  Non-intrusive load monitoring (NILM) aims at decomposing the total reading of
the household power consumption into appliance-wise ones, which is beneficial
for consumer behavior analysis as well as energy conservation. NILM based on
deep learning has been a focus of research. To train a better neural network,
it is necessary for the network to be fed with massive data containing various
appliances and reflecting consumer behavior habits. Therefore, data cooperation
among utilities and DNOs (distributed network operators) who own the NILM data
has been increasingly significant. During the cooperation, however, risks of
consumer privacy leakage and losses of data control rights arise. To deal with
the problems above, a framework to improve the performance of NILM with
federated learning (FL) has been set up. In the framework, model weights
instead of the local data are shared among utilities. The global model is
generated by weighted averaging the locally-trained model weights to gather the
locally-trained model information. Optimal model selection help choose the
model which adapts to the data from different domains best. Experiments show
that this proposal improves the performance of local NILM runners. The
performance of this framework is close to that of the centrally-trained model
obtained by the convergent data without privacy protection.
