Electrical Load Forecasting Using Edge Computing and Federated Learning

Abstract

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.

arXiv commentICC 2020-2020 IEEE International Conference on Communications (ICC)

References in corpus (3)

• Federated Learning for Mobile Keyboard Prediction
• Building Energy Load Forecasting using Deep Neural Networks
• A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art

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• A Federated Learning Framework for Smart Grids: Securing Power Traces in Collaborative Learning
• Empowering Prosumer Communities in Smart Grid with Wireless Communications and Federated Edge Learning
• Personalized Graph Federated Learning with Differential Privacy
• Secure short-term load forecasting for smart grids with transformer-based federated learning
• Electrical Load Forecasting over Multihop Smart Metering Networks with Federated Learning
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