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cs.LG2024
Improving accuracy and convergence of federated learning edge computing methods for generalized DER forecasting applications in power grid
Vineet Jagadeesan Nair, Lucas Pereira
This proposal aims to develop more accurate federated learning (FL) methods with faster convergence properties and lower communication requirements, specifically for forecasting di…
cs.LG2024★ 1 cited
Enhanced physics-informed neural networks (PINNs) for high-order power grid dynamics
Vineet Jagadeesan Nair
We develop improved physics-informed neural networks (PINNs) for high-order and high-dimensional power system models described by nonlinear ordinary differential equations. We prop…
cs.LG2024
Federated Learning Forecasting for Strengthening Grid Reliability and Enabling Markets for Resilience
Lucas Pereira, Vineet Jagadeesan Nair, Bruno Dias +2
We propose a comprehensive approach to increase the reliability and resilience of future power grids rich in distributed energy resources. Our distributed scheme combines federated…