Energy Optimization

Optimizes energy use in plants, buildings, vessels, networks, and charging infrastructure by using models, simulations, and digital twins to find waste and adjust operations.

Business impact

• Energy consumption — Mercedes-Benz reports 20% energy savings at the Rastatt paint shop, and Stellantis and Verizon also describe reduced energy use.
• Energy cost — Volkswagen reports a 12 percent reduction in energy costs at Volkswagen Poznań.
• Annual energy savings — Rockwell Automation reports projected annual energy savings of 15 to 30%.
• Energy efficiency — NYK Line’s vessels are described as 40% more energy efficient than conventionally fuelled ships in the same fleet.

Data requirements

• Energy-use trends (tabular) — Used to analyze consumption patterns and demand fluctuations.
• Simulation data (tabular) — Used to diagnose efficiency declines and test operating alternatives.
• Digital twin data (tabular) — Used to compare ideal and actual behavior in monitored assets.
• Geospatial data (tabular) — Used to optimize EV charging infrastructure siting and utilization.

AI methods and techniques

• Machine learning — Used to analyze energy-use trends, learn usage patterns, and support optimization decisions.
• Simulations — Mercedes-Benz uses simulations to diagnose efficiency declines in its paint shop.
• Digital twins — Used to compare ideal and actual behavior, support diagnostics, and model building or vessel operation.
• Predictive analytics — Used to anticipate demand fluctuations and reduce energy use by recommending changes before consumption rises.
• Prescriptive control — Used to adjust production parameters or switch off radios during low-traffic periods.
• Geospatial analytics — Used by SurplusMap to optimize EV charging infrastructure siting and utilization.

Industries

Real-world evidence

8 documented case studies on record.

Companies using this: Mercedes-Benz, Honeywell, Rockwell Automation, Stellantis, Verizon, Volkswagen, NYK Line, SurplusMap.