landlord–Tenant split |