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The advent of smart meters has enabled pervasive collection of energyconsumption data for training short-term load forecasting (STLF) models. Inresponse to privacy concerns, federated learning (FL) has been proposed as aprivacy-preserving approach for training, but the quality of trained modelsdegrades as client data becomes heterogeneous. In this paper we alleviate thisdrawback using personalization layers, wherein certain layers of an STLF modelin an FL framework are trained exclusively on the clients' own data. To thatend, we propose a personalized FL algorithm (PL-FL) enabling FL to handlepersonalization layers. The PL-FL algorithm is implemented by using the ArgonnePrivacy-Preserving Federated Learning package. We test the forecast performanceof models trained on the NREL ComStock dataset, which contains heterogeneousenergy consumption data of multiple commercial buildings. Superior performanceof models trained with PL-FL demonstrates that personalization layers enableclassical FL algorithms to handle clients with heterogeneous data.

Federated Short-Term Load Forecasting with Personalization Layers for Heterogeneous Clients