Generalized Policy Learning for Smart Grids: FL TRPO Approach
Yunxiang Li Nicolas Mauricio Cuadrado Samuel Horváth Martin Takáč
The smart grid domain requires bolstering the capabilities of existing energy
management systems; Federated Learning (FL) aligns with this goal as it
demonstrates a remarkable ability to train models on heterogeneous datasets
while maintaining data privacy, making it suitable for smart grid applications,
which often involve disparate data distributions and interdependencies among
features that hinder the suitability of linear models. This paper introduces a
framework that combines FL with a Trust Region Policy Optimization (FL TRPO)
aiming to reduce energy-associated emissions and costs. Our approach reveals
latent interconnections and employs personalized encoding methods to capture
unique insights, understanding the relationships between features and optimal
strategies, allowing our model to generalize to previously unseen data.
Experimental results validate the robustness of our approach, affirming its
proficiency in effectively learning policy models for smart grid challenges.