Purpose of this article
This article explains how Scaler estimates missing energy meter data using a transparent, weighted methodology, and how estimated values are used in analytics, exports, and reporting.
Note: In Scaler, "estimated" can refer to two different things. This article covers Scaler-generated estimates — values automatically calculated to fill gaps in your meter data. If you're looking for information about how your chosen monitoring method affects data classification and scoring, see Data reliability methodology.
What is estimation in Scaler?
Estimation fills missing or incomplete portions of energy meter data to create a continuous and consistent dataset.
Estimation is applied when:
- A meter reading covers only part of a month
- One or more months of consumption data are missing
- Data is delayed or partially reported
Estimated values are modelled, not measured, and are clearly identified in analytics and exports.
Estimation is different from normalization. Estimation fills data gaps; normalization adjusts data for comparability.
Where estimation is used
Analytics Portal
Location: Analytics Portal → Portfolio → Performance → Energy
How to view estimates:
- Select View → Estimated (Scaler algorithm)
- Estimated values appear alongside actual data
- Asset-level charts show estimated portions as stacked bars
Data Collection Portal exports
Location: Data Collection Portal → Portfolio → Meter List → Download
Export column: estimated_linear_extrapolation_consumption
Purpose: Complements actual_monthly_consumption to provide a complete time series
Estimated values are included to support continuous analytics, benchmarking and scoring, and external reporting workflows that expect uninterrupted time series.
Estimated data in framework reports
How estimated data flows into ESG framework reports depends on its source — whether it comes from the monitoring method selected by the client, or from Scaler's automated gap-filling model.
Monitoring method-based data
When a client selects a monitoring method such as postal code average or manual estimate, that data currently passes through to framework reports including GRESB without additional flagging. For details on how monitoring methods are classified and how they map to INREV SDDS specifically, see Data reliability methodology.
GRESB estimation rules
GRESB allows estimated data for gap-filling purposes, within defined limits. Scaler is currently exploring support for automated GRESB-compliant estimation, which would allow up to 20% estimated consumption to be included in GRESB exports in line with GRESB guidelines. We will share updates as this develops.
Scaler-generated estimates
Scaler's automated estimation model (described in this article) currently appears in analytics and data exports only — it is not included in any framework report outputs.
How the estimation model works
Scaler uses a linear, history-weighted estimation model based on each meter's actual past consumption.
The model follows these steps:
- Detects missing periods: Identifies incomplete months at the meter level, even if only a few days are missing.
- References recent history: Looks back up to 6 months of valid historical consumption for the same meter.
- Normalizes to daily averages: Converts historical monthly values into average daily consumption.
- Applies weighted logic: More recent months are weighted more heavily using a decay function.
- Estimates the gap: Missing days or months are filled using the weighted daily average.
- Assigns a confidence score: Each estimate is scored based on data availability and proximity to actual readings.
Confidence scoring
Each estimated value includes a confidence score to help assess reliability.
The confidence score considers:
- Amount of historical data available: More recent, complete history results in higher confidence.
- Distance from known data: Estimates closer to actual readings have higher confidence than those further away.
Note: Confidence scores are guidance indicators, not guarantees of accuracy.
Key benefits of Scaler’s estimation approach
- Avoids flat or arbitrary assumptions
- Uses real, meter-specific historical data
- Supports like-for-like scoring and benchmarking
- Improves portfolio comparability
- Aligns with expectations of external reporting frameworks
Limitations to consider
- Does not adjust for seasonality
- Assumes recent consumption patterns remain relevant
- Uses a fixed 6-month lookback window
- Estimated values should not replace measured data when actual readings are available
- Occupancy rates (
Annual vacancy rate) are not directly factored into estimation calculations. However, occupancy is implicitly reflected in the consumption data itself. Since Scaler estimates missing months using the past six months of actual consumption (with greater weight on recent months), any occupancy-related usage patterns are already captured in the historical data that drives the estimates.
How estimation differs from normalization
Estimation | Normalization |
Fills missing or unreported consumption | Adjusts reported consumption to a common basis |
Creates modelled values where data is missing | Scales existing values without inventing new usage |
Uses historical patterns to estimate gaps | Uses structural factors (time, occupancy, weather) |
Focused on data continuity | Focused on comparability |
Answers: “What likely happened when data is missing?” | Answers: “How would this data compare under equal conditions?” |
Note: Time-based normalization adjusts reported consumption to a full-year equivalent but does not estimate unreported usage; estimation is only applied when data is missing at the meter level.
See Normalization methodologies in Scaler for details on normalization logic.