Salling Group's Energy Savings through AI: A Case Study

Technology Category

Analytics & Modeling - Machine Learning
Sensors - Utility Meters

Applicable Industries

Buildings
Retail

Use Cases

Building Automation & Control
Predictive Waste Reduction

Services

System Integration

The Challenge

Salling Group, a Danish retail giant, was faced with the challenge of reducing its energy consumption and costs. The company had an ambitious climate plan that included investments of approximately EUR 330 million over the next few years in equipment like heat pumps and solar. However, the rising energy prices necessitated more immediate action. The most significant gains could be achieved by optimising building operations, a task that required a solution that could be implemented immediately without any large up-front investments. The solution also needed to be applicable to all major building owners and be able to work with available energy consumption data.

About The Customer

Salling Group is a Danish retail giant with over 700 supermarkets and hypermarkets in Denmark. The company is committed to reducing its carbon footprint and has an ambitious climate plan that includes significant investments in renewable energy sources. However, the rising energy prices have created a need for more immediate action. Salling Group needed a solution that could help it optimise its building operations and reduce energy consumption and costs without requiring large up-front investments. The company also wanted a solution that could provide insights into energy consumption and suggest ways to save money and reduce CO2 emissions.

The Solution

Salling Group turned to Ento, a Danish software company, for a solution. Ento's artificial intelligence platform uses consumption data from utility companies’ electricity meters, a data source most companies have access to if smart meters are installed. The AI platform was able to ingest data from all of Salling Group's 700+ supermarkets and hypermarkets in Denmark and rank all possible energy savings for each building according to the greatest possible savings. The AI platform created insight into energy consumption and suggested where Salling Group’s energy managers could both save money and reduce CO2 emissions. The platform also used publicly available data sources like weather and store opening hours to understand what affects consumption in individual buildings. It was able to automatically identify buildings that were not operating optimally and document energy savings.

Operational Impact

The implementation of Ento's AI platform has allowed Salling Group to make significant energy savings without disrupting building comfort. The AI platform has provided the company with insights into its energy consumption and has suggested ways to save money and reduce CO2 emissions. The platform has also enabled the automatic identification of buildings that are not operating optimally and has provided documentation of energy savings. The use of publicly available data sources has meant that no manual setup was required to get started. As a result, energy managers at Salling Group were able to begin optimising their buildings’ energy consumption the day after they gave access to their consumption data.

Quantitative Benefit

Savings of several million Euro within a few months of using Ento's AI platform.
Potential reduction of total energy consumption by 5-20% in the first year.
Some building owners have saved 20% of total electricity consumption using Ento’s artificial intelligence.