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Modelling, analysis and artificial intelligence

SINTEF has a strong competency within mathematical modelling and analysis. Using information collected from measurement at physical equipment complex systems can be analyzed and represented by models. With careful selection of useful data effective instrumentation with sensors can be achieved in a cost-efficient way.

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We have experience of digital models for real-time monitoring and presentation of system state, but also of predictive models and simulation for testing of possible scenarios. Both data driven and physical models are used depending on what is most appropriate for a specific problem.  

We work within these areas:

  • Optimization
  • Digital twins
    • Predictive modelling
    • Artificial intelligence

Typical projects for us are:

  • Modeling of micro climate, induced by terrain and buildings
  • Development of digital twins for simulation and optimization of power grids
    • Use of data from connected power meters (AMS) for analysis of the effect of PV panels at prosumers (plusskunder)

The methods we use are:

  • Simulation based on physical and data driven models
  • Machine learning, for example explainable AI and machine learning in control systems (ML-in-the-loop)
    • State estimation, for example for power flow and component state
    • Information modeling

Why choose SINTEF?
SINTEF has a broad experience in mathematics and modeling and can find the right way of tacking a wide range of challenges. SINTEF is also at the leading edge of AI and machine learning. 

Who are we doing this for? 

  • Industry
  • Distribution system operators

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