Improving Energy Efficiency through Collaborative Edge Intelligence in Distributed Smart Meter Networks

Smart grids require fine-grained data to manage demand-side flexibility and integrate renewable energy sources efficiently. However, the ubiquitous smart meters currently lack the computational power to perform complex on-device analytics due to severe hardware limitations in memory and processing speed. This technology profile introduces a federated split learning framework designed specifically for resource-constrained environments to enable edge intelligence. By combining the strengths of federated learning for privacy-preserving collaboration and split learning for computational offloading, the framework allows smart meters to participate in advanced deep learning training. This innovation bypasses the need for massive data uploads to central servers, thereby reducing communication overhead and enhancing consumer privacy. The proposed system enables precise on-device load forecasting even on microcontrollers with minimal memory capacity. Investors and policy makers can view this as a low-cost pathway to intellectualizing the grid’s edge without replacing existing hardware. This solution fosters a more autonomous energy market where consumers manage resources with high-fidelity predictions. Implementation of this distributed AI approach effectively bridges the gap between theoretical machine learning and practical grid deployment. The system offers a scalable model that maintains high forecasting accuracy while reducing the energy and time costs associated with traditional centralized data management. By turning sensor nodes into intelligent agents, the grid becomes more resilient and capable of managing decentralized power resources.