Could your business use energy data modelling for housing stock?

According to the Climate Change Committee, 23% of total UK carbon emissions come from building stock. There are currently around 29 million homes in the UK, with 62% of those below the recommended minimum Energy Performance Certificate (EPC) rating of C. In England, an estimated 3.18 million households are classed as fuel poor (13% of all households).

Together, these households offer a large market that would benefit from advice on living more sustainably and energy efficient home upgrades, as well as support with grants such as the Warm Homes discount.

There are wider economic benefits to be gained from supporting improvements to the UK’s housing stock. According to Bankers for NetZero, retrofitting the UK’s building stock with low carbon solutions could reduce national energy costs by £7.5 billion a year, create more than 150,000 jobs over a 10-year period and cut carbon emissions by 20%.

You might already have some data that details the home energy characteristics of your region. This data can be used to target and support vulnerable energy consumers, fuel poor households, or households looking to invest in energy efficient products.

It’s essential to use both the data you have and modelling solutions to improve the energy efficiency of homes. This can often be time-consuming, especially since:

• Open source EPC data doesn’t have complete coverage – in fact, it only covers about 60% of homes in the average local authority area.
• Costs for energy efficient retrofits, eligibility for grants and accurate estimates of savings and carbon reductions based on different retrofit scenarios will need experienced data analysts and digital modelling tools.

Data modelling for missing energy characteristic data based on a variety of factors, such as location, distance to grid, property age and neighbouring housing is one of the many things that our Home Analytics database can do. The database can vary depending on the underlying data sources and the fields being modelled.

Working in tandem with our Home Analytics data, we also developed a Portfolio Energy Assessment Tool that is able to run, test and model different scenarios to find the most effective way to get fuel bill savings and SAP score increases. This gives our clients the best course of action for fuel poverty and carbon reduction plans, or it can specify the best areas to install particular energy efficiency measures, such as new roof insulation or heating controls.

This modelled data has helped shape the strategy of some of our clients, including Wirral Council and Dacorum Borough Council.

Ultimately, given the challenges, having a modelled database is important if you want to have a comprehensive evidence base that uses best practice research, modelling and analysis to:

• Baseline the current conditions of housing stock.
• Identify cost-effective upgrades that can be installed in each home.
• Estimate the impacts of undertaking retrofit work (eg costs, fuel bill savings, SAP score improvement, CO₂ emission reductions).
• Investigate different ways to optimise delivery over time.
• Identify current and emerging funding sources that can accelerate uptake
• Translate the regional retrofit strategy into an action plan for local job creation and skills requirements.

If Home Analytics would benefit your business, programme, or the development of a business case for domestic home retrofit, contact us today.