Purpose
This article explains how Scaler evaluates data reliability: how closely reported consumption represents actual energy, water, or waste use over time.
Scaler assesses data reliability based on the Monitoring method used to obtain consumption values, in line with a PCAF-aligned methodology and market practices used by frameworks such as GRESB and INREV.
Scaler calculates a Data reliability Score (0–100) at the asset level for:
- Energy
- Water
- Waste
This can be found at Data Collection Portal → Portfolio → Reports.
The score helps users understand the confidence level of reported KPIs, particularly for benchmarking, trend analysis, and ESG disclosures.
Data quality vs data reliability
Data quality and data reliability are related but distinct concepts. Quality checks ensure valid inputs; reliability scoring indicates how well those inputs represent actual consumption over time.
Expand to understand the difference
- Data quality refers to whether input data is correct, complete, and valid at the point of entry
- Managed through Scaler's alert system (errors, missing data, warnings)
- Ensures data meets basic standards before calculations run
- See Understanding data completion and alerts for details
- Data reliability refers to how representative and trustworthy consumption data is over time
- Evaluated through monitoring method scoring and coverage analysis
- Helps users understand confidence in reported KPIs
- This article focuses on reliability methodology
Both work together: quality checks ensure valid inputs, while reliability scores indicate how well those inputs represent actual consumption.
What is data quality?
Data quality describes whether your data is:
- Complete – all required fields are filled in
- Accurate – values are correct and properly formatted
- Consistent – logic and values align across the portfolio
- Valid – data passes required validation and reporting rules
High data quality ensures that analytics and reports are built on clean, usable inputs.
How Scaler supports data quality
Scaler supports data quality through a combination of structural controls and automated checks:
- Required fields and controlled dropdowns for standardization
- Built-in validation rules to flag invalid or illogical entries
- Automated alerts for:
- Errors (invalid values)
- Missing data
- Warnings (outliers or inconsistencies)
- Outlier detection to highlight unusual changes in resource use
Together, these mechanisms help ensure data is reliable enough for analytics and reporting from the start.
What is data reliability?
Data reliability reflects how closely reported consumption represents actual energy, water, or waste use over time. Scaler assesses data reliability based on the Monitoring method used to obtain consumption values, in line with a PCAF-aligned methodology and market practices used by frameworks such as GRESB and INREV.
Scaler calculates a Data reliability Score (0–100) at the asset level for:
- Energy
- Water
- Waste
This can be found at Data Collection Portal → Portfolio → Reports.
The score helps users understand the confidence level of reported KPIs, particularly for benchmarking, trend analysis, and ESG disclosures.
Next to the asset level bar graphs reflecting data reliability, Scaler also displays an annual trend graph of progress at portfolio level to give insight where improvements in data coverage or monitoring could be made.
How data reliability is determined
Data reliability is based on three primary factors:
Monitoring method
Area coverage– how much of the asset’s floor area is covered
Time coverage– how many days in the reporting period have valid data
The overall reliability score is a weighted average based on Monitoring method and how much area and time they cover.
Monitoring method scoring
Different monitoring methods contribute differently to the data reliability score based on how closely the underlying data reflects direct measurement of the asset. All monitoring methods represent data that is manually entered by clients — Scaler does not generate or substitute this data on your behalf.
Monitoring method | Score | Data source type | Classification |
Smart meter | 1.0 | Automatically recorded | Measured |
Invoice | 0.8 | Directly reported | Measured |
Conventional meter | 0.8 | Directly reported | Measured |
Standard consumption (cluster average) | 0.6 | Aggregated from comparable assets | Measured |
Estimation (Number of bins) — Waste only | 0.2 | Derived from observed/invoiced waste volumes | Measured |
*Standard consumption (postal code) | 0.4 | Averaged across geographic area | Modelled |
*Manual estimate | 0.2 | Internally calculated | Modelled |
*Based on our mapping to the INREV SDDS reporting framework, these methods correspond to estimated data as defined by INREV. All other methods, including Estimation (Number of bins), correspond to actual data in INREV SDDS outputs.
Note: The final asset score reflects both the monitoring method score and the proportion of area and time covered by each method.
Measured vs modelled consumption data
Measured methods reflect data that is directly recorded, reported, or invoiced at the asset level — or derived from aggregated actual meter readings provided by grid operators.
Modelled methods reflect data that has been averaged or calculated without direct measurement of the specific asset. This data is still entered manually by the client — the label describes the nature of the underlying data source, not a calculation performed by Scaler.
Important: Scaler has a separate linear extrapolation model that generates estimated consumption figures to fill data gaps. This is entirely distinct from the monitoring methods described here. Scaler-generated estimates currently appear only in analytics and a meter-level data export — they are not included in any reports. See Estimated consumption data in Scaler for more information.
Monitoring method name changes
As of February 2026, the dropdown options have changed to be general rather than using Netherlands specific terminology.
New name | Previous name |
Standard consumption (cluster average) | estimation_(sjv_cluster) |
Standard consumption (postal code) | estimation_(sjv_postal_code) |
Manual estimate | estimation_(calculation) |
Country-specific standardized consumption systems
In some countries, grid operators provide standardized consumption data by aggregating actual meter readings across multiple assets. This data is available in two forms, each with different reliability levels:
Cluster average vs postal code average
- Cluster average (
Standard consumption (cluster average)): Aggregated actual consumption from similar asset types (e.g., office buildings of similar size and characteristics). Because this reflects real meter readings from comparable assets, it is treated as actual/measured consumption with a score of 0.6.
- Postal code average (
Standard consumption (postal code)): Averaged consumption across all building types within a geographic area. This broader averaging is treated as modelled consumption with a score of 0.4 because it includes diverse property types and use patterns.
Why the difference matters
Cluster averages provide more reliable data because they compare similar assets, while postal code averages dilute specificity by including all property types in an area. This distinction affects both data reliability scoring and whether consumption is categorized as actual/measured or modelled/estimated for reporting purposes.
Netherlands: Standaard Jaarverbruik (SJV)
The Netherlands uses Standaard Jaarverbruik (SJV) data provided by grid operators:
- SJV Cluster data: Corresponds to
Standard consumption (cluster average)— aggregated from comparable asset types
- SJV Postal Code data: Corresponds to
Standard consumption (postal code)— averaged across all buildings in a postal code
Other countries
Similar systems may exist where grid operators provide aggregated consumption data at the asset-type or connection level. Where such systems are methodologically comparable to cluster-based aggregation, clients may use Standard consumption (cluster average) in Scaler, provided this accurately represents how the consumption data was obtained.
Explaining anomalies
Scaler flags unusual consumption trends and allows users to add contextual comments directly in the platform.
This supports transparency around:
- Building closures
- Occupancy changes
- Operational disruptions
- Other one-off events
Why data quality and reliability matter
Together, data quality validation and data reliability scoring help ensure that:
- Inputs are correct from the start (via quality checks)
- KPIs can be trusted over time (via reliability scoring)
- ESG reporting is defensible and auditable
- Decision-making for decarbonisation pathways is better informed
Related: To understand how Scaler validates data at the point of input through automated alerts, see Understanding data completion and alerts.