Federated Learning (FL) has emerged as a transformative paradigm in machine learning,
enabling decentralized model training across multiple devices while preserving data …

Federated Learning (FL) enhances predictive accuracy in load forecasting by integrating
data from distributed load networks while ensuring data privacy. However, the …

The integration of AI and ML into energy forecasting is crucial for modern energy
management. Federated Learning (FL) is particularly noteworthy because it enhances data …

Federated learning (FL) has established itself as a communication-efficient, privacy-aware,
and cost-effective technique for training machine learning models in energy forecasting. This …

The integration of artificial intelligence (AI) into energy networks significantly advanced short-
term forecasting, particularly in smart meter applications. However, as distributed energy …

A continuous supply of electricity is necessary to maintain an acceptable standard of life,
and the power distribution system's overhead line components play a crucial role in this …

The integration of AI and ML in energy forecasting is pivotal for modern energy
management. Federated Learning (FL) stands out by enhancing data privacy and …

Federated Learning (FL) in load forecasting improves predictive accuracy by leveraging
data from distributed load networks while preserving data privacy. However, the …

Energy networks face challenges in managing and securing the vast data generated by
smart grids. Federated Learning (FL) offers a cost-effective, privacy-aware solution for model …

Accurate short-term load forecasting (STLF) is essential for the efficient and reliable
operation of power systems, enabling effective scheduling and integration of renewable …