A new study from researchers at the University of Sharjah in the United Arab Emirates provides insights into how artificial intelligence-powered digital twins are helping to speed up the energy transition, while also emphasising their limitations.
In a review article for the journal Energy Nexus, the researchers concluded that digital twins had the potential to transform generation, management, and optimisation of energy across diverse clean energy platforms, accelerating the transition away from fossil fuels. However, the authors warned that current digital twin models still faced notable limitations restricting their full potential in harnessing energy from sources such as wind, solar, geothermal, hydroelectric, and biomass.
“Digital twins are highly effective in optimizing renewable energy systems. Yet, each energy source presents unique challenges—ranging from data variability and environmental conditions to system complexity—that can limit the performance of digital twin technologies, despite their considerable promise in improving energy generation and management,” the researchers said.
The study included a review of existing literature on the application of digital twins in renewable energy systems, allowing the authors to examine contexts, functions, lifecycles, and architectural frameworks to understand how digital twins were being used and where gaps remain.
To produce their insights, the researchers employed text mining techniques, using AI, machine learning, and natural language processing. These enabled them to analyse large volumes of raw data and uncover structured patterns, concepts, and emerging trends.
The authors identified research gaps, proposed new directions, and outlined the challenges to be tackled if the potential of digital twin technology in the renewable energy sector is to be fully realised. To take three of the five renewable energy sources analysed, the benefits and challenges the identified included:
For wind energy, on the plus side, digital twins were improving efficiency by predicting unknown parameters and correcting inaccurate measurements, as well as enhancing system reliability and performance. But the researchers said digital twins faced challenges in accurately modelling and monitoring environmental conditions, while also struggling to simulate critical factors such as blade erosion, gearbox degradation, and electrical system performance.
For solar energy, digital twins were helping to identify factors that influence efficiency and output power, enabling better system design and optimization. However, they were still falling short in reliably predicting long-term performance and had difficulty tracking panel degradation and accounting for environmental influences over time.
For geothermal energy, digital twins could simulate the entire operational process, notably drilling, enabling improved cost analysis and cutting development time and costs. But the lack of high-quality data was hampering the ability of digital twins to simulate subsurface conditions, while they also faced the complex process of modelling the long-term properties of geothermal systems, such as heat transfer and fluid flow dynamics.
The authors said their analysis of the limitations in the application of digital twin technology across renewable energy sources showed the need for more robust models that could address specific challenges unique to each renewable energy system.
The researchers offered a set of guidelines and a research roadmap aimed at helping scientists enhance the reliability and precision of digital twin technologies. Recommendations included improving data collection methods, advancing modelling techniques, and expanding computational capabilities to ensure digital twins could deliver trustworthy insights for decision-making and system optimisation.
Regional expertise
This UAE-based research underlines the strong focus of institutions and companies in the region on the development of AI-driven and digital twin technologies. For example, in May, we reported on how Abu-Dhabi based energy company ADNOC has incorporated AI into its operations with international partners.
The rising level of regional expertise in these technologies is also reflected in informed, vibrant discussion at the Future Digital Twin & AI Middle East event, the 2025 version of which takes place on 30 October in Abu Dhabi.
(Photo: Sharjah/Shutterstock)
