  Privacy is important when dealing with sensitive personal information in
machine learning models, which require large data sets for training. In the
energy field, access to household prosumer energy data is crucial for energy
predictions to support energy grid management and large-scale adoption of
renewables however citizens are often hesitant to grant access to cloud-based
machine learning models. Federated learning has been proposed as a solution to
privacy challenges however report issues in generating the global prediction
model due to data heterogeneity, variations in generation patterns, and the
high number of parameters leading to even lower prediction accuracy. This paper
addresses these challenges by introducing FedWOA a novel federated learning
model that employs the Whale Optimization Algorithm to aggregate global
prediction models from the weights of local LTSM neural network models trained
on prosumer energy data. The proposed solution identifies the optimal vector of
weights in the search spaces of the local models to construct the global shared
model and then is subsequently transmitted to the local nodes to improve the
prediction quality at the prosumer site while for handling non-IID data K-Means
was used for clustering prosumers with similar scale of energy data. The
evaluation results on prosumers energy data have shown that FedWOA can
effectively enhance the accuracy of energy prediction models accuracy by 25%
for MSE and 16% for MAE compared to FedAVG while demonstrating good convergence
and reduced loss.
