  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.
