> ## Documentation Index
> Fetch the complete documentation index at: https://docs.abisko.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Gap Filling

> Estimating and reporting missing consumption data.

Abisko automates gap-filling in a transparent manner that maximizes estimation accuracy and aligns fully with GRESB reporting guidelines.

## Methodology

To properly address cases where individual meters have gaps on different dates, and to avoid unnecessary estimation, gap-filling is performed at the individual meter level rather than on aggregated values. All gap-filled meter values are then aggregated as a final step before reporting total consumption and emissions.

### Gap Filling Algorithm

The following algorithm is used to estimate missing data in accordance with GRESB guidelines.

<Steps>
  <Step title="Calculate data availability for each meter">
    Abisko first calculates the data availability for each meter based on its entries over the reporting period. Custom values are used if they are added by a user.

    [Click here](/gresb/data-availability) to learn about **Data Availability**.
  </Step>

  <Step title="Determine the data completeness for each meter">
    The data completeness is calculated for every meter over the data availbility period, rather than for over the whole reporting period.

    **Example:** For a meter, the data availability spans from 1/1/2024 to 10/31/2024 (304 days) and there is one month of missing data in the middle of the year (e.g. June), then the meter data completeness would be \[(304-30)/304] = 90.13% over the data availability period.
  </Step>

  <Step title="A gap-filling scaling factor is determined for each meter">
    The scaling factor is determined by a formula that limits the gap-filling to 20% of the meter’s reported data over the **Data Availability** period:

    * scaling\_factor = MINIMUM \[1, data\_completeness \* 1.2] / data\_completeness

    **Example:** For 90.13% data completeness, the scaling\_factor = 1/.9013 = 1.1095

    <Note>
      The scaling factor is based on available data and protects against over-filling. If only 10% of data is missing for a meter, it will be filled to 100%. If 50% is missing, it will be filled to 60%.
    </Note>
  </Step>

  <Step title="The total meter energy consumption is scaled for each meter">
    The scaling factor is multiplied against each meter’s total energy consumed over its **Data Availability** period.

    **Example:** If a meter has 500,000 kWh consumed over its data availability period, the final reported consumption with gapfilling for this meter is 500,000\*1.1095 = 554,750 kWh
  </Step>

  <Step title="Scale the energy consumption across all relevant meters">
    Steps \[1-4] are applied to all meters associated with a space (e.g. Whole Building (in-metric), Landlord / Tenant (assigned)) before consumption values are aggregated into their respective space-type totals for reporting.
  </Step>
</Steps>

***

## Reporting with Gap Filling

To report estimated data to GRESB,  check the box next to **Fill gaps** when generating an Asset Level Spreadsheet.

<img src="https://mintcdn.com/abisko/hDzFNPMaT6azOeSd/images/Screenshot2026-02-16at12.14.10PM.png?fit=max&auto=format&n=hDzFNPMaT6azOeSd&q=85&s=45ca3aec9bf963914b4e2805b40389f5" alt="Screenshot 2026 02 16 At 12 14 10 PM" width="2086" height="450" data-path="images/Screenshot2026-02-16at12.14.10PM.png" />