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Ento > Case Studies > AI-Driven Energy Optimization in Retail Banking: A Case Study of Arbejdernes Landsbank

AI-Driven Energy Optimization in Retail Banking: A Case Study of Arbejdernes Landsbank

Technology Category

Analytics & Modeling - Machine Learning
Sensors - Utility Meters

Applicable Industries

Buildings
Retail

Applicable Functions

Facility Management

Use Cases

Building Automation & Control
Inventory Management

Services

Hardware Design & Engineering Services
System Integration

The Challenge

Arbejdernes Landsbank, a retail bank in Denmark, was committed to reducing its energy consumption and carbon footprint. The bank had already entered into a climate partnership with Ørsted in 2018, sourcing its electricity from renewable resources. However, it was still seeking ways to further reduce energy consumption in its building stock. The bank's Facility Management team faced the challenge of finding energy savings across multiple buildings, a task that was both cumbersome and time-consuming. Traditional solutions focused more on documentation rather than actual energy savings. The team needed a tool that could proactively identify energy savings and provide documentable results with minimal setup and resource requirements.

About The Customer

Arbejdernes Landsbank is a retail bank based in Denmark. In 2018, the bank entered into a climate partnership with Ørsted, sourcing its electricity from renewable resources. Despite this, the bank set ambitious climate goals, including an annual reduction of energy consumption in its building stock. The bank's Facility Management team was tasked with finding energy savings across multiple buildings, a task that was both cumbersome and time-consuming. The team needed a tool that could proactively identify energy savings and provide documentable results with minimal setup and resource requirements.

The Solution

Arbejdernes Landsbank chose Ento's AI-based solution to address their energy consumption challenge. Ento's system was given access to the bank's building consumption data through a simple online process. The system then automatically compared this consumption data with building and weather data to identify potential savings. The results were ready in less than 24 hours and were presented at a meeting before the collaboration was formalized. The implementation of energy improvements was primarily achieved by optimizing existing hardware, with changes made at the centrally controlled Building Management System. In other cases, local or external technicians performed the improvement tasks. The savings were automatically documented to ensure that the expected effect was achieved.

Operational Impact

The implementation of Ento's AI-based solution has led to significant operational improvements at Arbejdernes Landsbank. The system's ability to automatically identify and prioritize potential savings has streamlined the bank's energy management process, reducing the burden on the Facility Management team. The solution has also improved the indoor climate in the bank's buildings, which has had a positive impact on the bottom line. Furthermore, the system's ongoing automatic monitoring capability allows the bank to quickly identify and address unexpected changes in energy consumption, helping to avoid larger, unexpected increases in energy consumption.

Quantitative Benefit

The bank's energy consumption and climate footprint were reduced annually by more than 100 tonnes of CO2e.
This reduction corresponds to 16% of the bank's total consumption.
In some buildings, consumption has fallen by more than 50%.