论文标题
隐私保留需求预测，以鼓励消费者接受智能能量表
Privacy Preserving Demand Forecasting to Encourage Consumer Acceptance of Smart Energy Meters
论文作者
论文摘要
在这份提案文件中，我们强调了对保留能源需求预测的必要性，以减轻消费者对智能电表安装的主要关注。高分辨率智能电表数据可以暴露消费者家庭的许多私人方面，例如占用率，习惯和个人设备使用情况。然而，智能计量基础设施有可能通过提高的运营效率大大减少能源部门的碳排放。我们建议将分布式机器学习设置应用于联合学习，以在各种尺度上预测能源需求，以使负载预测成为可能，同时保留消费者原始能源消耗数据的隐私。
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.
