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
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 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.
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
Quantitative Benefit
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