As energy costs are increasing and Europe is putting pressure to meet sustainability goals, industrial companies are urgently seeking ways to operate more efficiently, starting with energy consumption in mind. One technology proving to be both transformative and practical is the digital twin. It is already commonly used in aerospace and high-tech engineering, but digital twins are now rapidly gaining ground in sectors like manufacturing, energy and logistics. In the case of energy applications, there are two key uses of these: predicting energy consumption and optimizing maintenance operations.
A digital twin is a dynamic, real-time digital replica of a physical system even if it is a machine, an entire production line or a power grid. It connects physical assets with their virtual counterparts through sensors, data streams and intelligent algorithms. This connection allows real-time monitoring, simulation of future scenarios and AI-driven prediction of behaviors of the energy use. Unlike traditional static models, digital twins continuously learn and evolve with the system they mirror, making them ideal for complex environments. According to Gartner, by 2027 more than half of all advanced industrial facilities will integrate digital twin technology into their operations, reflecting the enormous value they deliver in decision-making and performance optimization, including energy consumption.
Predicting Energy Consumption in Industrial Systems
Energy consumption in industrial environments is often inefficient, fluctuating and difficult to manage proactively. Here’s where digital twins can change the game. By continuously collecting data from machines, HVAC systems, lighting and production processes, a digital twin can accurately model a energy profile of a plant.
This enables companies to visualize consumption in real time, identify anomalies and simulate the impact of operational changes — like adjusting production schedules or turning off idle equipment. These simulations help managers make decisions that align production needs with energy efficiency goals. Companies such as Siemens and ABB have implemented digital twin platforms that analyze thousands of energy variables simultaneously, leading to reductions in consumption of up to 20% in some facilities.
Digital twins can also help forecast future energy demand based on predictive inputs like order volume, weather conditions or machine load. This is particularly useful in energy-intensive industries like metallurgy, chemical processing or food manufacturing, where even small improvements in efficiency can bring significant cost savings and emission reductions.
Optimizing Maintenance Through Prediction
In traditional industrial settings, maintenance is often reactive (“fix it when it breaks”) or preventive (“schedule it every X hours”). With a digital twin in place, maintenance becomes predictive and adaptive. The system continuously monitors key health indicators such as vibration, temperature, pressure or lubricant condition — and uses historical data and machine learning to detect patterns that precede failures. This allows maintenance teams to act before breakdowns occur, reducing the risk of unplanned problems and extending the life of critical components.
Take General Electric as an example: GE’s digital twin systems monitor jet engines, power turbines and industrial equipment across the globe. Their predictive maintenance programs have reduced downtime by up to 30–50%, while also cutting costs, energy consumption and improving safety. Similarly, in the manufacturing sector, companies are using digital twins to schedule maintenance only when the system’s condition justifies it — not too early, not too late.
Here’s another example from the Istanbul megapolis in Türkiye, where the local operator of the metro stations utilizes digital twins to achieve a 37.5% increase in operational efficiency and a 25% reduction in energy consumption and maintenance costs.
Business Impact and Strategic Value
When applied effectively, digital twins bring set of benefits. Energy consumption can be optimized by 10–20%, while maintenance costs may drop by 20–40% thanks to better planning and fewer emergency interventions. Most significantly, unplanned downtime — a major source of lost revenue in industry — can be reduced by up to 50%, and environmental impacts such as carbon emissions can be measurably lowered.
But the value extends beyond operations. Digital twins specially support strategic decision-making by providing executives with a data-driven performance view, resource usage and investment needs.
Digital twins are no longer just futuristic models as they are real and scalable tools that help businesses become more intelligent, sustainable and resilient. In case somebody is running a single production line or managing a complex network of facilities, digital twin technology offers a clear path to energy efficiency, maintenance optimization and long-term cost savings.
References
