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Arbejdernes Landsbank Achieves 67% Annual Energy Savings through AI
Technology Category
- Analytics & Modeling - Machine Learning
- Networks & Connectivity - Radio Access Network
Applicable Industries
- Buildings
- Retail
Applicable Functions
- Facility Management
Use Cases
- Building Automation & Control
- Predictive Waste Reduction
Services
- System Integration
The Challenge
Arbejdernes Landsbank, a Danish retail bank, was facing significant energy inefficiencies across its 70 branches. The bank's building portfolio, a mix of older and newer buildings, had some well-run technical facilities controlled by the building management system (BMS). However, upon analyzing the energy consumption in its branches, it was discovered that not all building automation was functioning as intended. The branch on Bredgade in Kalundborg was identified as one of the buildings with the poorest energy performance. The energy consumption in the building had suddenly increased, with idle consumption rising from approximately 2 kW to just over 6 kW. The bank was closed more than two-thirds of the time, and the difference of 4 kW between good and poor energy performance was leading to significant energy waste.
About The Customer
Arbejdernes Landsbank is a stock-listed Danish retail bank with 70 branches. The bank has a diverse building portfolio, comprising both older and newer buildings. Some of these buildings have well-run technical facilities that are centrally or decentralised and controlled by the building management system (BMS). However, not all building automation was working as intended, leading to significant energy inefficiencies. The bank is committed to energy optimization and has an ambitious climate strategy, which includes investing in new technology to lower energy consumption.
The Solution
The bank turned to artificial intelligence (AI) to identify and rectify the energy inefficiencies. The AI identified the increased baseload consumption and helped the bank's energy managers to identify energy savings. The problem was found in the control of the ventilation system at the Kalundborg branch. Once the poor operation was noticed, it did not require new technology or many hours for the technician to correct the problem. The control of the ventilation system was optimized, and the baseload consumption fell back to the optimal level for the building. As part of an ambitious climate strategy, the bank also invested in new energy-efficient ventilation motors to further lower energy consumption. The AI also helped to document energy improvements after implementation.
Operational Impact
Quantitative Benefit
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