  In this proposal paper we highlight the need for privacy preserving energy
demand forecasting to allay a major concern consumers have about smart meter
installations. High resolution smart meter data can expose many private aspects
of a consumer's household such as occupancy, habits and individual appliance
usage. Yet smart metering infrastructure has the potential to vastly reduce
carbon emissions from the energy sector through improved operating
efficiencies. We propose the application of a distributed machine learning
setting known as federated learning for energy demand forecasting at various
scales to make load prediction possible whilst retaining the privacy of
consumers' raw energy consumption data.
