부산대학교 도서관

The prediction of the net electricity demand is crucial to the management and optimization of transactive energy communities. Such prediction usually relies on net-demand information, but each building can have additional information, such as separated generation and demand profiles, weather, or occupancy data. Such information is not only relevant for the net-demand prediction of each building, but also to other buildings with the same type of use. However, buildings avoid sharing such information due to privacy concerns. This paper proposes a novel federated learning framework for predicting building temporal net-demand in transactive energy communities. The proposed approach leverages centralized oversight of a central agent (aggregator) to inform distributed collaboration among each client (buildings), which are willing to collaborate to improve their prediction accuracy. The proposed approach was tested using a dataset collected from several buildings from a University campus (from the University of Coimbra in Portugal), predicting the electricity demand, and then using the local generation data to evaluate the net-demand, in the community of buildings.