🤖 Not Every AI Model Is a Digital Twin

The journal Energy and Industry of Russia has published a new interview with Alexandra Khalyasmaa, Head of the Research Laboratory for Digital Twins in the Electric Power Industry at the Ural Power Engineering Institute (UralENIN), Ural Federal University (UrFU).

The interview focuses on the application of digital twins in modern power engineering and is particularly relevant to researchers and professionals working with artificial intelligence.

💻 A high-performing machine learning model does not automatically qualify as a digital twin.

If an algorithm is not linked to a specific physical asset and its current operational data, it may still serve as an effective tool for analysis or forecasting. However, the use of the term digital twin in such cases requires careful consideration.

📊 In real-world power engineering applications, algorithmic performance begins with data quality. Telemetry, diagnostic results, images, operational history, and measurement data must be accurately integrated with one another and associated with the relevant physical equipment.

Moreover, the system should provide not only a result but also an assessment of its uncertainty. In the energy sector, a seemingly precise and confident numerical output produced from insufficient data can be considerably more dangerous than an explicit indication that the available information is inadequate.

🔍 The interview also discusses advances in intelligent diagnostics, robotic monitoring, and multimodal data processing, ranging from imagery acquired across different spectral ranges to telemetry and spatial information.

This is what applied artificial intelligence in the energy sector looks like: an algorithmic workflow that systematically transforms data into an engineering decision.

Read the full interview:

https://www.eprussia.ru/epr/541-542/9380164.htm