DC 4: Use of machine learning tools for estimating EGs performance.
Doctoral Candidate
Claudia Guidolin
Bio
Claudia graduated in 2026 with an MSc in Environmental Engineering from the University of Padua, Italy, following a BSc in Civil Engineering. Claudia’s academic background focused on numerical modelling and statistical methods applied to natural hazards and their mitigation structures.
During my BSc and MSc, she completed two research internships. The first, involved an experimental investigation comparing laboratory tests and in situ measurements to characterize the stratigraphy and mechanical properties of a river embankment. The second, carried out at INRAE, focused on developing surrogate models for the design of rockfall protection structures, strengthening my interest in data-driven approaches to engineering applications.
As a Doctoral Candidate in the GENIUS Doctoral Network, Claudia’s research focuses on applying machine learning techniques to Energy Geostructures, with the aim of developing tools to support their design and management
The aim of this project is to investigate the possibility to use machine learning
techniques to predict the behaviour of EGs in terms of heat exchange and help in the optimisation of their functioning.
Specific Objectives
Collect monitoring and numerical data on different EG types and different functioning scenarios (heating-cooling modes, different external and climatic conditions).
Define input and output parameters adapted for the evaluation of the heat exchange of EGs, and design a model based on artificial neural networks. Train models and check their performance on available data.
Use the models to predict the performance of EGs under different future scenarios (change in climatic conditions, temperature, activation modes, etc).
Secondment
Politecnico di Torino (Italy)
University of Lille (France)
This project is funded by the European Union as part of the Horizon Europe programme, Marie Skłodowska-Curie Actions Doctoral Networks (MSCA-DN) 2024 and under the Agreement number 101226708
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