How to Use

The whole workflow in seven short steps.

1
Upload your energy data
Drop in a CSV of electricity consumption. The tool works out the structure for you.

Commonly handled layouts include:

  • Separate date and time columns
  • Combined timestamps
  • Half-hourly readings
  • Grid format (dates down the rows, intervals across the columns)
  • Settlement periods such as P1–P48 or 1–48
  • Other column orders and naming styles

You can also tag the site with an industry: School, Agriculture, Manufacturing, Sports facility, Warehouse, or None.

Industry selection never changes your actual consumption data. It only offers a reference shape if extrapolation is needed later.
Learn more about industry tagging
The tag is stored with the upload so that, once reviewed and approved, its anonymised shape can sit alongside other sites in the same category. Leave it as None if the site doesn't fit a category — nothing else in the workflow changes.
2
Check the data
Standardisation happens automatically — your job is a quick sanity check.
  • Identifies the date, time and consumption fields
  • Converts everything into one standard format
  • Adds half-hourly readings up into hourly consumption
  • Handles UK daylight-saving clock changes
  • Flags missing readings
  • Produces a consistent 365-day profile

Look over the consumption graphs and the validation summary before moving on. If anything about the detected structure looks wrong, open the detected-columns details on the check screen and confirm the fields before continuing.

3
Review missing data
See how much of the year is measured, missing, or already extrapolated.
  • Actual data — measured readings from your file, including genuine 0 kWh readings
  • Missing — hours with no reading at all (blank or absent), left empty rather than zero
  • Extrapolated — modelled hours, kept in their own column

A 0 in your file is treated as a real reading of zero consumption, so it is never overwritten by extrapolation. Only hours with no data are counted as missing.

The missing-periods table lists the dates involved. If the dataset is complete you can go straight to export; otherwise choose an extrapolation method.

4
Choose an extrapolation method
Fill Missing for gaps; Add Shortfall when part of the year isn't normal operation.
Fill Missing

Only fills hours where data is missing. Existing actual readings are never changed, and months whose actuals already exceed the estimate are left alone.

Add Shortfall

Use when part of the year doesn't represent normal site operation. Mark a reference period that does; the pattern learned there is used to top up the rest of the year.

Example: a warehouse ran fully March–August. Mark that as the reference period and the remaining months are topped up towards that level of operation.

Ranges use day and month only, and a range that ends before it starts (December → February) wraps across the year. Actual readings always stay separate from generated values.

Learn more about the extrapolation chat
The chat next to the charts is for specific requests — for example "treat the August shutdown as abnormal" or "scale December to match a known invoice". It turns your description into a plan you can apply or discard; the arithmetic itself stays deterministic.
5
Industry Reference Library (optional)
Reference shapes describe when energy is used — never how much.
Site data
How much energy
Reference library
When energy is used
Together
Extrapolated profile

The library collects approved datasets per industry as normalised shapes: hourly patterns, weekday versus weekend differences, daily patterns and seasonal patterns. Absolute consumption from another site is never reused.

Your site's own data always determines the amount of energy. A reference shape can only influence the distribution of extrapolated hours.
Learn more about how the library is used today
Right now the library is a browsable record of approved dataset shapes — the extrapolation engine works purely from your own site's actual data, so there is nothing to switch off. If reference shapes are wired into extrapolation later, the assistance will be optional and clearly labelled.
6
Review the result
Compare measured against generated values before you accept them.
  • Actual consumption — purple in the charts
  • Extrapolated consumption — green in the charts
  • Final modelled consumption — the two added together

The coverage panel shows the percentage of the year that is measured versus extrapolated, and any reference period you marked is listed above the charts. If the result doesn't look right, use Reset and try a different method.

7
Export
Two output shapes: Solar Edge (simple) or Zoho (detailed).
Solar Edge

Two columns — DateTime and Energy (kWh), where energy is the final total for that hour.

Zoho

Four columns — DateTime, Energy (kWh), Extrapolated (kWh) and Total (kWh), so measured and generated values stay distinguishable.

Exports are named after the project reference entered at upload, e.g. PRJ0123 Solar Edge.CSV.

Model quality and accuracy
Known energy totals are treated as fixed constraints. Site and industry profiles are used only to determine the shape of consumption within those totals. Model accuracy is validated using reconciliation checks and historical back-testing against approved complete datasets.

The Model quality panel below every generated profile shows:

  • Reconciliation — annual, monthly, day/night, daily and hourly totals must each match the level above exactly.
  • Hard constraints (your totals, night window, measured readings) are never altered; soft constraints (seasonal, weekday and hourly shapes) only decide timing.
  • Shape contribution — how much of the shape came from the site's own measured data versus the industry library, based on measured coverage.
  • Confidence — an arithmetic score from coverage, known constraints, contributing reference sites and library variability. It is never AI-generated.

A failed structural or reconciliation check blocks export until the inputs are fixed. Use Model accuracy in the sidebar to hide a known period from an approved dataset, rebuild it with the live model, and review energy, profile, peak and day/night error per industry, plus regressions between calculation versions.

Manual entry (no CSV)
Build a full hourly year from annual or monthly totals when no half-hourly file exists.

Switch to Manual on the upload screen, then:

  1. Enter the project reference, dataset name and industry as usual.
  2. Choose Annual total (one figure, in kWh, MWh or GWh) or Monthly totals (a 12-month grid). Blank months are estimated from the industry shape and flagged as Estimated in the reconciliation table.
  3. Optionally add a Day / Night split — enter the night energy or a night percentage and set the night window (default 00:00–07:00). The generated profile matches both the day and night totals exactly.
  4. Press Generate Consumption Profile. You get the same 8,760-row 2025 calendar, graphs and exports as an uploaded file.

Distribution across months, days of the week and hours comes from the industry reference shape; the magnitude comes only from the totals you entered. All arithmetic is deterministic, and every export row carries the input method, monthly target, target source and shape version so the result is auditable.

Manual results are modelled, not measured, so they are never contributed to the Reference Library.
Which option should I use?
Pick the line that matches your dataset.
My dataset is complete

Review the graphs and export.

A few hours or days are missing

Use Fill Missing.

Several weeks or months are missing

Use Fill Missing, and note in the review that a large share of the year is modelled.

Part of the year is reduced or abnormal operation

Use Add Shortfall and mark a normal operating period as the reference.

I have very little representative data

Extrapolate if you must, but treat the result as low confidence — the coverage percentages show how much is generated.

Help improve future reference profiles

Completed datasets can be approved for the Reference Library, contributing their anonymised consumption shape to the selected industry.

  • Only approved, actual measured data contributes.
  • Extrapolated or generated values are never used to build reference shapes.
  • Another site's absolute consumption values are never used when extrapolating a new site.