On the first day of the SPIE Optics + Optoelectronics 2025 Conference in Prague [1], my colleagues and I watched another colleague, Davorin Peceli, present our work on the digital twin. Among the participating organisations, there are no similar scientific operations like the one in the ELI ERIC. Frankly, there are not many similar operations in the world like ELI ERIC. Even each one in the same field has a unique way of operating, just like in the EU-Funded FlexRICAN project, where the commitment to carbon neutrality of Research Infrastructures (RIs) is the same, but the energy needs of ESS, ELI and EMFL, represented in the consortium, are different.
At the same Conference, whether my colleagues were talking about fibre optics, lasers at power levels and speeds beyond my imagination, or even just future projects, they all approached or directly referred to a few important pillars. Leaving aside project funding and grants, they very often oscillated around similar themes that shall concern us all: energy efficiency, stability, sustainability, reducing environmental harm, resource self-sufficiency, carbon footprint, etc.
More than one speaker talked about the application of artificial intelligence, digital twins, mathematical models and machine learning. Quickly, I realised that the fact that my colleagues were using these tools to simulate events on nanosecond timescales and the power of small Suns so that they don’t have to invest unnecessarily huge resources made sense.
ELI Beamlines, too, together with the colleagues from the consortium of the FlexRICAN project, have embarked on a journey to create a working model of a digital twin using artificial intelligence. If we manage to develop a system that will be universal for a large group of scientific institutions and, I am not afraid to predict, any building, it will be “a giant leap for humanity!”
What is a digital twin?
A digital twin is a virtual model of a real object – a building, for example – that is connected to that object through sensors and data streams. As a result, the model is continuously updated in real time, making it possible to monitor the current state, analyse the behaviour of the system and predict future developments. At the same time, the digital twin can be used to test different scenarios without interfering with the real environment.
According to Sharma et al. [4], a fundamental characteristic of a digital twin is its ability to communicate bidirectionally – not only does it receive data from the physical world, but it can also influence real-world operations through recommendations or automatic interventions.
Digital twins in building energy
One of the emerging areas is the use of digital twins in building energy management. Modern buildings are equipped with a variety of sensors – from temperature and humidity sensors, to CO₂ or occupancy sensors. This data can be collected and analysed using machine learning algorithms.
This way, digital twins can contribute to reducing greenhouse gas emissions, thus supporting the key objectives of European climate policy. In addition to their climate-friendly impact, they can assist in reducing the running costs of buildings and increasing the comfort of users.
Digital twins at the neighbourhood and city level
Latest approaches are extending the concept of digital twins to residential neighbourhoods or entire urban areas. For example, the OMEGAlpes tool, developed by Hodencq et al. [6], allows planning energy strategies at the neighbourhood level and simulating the impacts of different decisions.
Such systems help urban planners and energy managers to find optimal combinations of energy sources, design infrastructure interventions, or evaluate the effectiveness of different options for insulation and building retrofits. The aim is to integrate the digital twins into the concept of smart cities, which are data-driven and focused on sustainability.
Real-time as a key factor
One of the main technical requirements for digital twins is the ability for real-time analysis. As Es-haghi et al. (2024) [5] point out, effective use of digital twins requires fast algorithms that can process data and design responses instantaneously.
This places demands on both computational capacity and dataflow architecture. In some cases, edge computing is used to enable processing at the point of data collection, without the need to send it to remote servers.
Another important area is the modelling of dynamic phenomena that take place on different time scales. For example, the thermal behaviour of a building changes on a timescale of hours, while structural changes take place on a longer timescale. Chakraborty and Adhikari [8] discuss the creation of digital twins that can effectively integrate these different time scales into a single system.
Digital twin for high-end lasers: how to handle sensitive technology and save energy – ELI case
Modern high-power laser systems, such as ELI Beamlines, are extremely sensitive to their surroundings. Even small fluctuations in temperature or humidity can affect their performance – and thus the results of complex scientific experiments. For example, too low a temperature can disrupt the alignment of the optical elements and make the laser beam less accurate. Conversely, high humidity can lead to condensation on sensitive parts of the equipment, which can cause beam scattering or even permanent damage.
Add to that dust, chemicals and other airborne contaminants that can build up on the optics – and it’s clear that keeping a laser in perfect condition is no easy feat. That’s why it’s important to keep the environment under absolute control.
