Weather and climate data

Weather data looks like a nice extra on a dashboard. It is not — it is what makes energy figures comparable at all.

A building that used 8 % less energy this January than last has not necessarily improved. January may simply have been milder. Without outdoor temperature there is no way to tell those two situations apart, and every year-on-year claim is guesswork. With it, consumption can be corrected for the weather, and what is left is the part you actually influenced.

You do not need to do anything to get this. Weather data is available for every building automatically.

Sources

• Frost — official weather observations for Norway from the Norwegian Meteorological Institute, taken from the station nearest the building.
  

• Weather (outside Norway) — a commercial weather service covering buildings in other countries, so a portfolio spanning borders is corrected on the same basis throughout.
  

• Climate normals — the long-run average temperature for the building's location. This is the reference that "a normal year" means when consumption is normalised. It is deliberately stable data and only changes when the underlying model is updated.
  

• Air quality forecast — forecast air quality for Norwegian locations.
  

• Netatmo — a weather station on the building itself, for cases where the nearest public station is not representative. See Sensors and indoor climate.
  

What it makes possible

Temperature correction. Properate offers several correction methods, and the choice is yours because they answer slightly different questions:

• Uncorrected — what the meters actually recorded. The right choice for invoices and for budget follow-up.
  

• Temperature-corrected — consumption adjusted for how cold the period was, so a mild winter does not read as an efficiency gain.
  

• Location- and temperature-corrected — additionally normalised for where the building is, which is what makes a building in Tromsø comparable with one in Kristiansand.
  

• ET-model-corrected — correction based on the building's own measured relationship between energy and outdoor temperature, rather than a generic degree-day assumption. This tends to be the most accurate for a building with a decent history of data.
  

The distinction matters in practice: an owner asking "did our measures work?" wants a corrected figure, and a finance team reconciling invoices wants an uncorrected one. Both are right, and picking the wrong one produces arguments that look like data problems.

Understanding a building's behaviour. The ET chart plots consumption against outdoor temperature. A building with sound control shows a clear, tight relationship. A scattered cloud, or a heating curve that does not flatten in mild weather, usually means something is running when it should not be — and that is visible long before it shows up in a monthly total.

Control. Outdoor temperature is an input to automation as well as to reporting: heating curves, summer night cooling, and deciding when preheating should start.

Worth knowing

• The nearest station may not be very near. In parts of the country the closest official station is some distance away, or at a different altitude. For buildings where that matters — coastal, mountain, or a city microclimate — a local weather station gives a materially better correction.
  

• Corrected and uncorrected figures will not match, and should not. If two people quote different numbers for the same month, check which correction method each of them had selected before looking for a fault.