artificial intelligence for energy systems - ed.2

DIPARTIMENTO DI ENERGIA

The course offers lectures on AI methods and their practical application to energy components and systems. It has a modular framework with two mandatory modules: Module 1: AI fundamentals Module 2: Implementation of AI methods using the Python Data Science Stack. The participant can also choose from a portfolio of modules dedicated to the application of AI in different energy domains: Module 3: AI for modelling thermal systems Module 4: AI for renewable generation forecasting/ AI for integration of electric vehicles. Module 5: AI for reliability analysis and maintenance engineering of energy components and systems Module 6: AI for risk and resilience assessment of energy systems Please note that Module 3 and Module 4 will run in parallel: therefore participants may choose to attend only one of the two. Module 5 and Module 6 will also run in parallel: therefore participants may choose to attend only one of the two Lectures are held in English. All participants will receive a complete set of presentation slides with specific examples and case studies, selected reference lists and resources in electronic format. In the sessions of module 2, the general implementation of AI methods using Python Data Science Stack is practiced hands-on by the participants. The following modules also include hand-on practice, during which the participants directly apply the methods explained in the lectures to practical case studies of application.

Components and systems for electrical/thermal power generation,distributionandtransmissionareequippedwith sensing capabilities, which provide data that is informative on their operativeperformanceandstatus. Thesedataandother information(e.g.maintenancereports. inspection images, etc.) can be analyzed by artificial intelligence(AI)algorithmsandtools toextract information useful for operating the components and systems in an efficient,reliable,safeandsustainableway. The goal of this course is to provide the participants withmethodological competencies, analytical skills, and practicalknowledgeontheexistingalgorithmsandavailable computational toolsofAI forsystemmodellinganalysisand simulation,virtualsensing,componenthealthmanagement, forecasting,control,andremoteauditing, reliabilityanalysis andriskassessment management, multi-objective and multi-level optimization, anddecisionsupportsystems.

The expected participants to the course are data scientists, operation/maintenance engineers, asset/facility/energy managers, R&D and sustainability managers, researchers and PhD students working and studying in the areas of energy components and systems design, optimization, reliability and safety analysis, maintenance engineering, built environment, building, simulation/management, HVAC and thermal systems, power system modelling, electrical mobility, renewable energy generation and other energy domains.

Director: ENRICO ZIO

DIPARTIMENTO DI ENERGIA

GIULIA PERNICANO

0223993855

courses-deng@polimi.it