That’s where the digital twin comes in – a smart, digital model of the laser equipment that collects real-time data to help monitor and control what’s happening in the facility. This model acts as a virtual “copy” of the device, constantly assessing the current status, predicting potential problems and suggesting solutions before anything goes wrong. By simulating different conditions, the system helps to see what happens when humidity or temperature changes, for example, and adjust settings accordingly.
The aim of this new concept is to better control the conditions in which the laser operates, while increasing its stability and reliability. This technology is also being investigated in the EU-funded FlexRICAN project, aiming at enhancing the resource efficiency and reducing the environmental footprint of European RIs by developing, validating, and implementing new technologies and solutions via the adoption of a multi-method energy approach. In practice, this means that researchers are testing various smart tools – such as optimisation algorithms, artificial intelligence or machine learning – to help with better decision-making and operation.
One of the tools in use is OMEGAlpes, an energy modelling software. This, combined with techniques such as MILP (mixed integer programming) or Gaussian processes, can help not only with planning but also with how the system deals with uncertainties and variability in conditions. The results of this research should lead to more energy-efficient operation, a more stable environment and higher laser performance – all without undue environmental burden.
Challenges and open questions
Despite the promising developments, there are a number of challenges and open questions. Sharma et al. [6] identify several major challenges. With the rapid development of digital twins comes not only new opportunities but also a number of challenges that need to be addressed. One of them is the integration of heterogeneous data – buildings, cities and individual technological systems generate vast amounts of information from different sources, often in different formats and with different quality. For the digital twin to work properly, it is essential to link and harmonise this data and make it understandable across systems. Another key issue is the reliability and validation of models – that is, the question of how well the digital twin actually matches reality. Without careful validation, the model risks producing misleading results and leading to poor decisions. In addition, there is an increasing focus on cybersecurity because once the physical and digital worlds are connected, there is a risk of cyberattacks or leakage of sensitive information that can compromise operations and system security. Closely related to this are ethical and legal issues: who actually owns the data used by the digital twin? Who is liable if there is an error or damage based on the digital model? These challenges show that technological advances must go hand in hand with careful consideration of the rules, accountability and trustworthiness of the whole system.
Döllner et al. [9] propose a new approach that would link digital twins with climate models and environmental simulations. Such an integrated system could help to better respond to climate change and contribute to strategic planning for sustainable cities.
The future of digital twins
Digital twins represent a technology that can fundamentally transform the way we look at buildings, infrastructure and the overall management of energy systems. They make it possible to move from a traditional, reactive approach – that is, from dealing with problems as they arise – to much smarter, proactive management. Systems with a digital twin can anticipate needs, optimize their operations and suggest adjustments before any outages or losses occur.
Going forward, this technology can be expected to continue to evolve in several important ways. Artificial intelligence will play an important role, enabling the digital twins to independently design solutions without the need for human intervention – faster, more accurately and on a larger scale. It will also be essential to achieve standardisation of platforms and data formats, making it easier to connect different systems and ensure their compatibility with each other. Although this article focuses on Digital Twins from a Building Management perspective, digital twin technologies are beginning to be applied beyond the construction and energy sectors – for example, in healthcare, transportation or agriculture, where they can help to monitor, optimise and automate various processes.
Conclusion
Digital twins are revolutionising the way we perceive and manage physical objects – from individual buildings to entire city blocks. In the energy sector, they have the potential to make a significant contribution to achieving climate goals, reducing consumption and ensuring occupant comfort. However, the key to their successful deployment will be the ability to work effectively with data, the credibility of models and respect for ethical and legal considerations.
Developments in this area are dynamic and promising. Digital twins are becoming a bridge between technology and sustainability, between simulation and reality. It is likely that in the near future they will become a common part of the design and operation of buildings, research facilities and urban areas. And it is their ability to optimise operations, predict problems and save resources that can help shape a smarter, more efficient and sustainable world.
This article was written by Jakub Urban from ELI ERIC, who contributes to Work Package 7 – Implementation of Flexibility & Carbon Reducing Solutions, Task 7.2: Maintaining indoor climatic conditions.
REFERENCES
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- Karel Čapek – Factory for the Absolute / Krakatit. Prague: Československý spisovatel, 1982. Karel Čapek’s writings. ISBN 22049-82, p. 247
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