A Novel Federated Big Data Analytics Framework Using Latent Energy Interaction Modeling and Rule-Embedded Anomaly Intelligence for Smart Grids

Abstract

The growing use of smart grid technologies has led to large amounts of distributed energy data, and requires intelligent, scalable, and privacy-preserving analytics in order to manage loads efficiently. This paper presents a new Federated Adaptive Load Cognition and Optimization Network (FALCON) to monitor, predict, and identify anomalies in the smart grid aspects in real-time. The proposed framework entails a Latent Energy Interaction Modeling (LEIM) mechanism, which refers to the interaction of latent dependencies between substations. The framework exploits a federated learning paradigm, in which each smart meter or substation is an autonomous client, and collectively trains a global model without exchanging raw data, thus preserving data privacy and minimizing communication overheads. It uses a Cross-Attention Interaction Encoder to learn the dynamics of relationships between distributed energy nodes, and then a time-dependent load change is modelled using a temporal transformer. In order to improve the level of system reliability, an Energy Rule-Embedded Neural Validator (ERENV) is designed to detect anomalies by embedding prediction deviation, temporal inconsistency, rule, and operation violations into a single scoring mechanism. Experiments that have been carried out on actual Pecan Street Data port prove that the proposed structure is better at load forecasting accuracy 99% than the existing technique and ensures privacy of data. The proposed system is quite applicable to the application of the next-generation intelligent and decentralized smart grid.