Case study

Salling Group uses AI to save millions on energy consumption

Only a few months after starting to use Ento's AI-platform, the Energy Managers at the Danish retail giant Salling Group had verified savings of several million Euro.

Salling Group uses AI to save millions on energy consumption

Artificial intelligence brings results immediately

Salling Group’s ambitious climate plan includes investments of approximately EUR 330 million over the next few years in equipment like heat pumps and solar. However, rising energy prices have created an impetus to take more immediate action as well. Here, the most significant gains can be achieved by optimising building operations with the help of artificial intelligence.

This is a tool all major building owners can use immediately without any large up-front investments if energy consumption data is available.

Innovative solution quickly creates large energy savings

The artificial intelligence from the Danish software company, Ento, uses consumption data from utility companies’ electricity meters, a data source most companies will have access to if smart meters are installed.

Danish building owners can grant access to this data free of charge simply by submitting a power of attorney with a digital ID, but the process varies from country to country. For the German Netto stores, the data will be provided by the utility company and in other cases it may come directly from the smart meters. The energy optimisation platform works on any energy data source.

Salling Group manages 700+ supermarkets with the help of Ento’s AI

For Salling Group in Denmark, this meant that all +700 supermarkets and hypermarkets in Denmark went onto the platform. After ingesting data, all possible energy savings for each building were ranked according to the greatest possible savings.

There is thus no need for a long process of integrating data or investing in new expensive hardware in order to get started. The innovative part of the Ento solution is to use artificial intelligence to create insight into energy consumption and then to suggest where Salling Group’s energy managers can both save money and reduce CO2 emissions.

“We are working on all fronts to lower our energy consumption, but combating energy waste through Ento’s solution is an easy way to achieve energy savings immediately – and the results speak for themselves!“

Martin Kortegaard
Energy Manager, Salling Group

Reducing energy waste is a cheap and overlooked method of lowering energy consumption

The results at Salling Group are implemented by adjusting selected technical building systems. Experience with Ento’s solution for more than 10,000 buildings shows that total energy consumption can often be reduced by 5-20% in the first year, simply by optimising technical systems in the buildings.

Some building owners, such as the Danish bank Arbejdernes Landsbank, have saved 20% of total electricity consumption by using Ento’s artificial intelligence at their 70 branches.

Studies show that up to 30% of energy consumption in commercial buildings is wasted and can therefore be reduced without affecting building comfort. It simply requires awareness of where the adjustments are needed.

“Salling Group’s results are not unique on our platform. What makes the case of Salling unique is, of course, the scale. Few building owners have energy consumption on that scale – it truly affects the bottom line”.

Henrik Brink
CEO, Ento

Reducing energy waste is, by definition, not disruptive to building comfort, because consumption is primarily reduced outside of building opening hours.

A focus on action and savings rather than reporting

No additional data or meter solutions are required to start identifying large savings using Ento's solution. Instead, publicly available data sources like weather and store opening hours are used. The system’s artificial intelligence can understand what affects consumption in individual buildings, and it is not necessary to measure the consumption of individual technical systems.

The Ento system’s deep understanding of what affects consumption in each building also enables the automatic identification of buildings that are not operating optimally, as well as subsequent documentation of energy savings.

We have been given a hand on the analysis, as it is done automatically with artificial intelligence. The complexity is there, but you just don’t have to deal with it. Therefore, you can create action rather than spend time on analysis.

A system running on publicly available data has clear advantages: no manual setup is required to get started. This means that the day after they gave access to their consumption data, energy managers at Salling Group could begin to optimise their buildings’ energy consumption.


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