Dataset Overview
The scenario modeller is the engine behind the platform's scenario planning tool for buildings. Given a local authority area, a set of low carbon technologies (LCTs), an investment budget, and a target number of installations, it determines which properties should receive which technologies, and in which year, in order to best meet those targets within the available funding.
The model produces a property-level output for each scenario, indicating which technology has been assigned to each building, at what cost, and in which year.
Methodology
Building Data Assembly
The model begins by assembling a building-level dataset for the local authority. This draws together:
Domestic UPRNs
EPC data (energy rating, building type, age band, floor area)
Suitability flags and costs for each LCT (e.g. ASHP, GSHP, Rooftop PV)
Index of Multiple Deprivation (IMD) and income data
Where a scenario is configured with a portfolio filter (a user-defined subset of properties), the dataset is trimmed to only those UPRNs before further processing takes place.
Prioritisation
The model supports user-defined prioritisation rules, such as targeting properties with a low EPC rating or those in deprived areas. These rules are evaluated, and each UPRN is assigned a priority tier. Higher priority UPRNs are considered first when deploying technologies.
Where no prioritisation rules are defined, all properties receive equal priority.
Standard Assessment Procedure (SAP) Benefit Scores
Before deploying technologies to UPRNs, each property is assigned a SAP-based score for each technology. This score represents annual economic benefits (based on SAP benefit scores and fuel costs from RdSAP10). The score considers a standard timeframe of 10 years for each technology, and the total installation cost (including materials and labour). The deployment model uses this score to prefer installations that deliver greater energy efficiency improvements at the lowest cost.
Budget and Target Setup
The model supports input of an annual or total budget, and one or more installation targets per technology (e.g. "install 500 ASHPs by 2030"). The optimisation model tries to meet these constraints whilst maximising net benefit.
The budget constraint is always met, meaning the optimiser will not spend more money than specified in the input. However, installation targets may be missed due to the in-built deployment rate caps that limit annual deployment.
Deployment Rate Caps
To reflect real-world supply chain and installer capacity constraints, the model applies year-on-year deployment rate caps per technology. These limit the maximum number of installations that can occur in a single year, regardless of available budget. They are proportional to the existing number of deployments of the technology in the base year, meaning a low number of existing technologies will limit the amount of growth that can be achieved in future years.
Deployment Model
The deployment model optimally distributes the technologies across UPRNs, while respecting the budget caps and the user-defined technology targets. The model attempts to maximise the SAP benefit per property while minimising the installation costs.
Model Constraints
The input budget and deployment rate caps cannot be exceeded
The target technology inputs act as an upper threshold to the deployment model (the targets cannot be exceeded)
Technologies can only be installed to UPRNs where there is suitability (e.g. ASHP suitability is respected)