In the Laboratory of Analysis of Systems for the Assessment of Reliability, Risk and Resilience (LASAR3) at Politecnico di Milano we develop methods for risk and resilience assessment, reliability and availability analysis, prognostics and health management and maintenance by leveraging all available knowledge, information and data for the reliability, risk and resilience assessment of components, systems and critical infrastructures.
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Continuing Education Course
Artificial Intelligence for Energy Systems
II Edition | May 4–8, 2026
⏳ Time left to apply — deadline: 21 April 2026
Applications are now closed.
Building on the success of the I Edition, LASAR3 is co-organizing the II Edition of the course “Artificial Intelligence for Energy Systems”, directed by Prof. Enrico Zio. The course provides participants with methodological competencies, analytical skills, and hands-on practical knowledge on AI algorithms and computational tools for energy system modelling, virtual sensing, health management, forecasting, reliability analysis, risk and resilience assessment, and multi-objective optimization.
Mandatory modules (3 days combined)
AI Fundamentals
State-of-the-art data analysis & feature generation, clustering, regression, classification, prediction algorithms, optimization methods, and reinforcement learning.
Chair: Prof. Piero Baraldi, Politecnico di Milano
Implementation of AI Methods — Python Data Science Stack
Hands-on implementation of AI methods using the Python Data Science Stack. Computer labs: load prediction, temperature forecasting, occupancy estimation, PV generation forecasting, and more.
Chair: Prof. Emanuele Ogliari, Politecnico di Milano
Optional — choose one track (2 days)
AI for Modelling Thermal Systems
Application of AI to thermal systems modelling: demand flexibility, self-consumption enhancement, HVAC and building energy management.
Chair: Prof. Behzad Najafi, Politecnico di Milano
AI for Renewable Generation Forecasting & Integration of Electric Vehicles
Prediction-driven optimization for energy efficiency; renewable generation forecasting; integration of electric vehicles; benefits of AI in major energy systems.
Chair: Prof. Francesco Grimaccia, Politecnico di Milano
⚡ Modules 3 and 4 run in parallel — participants attend one of the two.
AI for Reliability Analysis & Maintenance Engineering
Reliability & availability analysis; prognostics and health management (PHM); preventive, condition-based, predictive and prescriptive maintenance; remote auditing and load disaggregation.
Chair: Prof. Ibrahim Ahmed, Politecnico di Milano
AI for Risk and Resilience Assessment of Energy Systems
Risk and resilience assessment and management; accidental scenario post-processing; particle filtering for failure time prediction; fuzzy methods for risk analysis.
Chair: Prof. Francesco Di Maio, Politecnico di Milano
⚡ Modules 5 and 6 run in parallel — participants attend one of the two.
For information: courses-deng@polimi.it · Min. 10 / Max. 30 participants · First-come, first-served
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