About this project

This project applies Physics-Informed Neural Networks (PINNs) to model and forecast the thermal behavior of power transformers within the Belgian transmission network. Using historical transformer measurements together with load and ambient variables, the model combines physical transformer equations with data-driven learning to estimate internal temperatures and identify potential overheating risks. This hybrid approach bridges white-box thermal modeling and AI-based prediction, enabling more accurate forecasting of transformer performance and supporting the development of data-driven predictive maintenance strategies.

Discover the team behind this project

Mario Real Enrique
Mario Real EnriqueArtificial Intelligence | Physicist
I am a motivated professional with a solid background in physics, mathematics, and data analysis. My academic journey and internships have provided hands-on experience with Python, PowerBI, and machine learning tools, alongside a problem-solving mindset.
Published On: 26 January 2026Categories:

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