  To reduce negative environmental impacts, power stations and energy grids
need to optimize the resources required for power production. Thus, predicting
the energy consumption of clients is becoming an important part of every energy
management system. Energy usage information collected by the clients' smart
homes can be used to train a deep neural network to predict the future energy
demand. Collecting data from a large number of distributed clients for
centralized model training is expensive in terms of communication resources. To
take advantage of distributed data in edge systems, centralized training can be
replaced by federated learning where each client only needs to upload model
updates produced by training on its local data. These model updates are
aggregated into a single global model by the server. But since different
clients can have different attributes, model updates can have diverse weights
and as a result, it can take a long time for the aggregated global model to
converge. To speed up the convergence process, we can apply clustering to group
clients based on their properties and aggregate model updates from the same
cluster together to produce a cluster specific global model. In this paper, we
propose a recurrent neural network based energy demand predictor, trained with
federated learning on clustered clients to take advantage of distributed data
and speed up the convergence process.
