Property Intelligence
Our property intelligence is designed to produce a bespoke heat pump quote for any address in England, Scotland, and Wales.
By using only the property address, we gather data from various sources, including Energy Performance Certificates (EPCs) and the Ordnance Survey. This provides us with a detailed view of the building fabric, property size, and the current heating system.
Our bespoke algorithms estimate both the annual energy requirements and the peak heat loss of the property. Designed with Heat Geek’s industry experience, this algorithm captures all key factors influencing heat pump installation suitability.
A property suitability assessment calculates the potential for heat pump installation. This assessment considers factors such as heat loss, building occupancy, and outdoor space availability for the heat pump. If suitable, we provide estimates for labour costs to install the system.
See the API in action
This API powers our Upgrades platform, which allows homeowners to get a heat pump quote in minutes. To see the API in action, visit Upgrades.
Estimating Heat Loss
While Energy Performance Certificates provide annual energy estimates, we employ our own heat loss algorithm to estimate peak heat loss and annual running costs. This enables us to predict heat requirements for any home in Great Britain and leverage Heat Geek’s extensive industry experience for more accurate results.
For heat pump design, we calculate several key values:
- Peak heat requirement: Used for sizing the heat pump to ensure it can supply heat year-round.
- System efficiency: A measure of how efficiently the heat pump meets both heating and hot water demands. This is the consumer-facing number to set realistic expectations.
- Annual energy demand: An estimate of the total electricity demand of the heat pump to help understand running costs.
We have developed a custom approach to separate hot water and heating demand, referencing the Microgeneration Certification Scheme (MCS), but fine-tuned to reflect modern heat pump efficiency during the hot water cycle.
| Category | Heat Loss Calculation Approach | Heat Pump Efficiency | Energy Demand (kWh) |
|---|---|---|---|
| Heating | - Custom methodology based on Heat Geek field knowledge | - Heat pump selected to meet peak heat loss | EH = QH / SCOPH |
| - Calculates peak heat loss and annual heat demand (QH) | - Defaults to 45°C flow temperature | ||
| - Building fabric, location, and "typical" heating usage | |||
| Hot Water | - Calculates annual hot water requirement (QDHW) | - SCOP of selected heat pump at 55°C | EDHW = QDHW / SCOPDHW |
| - Based on CIBSE approach (45 litres per person per day) | |||
| Combined | QTotal = QH + QDHW | SCOPSystem = QTotal / ETotal | ETotal = EH + EDHW |
Heating Demand
We developed a bottom-up modelling methodology to estimate a home's peak heat loss. This integrates data from EPCs, Ordnance Survey, and other third-party sources. Key building characteristics, such as age, insulation levels, and wall type, are used to estimate heat loss per square metre.
The peak heat loss is used to size the heat pump and estimate annual energy demand. Degree days data for the property location is incorporated, assuming typical heating usage.
Our heat loss methodology has been validated through independent testing on 100 homes, achieving an average accuracy within 17% of surveyed heat loss values.
Hot Water Demand
Our hot water demand calculations follow CIBSE and MCS guidelines. We assume 45 litres of hot water per person per day, stored at 50°C, with an inlet temperature of 10°C. The number of occupants is derived from the number of bedrooms in the property.
SCOP (Seasonal Coefficient of Performance)
The SCOP for space heating is based on EN14825 testing, commonly available for UK heat pumps. For domestic hot water (DHW), efficiency is determined using EN16147 testing. While manufacturers may not publish EN16147 results, we estimate hot water performance based on SCOP for a 55°C flow temperature, following previous guidance (Source).
References
Our End-to-End Tuning
While similar to the MCS heat loss calculation, our approach uses a machine-learning-driven model to derive heat loss characteristics. This model incorporates data from past heat loss surveys conducted across many homes in the UK. This enables our estimates to become more accurate over time, as we collect more data from completed installations.