PV Power Forecasting using Distributed Machine Learning for Smart Energy Grid – Muhammad Ammar ZAFAR
Muhammad Ammar ZAFAR
Master's thesis
PV Power Forecasting using Distributed Machine Learning for Smart Energy Grid
(An extension to Federated Learning)
Abstract:
This thesis investigates the application of federated learning and tree-based models, such as LightGBM and Catboost, in Photovoltaic (PV) power forecasting. Addressing challenges in accuracy, uncertainty, and scalability, the study designs a robust federated learning architecture tailored for tree-based forecasting models. The novel aggregation strategy efficiently combines updates from multiple nodes …moreAbstract:
This thesis investigates the application of federated learning and tree-based models, such as LightGBM and Catboost, in Photovoltaic (PV) power forecasting. Addressing challenges in accuracy, uncertainty, and scalability, the study designs a robust federated learning architecture tailored for tree-based forecasting models. The novel aggregation strategy efficiently combines updates from multiple nodes …more
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:
ZAFAR, Muhammad Ammar. \textit{PV Power Forecasting using Distributed Machine Learning for Smart Energy Grid}. Online. Master's thesis. České Budějovice: University of South Bohemia in České Budějovice, Faculty of Science. 2024. Available from: https://theses.cz/id/g4hel1/.
The right form of listing the thesis as a source quoted
ZAFAR, Muhammad Ammar. PV Power Forecasting using Distributed Machine Learning for Smart Energy Grid. České Budějovice, 2024. diplomová práce (Mgr.). JIHOČESKÁ UNIVERZITA V ČESKÝCH BUDĚJOVICÍCH. Přírodovědecká fakulta
Full text of thesis
Contents of on-line thesis archive
Published in Theses:- světu
Other ways of accessing the text
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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