iNeP - integrierte Netzentwicklungsplanung für die Energieträger Strom, Gas und Wärme Vorwerk, Daniela; Schulz, Detlef; Micheely, Stefan; Koch, Oliver Henry; Cosler, Cristoph; Heise, Johannes; Mostafa, Marwan; Povel, Alexander; Töbermann, Christian Stand der Technik und Digitalisierung bei integrierten Energiesystemen, Sektorenkopplungs- und Mobilitätstechnologien. - Hamburg : HSU, 2021. - (Hamburger Beiträge zum technischen Klimaschutz ; Bd. 3). - Seite 47-56 (2021)
Open Access
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Publisher DOI
This part of the course focuses on how to utilize ML methods to model and operate electric power systems. Electric power systems consist of generation units such as PV, loads or consumers and the grid that connects those actors and supports to transport energy. This part of the course helps to understand the data-driven modelling of generation units (e.g. PV & fuel cells), modelling of load behavior, and to formulate and solve a state estimation problem for distribution grids using neural networks.
This part of the course includes lectures to introduce the basics that are followed by practical examples and coding.
Performance accreditation:
m1785-2022 - Machine Learning in Electrical Engineering and Information Technology<ul><li>p1778-2022 - Machine Learning in Electrical Engineering and Information Technology: mündlich</li></ul>
Barthelme, J. (2022). Technisch-ökonomische Systemmodellierung und -anlayse eines urbanen Quatiers hinsichtlich des Einsatz von Wasserstoff als primärer Energieträger.