Open Data could save UK energy market £400 million in next 10 years

Open data has helped sectors like transport and medicine thrive. Used in the energy market, it can help the private sector work with the government to identify and correct inefficiencies

Open data has helped sectors like transport and medicine thrive. Used in the energy market, it can help the private sector work with the government to identify and correct inefficiencies

With the support of the Open Data Institute (ODI) and the Department of Energy and Climate Change (DECC), clean tech firm Guru Systems revealed how open data could zero-in on hidden inefficiencies in energy generation, distribution and consumption in the UK. 

Analysing consumption data from dozens of heat networks across the UK to identify hidden inefficiencies, Guru Systems found that the use of open data could save the UK energy market £400 million and 800,000 tonnes of CO2 over the next 10 years.

These financial savings were calculated from lower capital expenditure (68 per cent) – as better-sized networks are cheaper to build, as well as increased fuel efficiency across the lifetime of these new systems. Similarly, the carbon savings come from operational fuel reductions and savings in the embodied CO2 of the smaller systems compared to the alternative.

What is open data?

Open data refers to information that anyone can access, use or share. When large companies or governments release non-personal data, it allows small businesses, individual citizens, scientists and researchers to build solutions collaboratively.

For example, when Transport for London (TfL) made its data public, it opened up opportunities for a host of start-ups to develop travel apps like Citymapper, now a staple in most Londoners’ way around the city’s byways.

TfL has seen a 58:1 return on investment since opening up its data, and according to a report from McKinsey, a global market powered by open data from across seven sectors would create between $3 trillion and $5 trillion a year.

Open data and the energy sector
Heat networks see heat produced at a central point and distributed to a number of homes or buildings across a network. They currently account for 2 per cent of the UK energy market, but form a key part of the government’s strategy for meeting its decarbonisation targets.

25 per cent of London’s properties expected to be linked to localised networks by 2025.

The project uncovered issues throughout the networks’ lifecycles, from oversizing in design, to problems in specification, commissioning and operation.

“Designers currently use an outdated model to calculate the most amount of heat needed at any one time and this has lead to networks being drastically oversized to meet demand they will never actually experience,” Casey Cole, managing director of Guru Systems, said.

Guru Systems has calculated developers of heat networks can save more than 30 per cent on capital building costs, including expenditure on central boilers and pipework, if designs are sized using real-life data.

Residents also benefitted from the project and thanks to the improvements made, on one of the schemes involved residents saw their energy tariff almost halved from 7.7p to 3.8p per kWh.

Real-time problem-solving

Drawing on the success and findings of the trial, Guru Systems has now launched Pinpoint, a real-time data analytics platform, to allow operators to uncover the same inefficiencies. It has also released open data from the platform – with the help of the ODI- to allow engineers correctly size and design new heat networks.

“Open data is key to helping the UK energy sector deliver cheaper, more efficient heat to people living on heat networks across the country.

“By releasing anonymised consumption data we have been able to show operators, designers and subsequently customers what good looks like,” Cole added.

Pinpoint allows operators to identify inefficiencies and issues at any point in the system, including in individual properties.The software can also suggest measures for improvements to the system with expected costs so that clients can see the impact of any possible interventions.

Machine-learning algorithms can recognise patterns in the problems they discover, allowing the platform to constantly evolve and improve itself.

Praseeda Nair

Praseeda Nair

Praseeda was Editor for from 2016 to 2018.

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