  Load forecasting is an essential task performed within the energy industry to
help balance supply with demand and maintain a stable load on the electricity
grid. As supply transitions towards less reliable renewable energy generation,
smart meters will prove a vital component to facilitate these forecasting
tasks. However, smart meter adoption is low among privacy-conscious consumers
that fear intrusion upon their fine-grained consumption data. In this work we
propose and explore a federated learning (FL) based approach for training
forecasting models in a distributed, collaborative manner whilst retaining the
privacy of the underlying data. We compare two approaches: FL, and a clustered
variant, FL+HC against a non-private, centralised learning approach and a fully
private, localised learning approach. Within these approaches, we measure model
performance using RMSE and computational efficiency. In addition, we suggest
the FL strategies are followed by a personalisation step and show that model
performance can be improved by doing so. We show that FL+HC followed by
personalisation can achieve a $\sim$5\% improvement in model performance with a
$\sim$10x reduction in computation compared to localised learning. Finally we
provide advice on private aggregation of predictions for building a private
end-to-end load forecasting application.

Machine Learning;Computers and Society