Merton Council – why we chose AI to cut energy waste

The London borough of Merton identified energy waste valued at £25,000 within 15 minutes of employing a new artificial intelligence tool.

Programme

The council turned to AI to make sense of a wealth of energy consumption data, in a drive to reduce energy consumption in support of commitments linked to the borough’s climate emergency declaration announced in July.

Using consumption data for council-run schools, algorithms applied by our AMR DNA service quickly flagged unnecessary energy use outside normal operating hours. The cost of energy waste was estimated at £25,000.

“We have been collecting consumption data for many years through automated meter reading, and it has been very useful in identifying the root cause of specific energy-related problems. We understood the value inherent in the data but analysing the entire portfolio in near real-time would have needed an army of analysts. Now we can accomplish this and more through AMR DNA.” said Richard Neil of Merton Council.

Outcome

Now the council is applying the technology across its real estate portfolio, not only to prevent energy waste, but also to leverage continuous data analytics to ensure each building management system ‘learns’ what best energy performance looks like.

The building management industry faces the huge challenge of making best use of existing resources, including data. The approach Merton has taken shows it’s possible to embrace these challenges to help meet climate change commitments. George Catto, AMR DNA Client Services Director, commented: “Over half of all London boroughs have now declared a climate change emergency and they, like local authorities all around the country, will likely face challenges around reducing waste energy. AI is set to play a key role in developing the data-driven actions needed to optimise energy consumption in buildings and to help councils move towards carbon neutral operations.”

AMR DNA, which is powered by kWIQly architecture, uses AI-driven processes to interrogate energy data, find patterns of energy waste often hiding in plain sight, and then provide tailored recommendations. Thereafter, AI tracks the impact of changes, provides an audit trail of projects, monitors day-to-day consumption and automatically flags corrective actions that need to be taken. And because analysis is continuous, the best consumption profile for each building is progressively ‘learned’.

Said Richard Neil: “In the case of the schools, not only were we able quickly to identify sites using gas outside of school time, we were also automatically shown which of our sites we needed to focus on first to get the biggest results and use our resources most effectively.”

Earlier this year, Merton Council stepped up its commitment to combating carbon emissions and rising temperatures by declaring a climate change emergency.

Under the declaration, the council has pledged to make every effort to become a carbon neutral organisation by 2030. The actions will involve work to reduce and decarbonise the energy used in all its 340 buildings, with AI now being employed to build on progress made since 2009, which had already led to emissions being cut by 35%.