Consumers profiling based federated learning approach for energy load forecasting

Energy load estimation is critical for the smooth functioning of several activities, such as reliable supply, reduced wastage, decision making and generation planning tasks. So far, deep learning and hybrid models have demonstrated great capabilities in capturing non-linear energy load variations patterns. However, these models are data-intensive, requiring a large amount of data to be centrally available for achieving the desired accuracy. Sharing electricity consumption profiles hampers users' privacy and is computationally very expensive in terms of communication cost. In this regard, the current work proposes a novel federated learning-based approach mitigating the shortcomings of benchmark approaches. The proposed approach combines time-series characteristics patterns and clustering with federated learning to achieve desired privacy, cost benefits and accuracy. To validate these benefits, the performance comparison of the proposed approach is performed with local learning and benchmark prediction approaches. The experimental results on a real-world dataset of residential household buildings demonstrate that the proposed approach performs as good as the local learning approach while ensuring users' data privacy and reducing communication costs.