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.
Anotace v angličtině
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.
Klíčová slova
Machine Learning (ML), Photovoltaic(PV), A Friendly Federated Learning Framework
(FLWR), Mean Prediction Interval Range (MPIR), and Mean Quantile Loss (MQL),
Random Forests (RF), LightGBM, CatBoost
Klíčová slova v angličtině
Machine Learning (ML), Photovoltaic(PV), A Friendly Federated Learning Framework
(FLWR), Mean Prediction Interval Range (MPIR), and Mean Quantile Loss (MQL),
Random Forests (RF), LightGBM, CatBoost
Rozsah průvodní práce
97
Jazyk
AN
Anotace
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.
Anotace v angličtině
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.
Klíčová slova
Machine Learning (ML), Photovoltaic(PV), A Friendly Federated Learning Framework
(FLWR), Mean Prediction Interval Range (MPIR), and Mean Quantile Loss (MQL),
Random Forests (RF), LightGBM, CatBoost
Klíčová slova v angličtině
Machine Learning (ML), Photovoltaic(PV), A Friendly Federated Learning Framework
(FLWR), Mean Prediction Interval Range (MPIR), and Mean Quantile Loss (MQL),
Random Forests (RF), LightGBM, CatBoost