NextNRG Publishes Multiple Research Papers Validating AI-Driven Grid Intelligence Platform

NextNRG announced that members of its engineering team have published multiple peer-reviewed research papers in 2025 that validate the technical foundations of the company's AI-driven grid intelligence platform and support its commercial deployment at scale. The publications coincide with NextNRG's expansion of AI-enabled microgrid and grid-management deployments across healthcare, transportation, utility, and enterprise energy infrastructure, where forecasting accuracy, cyber resilience, and operational reliability are critical to performance. The peer-reviewed research underpins key components of NextNRG's Utility Operating System, including forecasting engines, grid security analytics, and microgrid control software. These capabilities are designed for repeatable deployment across diverse operating environments and form the core of the company's commercial energy offerings. As Dr. Hugo Riggs, senior engineer at NextNRG specializing in AI- and machine-learning-based forecasting, explained, the company's research is "built to translate directly into operational performance," with peer review serving as a mechanism to ensure deployed methods are robust, repeatable, and scalable in real-world conditions. Research authored and co-authored by NextNRG engineers, including Dr. Shahid Tufail and Dr. Riggs, has been published in Springer Nature conference proceedings and other leading technical forums. The work addresses practical challenges facing modern power systems, including demand forecasting accuracy, grid security, inverter fault detection, and renewable integration. According to Dr. Tufail, lead author on several of the studies, the objective across these efforts is consistent improvement in forecasting accuracy, system reliability, and grid resilience across diverse operating environments-outcomes he notes are essential for commercial adoption at infrastructure scale.