DER Forecast using Privacy Preserving Federated Learning

TL;DR

This paper presents a federated learning approach for DER forecasting that preserves consumer privacy while maintaining high prediction accuracy, validated through simulations on synthetic and real datasets.

Contribution

It introduces a privacy-preserving federated learning method for DER prediction using IoT networks, addressing privacy concerns in distributed energy forecasting.

Findings

Accurate DER forecasting with privacy preservation demonstrated in simulations.

Method performs well on real-world Pecan Street dataset.

Grid performance metrics show satisfactory results.

Abstract

With increasing penetration of Distributed Energy Resources (DERs) in grid edge including renewable generation, flexible loads, and storage, accurate prediction of distributed generation and consumption at the consumer level becomes important. However, DER prediction based on the transmission of customer level data, either repeatedly or in large amounts, is not feasible due to privacy concerns. In this paper, a distributed machine learning approach, Federated Learning, is proposed to carry out DER forecasting using a network of IoT nodes, each of which transmits a model of the consumption and generation patterns without revealing consumer data. We consider a simulation study which includes 1000 DERs, and show that our method leads to an accurate prediction of preserve consumer privacy, while still leading to an accurate forecast. We also evaluate grid-specific performance metrics such as load swings and load curtailment and show that our FL algorithm leads to satisfactory performance. Simulations are also performed on the Pecan street dataset to demonstrate the validity of the proposed approach on real data.