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Accurate load forecasting is crucial for the optimized operation and planning of smart power grids.
Traditional centralized forecasting models struggle with data privacy concerns, communication overheads, and lack of model adaptiveness.
This paper proposes a federated learning-based framework for short-term load forecasting in smart grids.
Local machine learning models are trained on distributed private datasets across different stations of the grid and only the model parameters are communicated to a central server to create an aggregated global model for load forecasting, without exchanging any raw private data.
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IEEE/ICACT20250070 Slide.03
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