The plausibility check helps ensuring that onboarded operational measurement data is correct and aligns with credible sustainability benchmarks. This process validates energy consumption data and flags any implausible values for further review.
How the Plausibility Checks Work
What is Checked?
Plausibility checks apply to energy consumption data (electricity and heating) and follow two validation approaches:
Year-over-Year (YoY) Comparison: Compares newly entered values to data from the same period in the previous year.
Benchmark Testing: Compares new data against predefined upper and lower threshold values based on building type and procurement method (landlord, tenant, or unspecified electricity).
If either check fails, the data is flagged as ‘Potential issue’.
The plausibility status of an operational measurement entry is assigned strictly by the calculation and cannot be changed by the user.
Plausibility Check Process
Step 1: Data Preparation
Before running a plausibility check, all energy-related Operational Measurements (OpMs) are grouped into electricity and heating based on their subtype and purpose. They are then aggregated or split into the monthly level and normalised for vacancy. This ensures that low values are not incorrectly flagged.
Step 2: Electricity Plausibility Check
For electricity data, the plausibility check includes:
YoY Check: If the absolute difference from the previous year exceeds 40%, the value is flagged as a ‘Potential issue’.
Benchmark Test: Consumption intensity is calculated based on Gross Internal Area (GIA) and adjusted using a correction factor. If the value falls outside the threshold range, it is flagged as a ‘Potential issue’.
Thresholds vary by building type and procurement method (landlord, tenant, unspecified). For example:
Building Type | Tenant Electricity (kWh/m2/month) | Landlord Electricity (kWh/m2/month) |
Residential | 0.560 - 40.356 | 0.067 - 26.808 |
Office | 0.155 - 20.456 | 0.178 - 20.740 |
Retail | 0.304 - 50.067 | 0.200 - 20.567 |
*The lower and upper thresholds were derived using the 1% and the 99% quantile of existing operational measurement data of BuildingMinds customers. If electricity is used for heating (e.g., heat pumps), heating plausibility checks apply.
Step 3: Heating Plausibility Check
For heating data:
Option 1: YoY Check compares monthly heating consumption values with those of the same month in the previous year, adjusted for heating degree days (HDD). If the difference exceeds 40%, it is flagged as ‘Potential issue’.
In the YoY check, only operational measurements that pass the benchmark test can be used as a base for comparison.
Option 2: The Benchmark Test compares the monthly values with the adjusted thresholds based on local climate conditions and HDD scaling factors. If the value falls outside expected thresholds, it is flagged as ‘Potential issue’.
For example, heating benchmarks use annual scaling factors based on HDD data from Eurostat and EU research projects. The method ensures that variations in insulation and heating efficiency across regions are accounted for.
Conclusion
Plausibility checks prevent incorrect energy data from affecting sustainability calculations.
Values are tested against both YoY trends and benchmark ranges. When there is no previous data for a YoY check, only the benchmark test is used for the plausibility check.
Electricity and heating checks adjust for procurement type, building type, and local climate conditions.
Future updates will improve the detection and classification of implausible data.