Privacy-preserving household load forecasting based on non-intrusive load monitoring: A federated deep learning approach.

ABSTRACT: Load forecasting is very essential in the analysis and grid planning of power systems. For this reason, we first propose a household load forecasting method based on federated deep learning and non-intrusive load monitoring (NILM). As far as we know, this is the first research on federated learning (FL) in household load forecasting based on NILM. In this method, the integrated power is decomposed into individual device power by non-intrusive load monitoring, and the power of individual appliances is predicted separately using a federated deep learning model. Finally, the predicted power values of individual appliances are aggregated to form the total power prediction. Specifically, by separately predicting the electrical equipment to obtain the predicted power, it avoids the error caused

SUBMITTER: Zhou X 

PROVIDER: S-EPMC9455055 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature