  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). For
all 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 by the strong time dependence in the power signal of
a single device. And in the federated deep learning prediction model, the
household owners with the power data share the parameters of the local model
instead of the local power data, guaranteeing the privacy of the household user
data. The case results demonstrate that the proposed approach provides a better
prediction effect than the traditional methodology that directly predicts the
aggregated signal as a whole. In addition, experiments in various federated
learning environments are designed and implemented to validate the validity of
this methodology.
