论文标题
使用边缘计算和联合学习的电气负载预测
Electrical Load Forecasting Using Edge Computing and Federated Learning
论文作者
论文摘要
在智能电网中，大量的消费数据用于训练深度学习模型，以进行负载监控和需求响应等应用。但是，这些应用程序引起了人们对安全性的担忧，并具有很高的准确性要求。一方面，所使用的数据对隐私敏感。例如，由智能电表在消费者家中收集的细粒度数据可能会揭示有关设备的信息，从而揭示消费者在家中的行为。另一方面，深度学习模型需要大数据量，并需要足够的培训。在本文中，我们评估了边缘计算和联合学习的使用，这是一种分散的机器学习方案，允许增加用于训练深度学习模型的数据的数量和多样性，而不会损害隐私。据我们所知，本文首次将联邦学习用于家庭负载预测并取得了令人鼓舞的结果。使用来自美国德克萨斯州的200栋数据的数据进行了tensorflow进行了仿真。
In the smart grid, huge amounts of consumption data are used to train deep learning models for applications such as load monitoring and demand response. However, these applications raise concerns regarding security and have high accuracy requirements. In one hand, the data used is privacy-sensitive. For instance, the fine-grained data collected by a smart meter at a consumer's home may reveal information on the appliances and thus the consumer's behaviour at home. On the other hand, the deep learning models require big data volumes with enough variety and to be trained adequately. In this paper, we evaluate the use of Edge computing and federated learning, a decentralized machine learning scheme that allows to increase the volume and diversity of data used to train the deep learning models without compromising privacy. This paper reports, to the best of our knowledge, the first use of federated learning for household load forecasting and achieves promising results. The simulations were done using Tensorflow Federated on the data from 200 houses from Texas, USA.
