Communication-Efficient Design of Learning System for Energy Demand Forecasting of Electrical Vehicles

TL;DR

This paper introduces a communication-efficient federated learning model using transformer architectures for energy demand forecasting of electric vehicles, achieving comparable accuracy with reduced data transmission across dispersed data sources.

Contribution

The paper presents a novel federated learning approach with transformers tailored for geographically dispersed EV energy data, reducing communication costs while maintaining prediction accuracy.

Findings

Parity in prediction performance with reduced communication overhead

Effective generalization across different time series datasets

Demonstrated flexibility beyond energy demand forecasting

Abstract

Machine learning (ML) applications to time series energy utilization forecasting problems are a challenging assignment due to a variety of factors. Chief among these is the non-homogeneity of the energy utilization datasets and the geographical dispersion of energy consumers. Furthermore, these ML models require vast amounts of training data and communications overhead in order to develop an effective model. In this paper, we propose a communication-efficient time series forecasting model combining the most recent advancements in transformer architectures implemented across a geographically dispersed series of EV charging stations and an efficient variant of federated learning (FL) to enable distributed training. The time series prediction performance and communication overhead cost of our FL are compared against their counterpart models and shown to have parity in performance while consuming significantly lower data rates during training. Additionally, the comparison is made across EV charging as well as other time series datasets to demonstrate the flexibility of our proposed model in generalized time series prediction beyond energy demand. The source code for this work is available at https://github.com/XuJiacong/LoGTST_PSGF