Most people think heat pumps are sized using one design temperature. They are not.
Modern heat pump sizing combines two different engineering questions:
Peak power
What is the maximum heating power required during cold outdoor conditions?
Operating distribution
How many hours does the building operate at each outdoor temperature?
The second question is where climate data becomes essential.
Step 1: Start with an hourly climate year
The calculation begins with a representative climate dataset containing 8760 hourly outdoor-temperature values. In Climetry, this is the role of the Climetry Environmental Year (CEY).
For every hour, the outdoor temperature is known. Instead of asking, what is the annual average temperature?, the engineer asks: how often does every temperature actually occur?
Heating-system sizing needs a climate with meaningful cold-weather operation. Singapore is excellent for humidity and corrosion examples, but it is not useful for demonstrating heating-load duration curves. The example below uses real hourly Helsinki-Vantaa Airport observations.
Step 2: Build a 1 C temperature duration histogram
The 8760 temperatures are grouped into 1 C bins. This is the resolution Climetry uses for engineering temperature-duration outputs. The example below uses Helsinki-Vantaa Airport observations annualized from the 2015-2024 period.
Instead of 8760 individual values, the engineer now has a compact description of the climate. The histogram immediately answers: how long does the heat pump operate at each outdoor temperature?
Step 3: Calculate building heat loss
For every temperature bin, the building heat loss is calculated. The colder the outdoor air, the larger the required heating power.
Example required heating power: 8.2 kW.
Example required heating power: 7.5 kW.
Example required heating power: 6.9 kW.
This determines the peak heating load and also prepares the calculation of annual heating energy.
Step 4: Convert power into annual energy
Now the climate histogram becomes powerful. In the Helsinki example, the -8...-7 C bin occurs for about 91 annualized hours. If the building requires 7.5 kW in that bin, the annual energy contribution is:
The same calculation is repeated for every 1 C temperature bin. The sum over all bins gives the annual heating energy demand.
Why engineers do not use annual average temperature
An annual mean temperature of 10 C says almost nothing about heating demand. Two cities may have the same annual average but completely different temperature distributions.
One may spend hundreds of hours below freezing. The other may almost never freeze. The duration histogram captures this difference immediately.
Beyond temperature
This histogram-based thinking is not limited to heating. Exactly the same engineering principle can be applied to other environmental loads.
Relative humidity
How many hours does a product operate above RH80 or RH90?
Absolute humidity
How much moisture is present in the air around electronics and enclosures?
Dew point
How close is the air to condensation-relevant conditions?
Temperature cycles
How often do thermal cycles occur, and how large are they?
From weather data to engineering climate
Traditional weather datasets describe the atmosphere. Engineering decisions require something different: how long is my product exposed to a certain condition?
This is the idea behind the Engineering Climate Fingerprint™ used by Climetry. Instead of presenting thousands of hourly values, Climetry transforms representative climate data into engineering-relevant exposure distributions that can support qualification planning, durability assessment and environmental design decisions.
Turn climate data into engineering exposure distributions.
Analyze location-level thermodynamic conditions, exposure histograms and professional CEY-based outputs.