Electrical Load Forecasting Using Edge Computing and Federated Learning

TL;DR

This paper explores the application of federated learning combined with edge computing for household load forecasting, enhancing data privacy and model accuracy in smart grid systems.

Contribution

It is the first to evaluate federated learning for household load forecasting, demonstrating its potential in privacy-preserving, decentralized energy demand prediction.

Findings

Achieved promising load forecasting accuracy using federated learning.

Utilized data from 200 houses in Texas for simulation.

Showed federated learning can improve privacy without sacrificing model performance.

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.