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.

翻译：高分辨率智能计量数据可以暴露消费者家庭的许多私人方面,如占用、习惯和个人用具的使用。然而,智能计量基础设施可以通过提高运营效率,大幅减少能源部门的碳排放。我们提议应用一个分布式的机器学习环境,称为在不同规模的能源需求预测联合学习,使负荷预测成为可能,同时保留消费者原始能源消费数据的隐私。