Introduction

This article explains how we estimate heat demand for non-domestic buildings, such as shops, offices, warehouses, and other commercial or public premises.

Because these buildings vary widely in size, purpose, and operating hours, the modelling approach is different from the one used for domestic properties.

The goal is to provide a best-estimate heat-demand profile for each building, including annual heat demand (kWh), energy use intensity (kWh/m2) and peak heating load (kWp).

Methodology

For each building, the model considers:

Building size

Larger buildings generally need more heating. We compare a building’s area with typical sizes in its sector to help estimate its expected heating intensity.

More specifically: each premise is assigned an area percentile using Building Energy Efficiency Survey (BEES) premise-size distributions:

Percentile is linearly interpolated from Ordnance Survey premise area data
Values above the BEES maximum are capped at the 100th percentile
Values below zero (possible for unusually small calculated areas) default to the 10th percentile

This percentile determines the intensity band applied to the premise. We assume direct correlation between area percentile and energy-intensity percentile.

Sector heating behaviour

Each type of building uses energy differently, and BEES provides typical heating shares for each sector. For example, some sectors use a large proportion of energy for heating, while others use much less.

Each premise is mapped to a BEES sector using its:

Primary classification code
Secondary classification code
Tertiary classification code

This mapping enables sector-specific modelling of energy intensity and heating share.

Energy-intensity benchmarks

BEES also provides typical heating demand per square metre for buildings of different sizes. We use this to estimate the annual heating requirement of each building.

Daily heating patterns

Commercial buildings often have predictable daily heating peaks, for example:

morning warm-up periods
daytime usage
lower evening activity

Where sector-specific patterns are available, we apply them. Where they are not, we estimate the pattern based on typical building behaviour and operating hours.

Calculating heat demand

To estimate the heat demand per m2, the energy intensity is multiplied by the sector-specific share of energy used for heating:

Finally, the total annual heat demand is determined using the following equation:

The daily peak heat demand is determined by adjusting the annual heat demand to the premise’s design conditions using the following formula:

Where:

kWh = Annual heat demand (kWh)

HDD = heating degree days

Ti = internal design temperature

Te = external design temperature

The heating degree days and external design temperatures are determined based on the specific location of the modelled region. The external design temperature is the yearly minimum within the region.

Assumptions:

Area percentile directly correlates with energy-intensity percentile.
Indoor design temperature is fixed at 21 °C.
If no sector-specific energy-usage pattern exists:
All final outputs are rounded to 2 decimal places.
Handling parent shells: Some non-domestic buildings are recorded as parent shells with multiple internal child units (e.g., shopping centres, industrial estates). To avoid under- or over-estimating heat demand, the following procedure is applied:
1. Identify parent shells using the AddressBase Classification Code.
2. Determine the parent sector by selecting the most common Primary-Secondary code among its child premises.
3. Estimate the parent shell’s heat demand using that derived sector code.
4. Reconciliation rule:
  1. If the calculated parent-shell heat demand is lower than any child’s heat demand, the parent heat demand is reset to the sum of all child heat demands for that TOID,