Original title: PV Power Forecasting using Distributed Machine Learning for Smart Energy Grid
Authors: ZAFAR, Muhammad Ammar
Document type: Master’s theses
Year: 2024
Language: eng
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, enhancing forecast accuracy.
Keywords: A Friendly Federated Learning Framework (FLWR); and Mean Quantile Loss (MQL); CatBoost; LightGBM; Machine Learning (ML); Mean Prediction Interval Range (MPIR); Photovoltaic(PV); Random Forests (RF)
Citation: 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

Institution: University of South Bohemia in České Budějovice (web)
Document availability information: Fulltext is available in the Digital Repository of University of South Bohemia.
Original record: http://www.jcu.cz/vskp/73293

Permalink: http://www.nusl.cz/ntk/nusl-694989


The record appears in these collections:
Universities and colleges > Public universities > University of South Bohemia in České Budějovice
Academic theses (ETDs) > Master’s theses