Ento > Case Studies > Arbejdernes Landsbank Achieves 67% Annual Energy Savings through AI

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

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 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.

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

The use of artificial intelligence for energy optimization has led to significant operational improvements for Arbejdernes Landsbank. The AI system was able to identify major energy waste in the building and helped the bank's energy managers to identify and implement energy savings. The bank was able to optimize the operation of technical facilities and install energy-efficient ventilation motors, leading to significant energy savings. The AI system also helped to document the effect of these annual savings, providing clear evidence of the effectiveness of the bank's energy optimization efforts. This has not only resulted in cost savings but also supports the bank's ambitious climate strategy.

Quantitative Benefit

Identified and rectified a 4 kW difference between good and poor energy performance
Reduced the annual energy consumption from approximately 68,000 kWh/year to 23,000 kWh/year
Achieved documented energy savings of 45,000 kWh