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

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...

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Bibliographic Details
Main Authors: Pascal Riedel, Kaouther Belkilani, Manfred Reichert, Gerd Heilscher, Reinhold von Schwerin
Format: Article
Language:English
Published: Elsevier 2024-12-01
Series:Energy and AI
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
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