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In this proposal paper we highlight the need for privacy preserving energydemand forecasting to allay a major concern consumers have about smart meterinstallations. High resolution smart meter data can expose many private aspectsof a consumer's household such as occupancy, habits and individual applianceusage. Yet smart metering infrastructure has the potential to vastly reducecarbon emissions from the energy sector through improved operatingefficiencies. We propose the application of a distributed machine learningsetting known as federated learning for energy demand forecasting at variousscales to make load prediction possible whilst retaining the privacy ofconsumers' raw energy consumption data.

Privacy Preserving Demand Forecasting to Encourage Consumer Acceptance of Smart Energy Meters