Enhancing PV feed-in power forecasting through federated learning with differential privacy using LSTM and GRU

Given the inherent fluctuation of photovoltaic (PV) generation, accurately forecasting solar power output and grid feed-in is crucial for optimizing grid operations. Data-driven methods facilitate efficient supply and demand management in smart grids, but predicting solar power remains challenging d...

Full description

Bibliographic Details

Published in:	Energy and AI
Main Authors:	Pascal Riedel, Kaouther Belkilani, Manfred Reichert, Gerd Heilscher, Reinhold von Schwerin
Format:	Article
Language:	English
Published:	Elsevier 2024-12-01
Subjects:	Federated learning Deep learning Recurrent neural networks Data privacy Solar power forecasting Smart grid
Online Access:	http://www.sciencedirect.com/science/article/pii/S2666546824001186