Federated Shift-Invariant Dictionary Learning Enabled Distributed User Profiling

Transitioning to a low-/zero-carbon energy ecosystem requires a thorough and accurate understanding of how energy is consumed on the demand side. To achieve this goal, user profiling has become a crucial data analytical tool to understand consumers' energy activities and characterize the patterns/trends of different consumers. Though useful, user profiling remains a challenge due to the high irregularity and volatility inherited in consumer energy activities. Moreover, traditional user profiling requires time-series load consumption data to be collected and processed in a centralized server, which increases processing budgets and privacy leakage risks. To address these challenges, we propose a federated shift-invariant dictionary learning clustering approach to enable distributed and computationally efficient user profiling. The proposed approach elucidates the characteristics of each user by decomposing its time-series load consumption data into a linear combination of electricity consumption patterns. Furthermore, with the aid of a federated learning framework, the proposed approach allows most user profiling steps to be completed locally. Simulation studies based on real-world dataset show that compared with conventional federated clustering methods, the proposed approach is capable of better revealing the patterns behind the load series data, which improves the accuracy and effectiveness of user profiling.