Applications of Machine Learning Methods for Sustainable Development in Power Engineering
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F23%3A00366694" target="_blank" >RIV/68407700:21230/23:00366694 - isvavai.cz</a>
Výsledek na webu
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DOI - Digital Object Identifier
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Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Applications of Machine Learning Methods for Sustainable Development in Power Engineering
Popis výsledku v původním jazyce
This document aims to summarise the learnings from the first two years of PhD level study from the perspective of the author, who is focused on exploring the applications of Machine Learning based methodologies within power engineering systems. The document first explores the motivation for the chosen topic, then covers the current learnings about the state-of-the art in the field and follows with specific research project proposals aiming to answer research questions connected to the dissertation topic. In the last part of the document, key research aims are laid out, and a formal topic for the dissertation is proposed, as a material for discussion with the expert committee.
Název v anglickém jazyce
Applications of Machine Learning Methods for Sustainable Development in Power Engineering
Popis výsledku anglicky
This document aims to summarise the learnings from the first two years of PhD level study from the perspective of the author, who is focused on exploring the applications of Machine Learning based methodologies within power engineering systems. The document first explores the motivation for the chosen topic, then covers the current learnings about the state-of-the art in the field and follows with specific research project proposals aiming to answer research questions connected to the dissertation topic. In the last part of the document, key research aims are laid out, and a formal topic for the dissertation is proposed, as a material for discussion with the expert committee.
Klasifikace
Druh
O - Ostatní výsledky
CEP obor
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OECD FORD obor
20205 - Automation and control systems
Návaznosti výsledku
Projekt
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Návaznosti
S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2023
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů