Working together to make innovation happen
Wednesday 11th March 2026
DIME (Demand Insights for Metered Enterprises) tackles a critical data gap for small and medium-sized enterprises (SMEs) with non-half-hourly metering, particularly in high street areas.
These businesses make up 99.8% of the UK’s private sector and are vital to achieving decarbonisation goals, yet DSOs lack visibility of their demand profiles.
DIME uses alternative data sources and machine learning to infer business types and create accurate demand profiles. This enables better network planning, cost savings through efficient reinforcement, and faster deployment of low-carbon technologies.
By preparing for Market-Wide Half-Hourly Settlement (MHHS) rollout, DIME ensures SMEs’ full integration into future energy strategies.
There are 5.5 million small and medium-sized enterprises (SMEs) in the UK, representing 99.8% of the private sector business population. These businesses are critical to achieving the UK’s decarbonisation goals by reducing their own emissions and must be accurately represented in network modelling. However, a major data gap exists for SMEs with traditional, non-half-hourly (NHH) metering—particularly those on high streets and in mixed-use urban areas. This gap affects demand modelling across all Distribution System Operators (DSOs), leading to inefficient investment and missed decarbonisation opportunities.
Improved access to data can have substantial financial impacts. For instance, analysis by Frontier Economics for SPEN showed that LV monitoring deferral benefits were worth £20.1m for 5,391 LV monitors – around £3,700 per monitor. Scaled across SPEN’s rollout, benefits reached £56m. For SSEN’s larger customer base, benefits could exceed £60m.
The techniques developed by this project will enhance the precision of network load models, providing greater certainty about medium-term demand. This enables SSEN – and eventually other DNOs – to make more informed decisions, reducing unnecessary conservatism and improving efficiency.
Accurate models help determine when assets are at risk of thermal or voltage constraints. With better data, reinforcement decisions can be delayed without compromising reliability, saving significant costs.
Example: The baseline could be quantified using the net present value (NPV) of current reinforcement plans.
SSEN will collaborate with the CGI, Frontier Economics and Southampton City Council for the DIME Discovery phase SIF innovation project.
Frontier Economics is one of the largest economic consultancies in Europe, and owned entirely by our staff. Energy is our largest sector specialism, and we bring a thorough understanding of the regulatory and commercial environment in which energy networks operate, as well as experience in delivering successful and impactful innovation projects.
The Frontier team assembled for this project brings together specialists in appraisals and evaluation, and energy network operations and regulation.
CGI has extensive experience in collecting, securing, analysing and using smart meter and other network data to support client business use cases. CGI have energy-specific experience in operating large data systems and network data analytics, including extensive data science techniques. Specifically, CGI currently operates SSEN’s smart meter data collection solution and network model project.
Southampton City Council brings place-based insight and access to a wide range of property types, which is essential for identifying and engaging non-half-hourly metered sites. Their understanding of local economic activity, planning priorities, and business demographics supports the targeting and validation of demand estimation. They also play a key role in facilitating data access and stakeholder engagement.
DIME successfully secured SIF Discovery funding, with the project starting in February 2026 for 4 months and currently preparing for the Project Kick-Off.
Total Discovery phase project costs: £170,659
SIF Discovery funding requested: £146,426
Start Date – 02 Feb 2026
End Date – 15 May 2026
Brandon Jones
