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GP-OML Course – Fundamental Knowledge on Transport, Infrastructure & Logistics

GP-OML Course – Fundamental Knowledge on Transport, Infrastructure & Logistics 23 september 2024 10:00 t/m 16:00 - Locatie: Utrecht | Zet in mijn agenda Register now Registration is required for the GP-OML course Register now Date 23 September, 14 & 28 October, 18 November 2024 Time 10.00 – 16.00 h Location Utrecht Lecturer Prof. dr. Bert Van Wee and Dr. Jan Anne Annema Days 4 ECTS 1 (attendance only) | 4 (attendance + passing assignment) Course fee Free for TRAIL/Beta/ERIM members, others please contact the TRAIL office The course consists of four parts. In Part I (1 day) we describe how the transport system is structured. The topics are: A general introduction explaining the structure of the course and the related book; the wants and needs of people which drive passenger transport; the needs of companies to transport goods; dominant land-use factors which drive the transport system. transport resistance factors (time, costs, effort) which interacts with the transport system. In Part II (1 day) impacts of the transport system on accessibility, the environment and safety are discussed: Traffic flow theory(final course of Part I) Transport technology to reduce transport’s negative impacts Accessibility Transport and the environment Traffic safety Part III (1 day) gives an introduction in transport policy and related research. The reasons why governments develop transport policies will be explained. Cost-Benefit Analysis (CBA) and Multi Criteria Analysis (MCA), the two most important methods to ex ante evaluate candidate policy options will be treated. Finally, some dominant transport models and their applications will be discussed. Transport policy Transport futures research Appraisal methods for transport policy Transportation models and their applications Part IV (1 day – at least for those who do an assignment; others: on voluntary basis). Participants present the outline of their assignment. In the group the assignments will shortly be discussed. More information and registration

PowerWeb Lunch Lecture: Martin van den Heuvel

PowerWeb Lunch Lecture: Martin van den Heuvel 19 september 2024 12:45 t/m 13:30 - Locatie: Faculty of EWI, Mekelweg 4 (Chip Hall) - Door: Martin van den Heuvel, Magnus Energy | Zet in mijn agenda “The Role of Control Room of the Future in Realising the Energy Transition - Pains and Gains” Moderator: Dr Francesco Lombardi Registration: fill the form at Abstract: To realise the energy transition, centralized, unidirectional power grids are being replaced by decentralized, multidirectional systems with hard-to-predict demand. Meanwhile, as the grid ages and electrification surges, the role of grid companies evolves from grid operators to system operators, balancing supply, demand, and capacity in increasingly shorter timeframes. This transition leads to new challenges on how the power grid is managed and requires more advanced and real-time control. Hence, it is pivotal to simulate disruption to the power grid in a controlled research environment in realising the energy transition – i.e., the Control Room of the Future. In the Control Room of the Future, IT-OT convergence (the integration of data management systems (IT) with industrial operation systems (OT)) and sophisticated Energy Management Systems (EMS) are crucial, as exemplified by high-frequency measurements, and platforms like WAMS for real-time analysis. We will discuss new functions like inertia modeling, data integration, and capacity market management, highlighting their significance in maintaining a resilient and efficient energy grid. Notably, these advancements scale from regional (DSOs) to national (TSOs) and even panEuropean levels, involving entities such as RCCs and ENTSO-E. This shift is essential for sustainability, geopolitical security, and the integrity of critical infrastructure, which speeds up the energy transition from different angles. Bio: Martin van den Heuvel is a Partner at Magnus Energy. He has over 20 years’ experience in several areas in the utilities sector, including smart meter, grid operation, control room of the future, enterprise asset management and grid digitization. With a background in mechanical engineering (TU/e, cum laude) and extensive experience in energy sector liberalization and digitization, Martin will share insights on the Control Room of the Future, along with the challenges and opportunities posed by grid congestion and the shift to balance-driven systems. Join us to understand how these changes will drive the energy transition forward.

GP-OML Course – Machine Learning

GP-OML Course – Machine Learning 12 september 2024 09:30 t/m 15:00 | Zet in mijn agenda Fully booked Date 12, 19 & 26 September & 10 October 2024 Time 09.30 – 12.00 h & 13.00 – 15.00 h Location Online Lecturer Prof. dr. Inneke Van Nieuwenhuyse (Hasselt University) and Prof. dr. David Wozabal (VU Amsterdam) Days 4 ECTS 1 (attendance only) | 4 (attendance + passing assignment) Course fee Free for TRAIL/Beta/ERIM members, others please contact the TRAIL office This course provides a comprehensive introduction to the fundamental principles of machine learning and statistical pattern recognition. It covers both the theoretical foundations and practical implementation of machine learning methods, guiding participants through the end-to-end process of data investigation using machine learning techniques. The objective is to either uncover new insights in areas with limited prior knowledge or achieve accurate predictions of future observations. Beginning with an overview and characterization of machine learning methods, the course delves into general principles for data manipulation, feature engineering, model selection, calibration, and evaluation. It then focuses on supervised learning, specifically tree-based regression and classification models, which are currently considered state-of-the-art for tabular data as well as on Gaussian processes. The morning sessions primarily emphasize theoretical aspects, while the afternoon sessions offer hands-on demonstrations of machine learning methods using Python. The course does not center around specific applications, as those are addressed in the optional project. Participants are encouraged to apply the foundational knowledge gained in the course to a machine learning application relevant to their own scientific domain. Throughout the sessions, examples of machine learning applications are provided for reference More information and registration

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