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FreeGNN: Continual Source-Free Graph Neural Network Adaptation for Renewable Energy Forecasting
arXiv – CS AI|Abderaouf Bahi, Amel Ourici, Ibtissem Gasmi, Aida Derrablia, Warda Deghmane, Mohamed Amine Ferrag||2 views
🤖AI Summary
Researchers developed FreeGNN, a continual source-free graph neural network framework for renewable energy forecasting that adapts to new sites without requiring source data or target labels. The system uses a teacher-student strategy with memory replay and achieved strong performance across three real-world datasets including GEFCom2012, Solar PV, and Wind SCADA.
Key Takeaways
- →FreeGNN enables renewable energy forecasting on new sites without access to labeled data from target locations.
- →The framework combines spatio-temporal GNN with teacher-student strategy and memory replay to prevent catastrophic forgetting.
- →Testing on three real-world datasets showed strong performance with MAE ranging from 0.382 to 5.237 across different energy sources.
- →The approach addresses privacy and cost constraints in renewable energy grid management through source-free adaptation.
- →Implementation is open-sourced for reproducibility and real-world deployment in adaptive renewable energy systems.
#ai#machine-learning#renewable-energy#gnn#graph-neural-networks#energy-forecasting#continual-learning#source-free-adaptation
Read Original →via arXiv – CS AI
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