Smart Grid Energy Forecasting: Enhancing Forecast Performance through Federated and Split Learning – Marwan Moustafa Mohamed AHMED
Marwan Moustafa Mohamed AHMED
Master's thesis
Smart Grid Energy Forecasting: Enhancing Forecast Performance through Federated and Split Learning
Abstract:
Federated and split learning models were applied to forecast power consumption in smart grids, with a focus on integrating renewable energy sources while prioritizing data privacy, computational efficiency, and accuracy. The study conducted a comparative evaluation of these two methods, investigating various parameters influencing the performance of split learning.
Language used: English
Date on which the thesis was submitted / produced: 8. 2. 2024
Thesis defence
- Supervisor: prof. Dr. Andreas Kassler
Citation record
ISO 690-compliant citation record:
AHMED, Marwan Moustafa Mohamed. \textit{Smart Grid Energy Forecasting: Enhancing Forecast Performance through Federated and Split Learning}. Online. Master's thesis. České Budějovice: University of South Bohemia in České Budějovice, Faculty of Science. 2024. Available from: https://theses.cz/id/x4ubkf/.
The right form of listing the thesis as a source quoted
AHMED, Marwan Moustafa Mohamed. Smart Grid Energy Forecasting: Enhancing Forecast Performance through Federated and Split Learning. České Budějovice, 2024. diplomová práce (Mgr.). JIHOČESKÁ UNIVERZITA V ČESKÝCH BUDĚJOVICÍCH. Přírodovědecká fakulta
Full text of thesis
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Institution archiving the thesis and making it accessible: JIHOČESKÁ UNIVERZITA V ČESKÝCH BUDĚJOVICÍCH, Přírodovědecká fakultaUNIVERSITY OF SOUTH BOHEMIA IN ČESKÉ BUDĚJOVICE
Faculty of ScienceMaster programme / field:
Artificial Intelligence and Data Science / Artificial Intelligence and Data Science
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Bulánová, L.
9/2/2024