DER Forecast Using Privacy-Preserving Federated Learning

With the 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 article, a distributed machine learning approach, federated learning (FL), is proposed to carry out DER forecasting using a network of Internet of Things (IoT) nodes, each of which transmits a model of the consumption and generation patterns without revealing consumer data. We consider a simulation study that includes 1000 DERs and show that our method leads to an accurate prediction of preserving 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 data set to demonstrate the validity of the proposed approach on real data.