LV grid state estimation using local flexible assets: a federated learning approach
conference proceedings
To tackle the current needs of power operations in distributed context the new digital means are required to be able to exchange information between the energy market players in safe fashion. This paper tackles the solution allowing efficient exchange of sensitive information between the local assets and centralized powerful analysis of the state of the assets. We show how to apply Federated Learning distributed technique, including platform and local predictive models which are used in learning the state of the whole grid centrally without sharing sensitive data in huge amounts.
TNO Identifier
992319
ISBN
978-1-83953-855-1
Source
27th International Conference on Electricity Distribution (CIRED 2023)
Publisher
IET
Files
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