Miami, FL, Jan. 5, 2026 (GLOBE NEWSWIRE) — NextNRG, Inc. (NASDAQ: NXXT), a leader in AI-driven energy solutions, announced today that its engineering team has published multiple peer-reviewed research papers throughout 2025. These publications validate the technical foundations of the company’s AI-driven grid intelligence platform, supporting its commercial deployment at scale across various sectors including healthcare, transportation, and utility infrastructures.
The timing of these publications aligns with NextNRG’s strategic expansion of AI-enabled microgrid and grid management systems, where forecasting accuracy, cyber resilience, and operational reliability are vital. The peer-reviewed research substantiates key elements of NextNRG’s Utility Operating System, encompassing forecasting engines, grid security analytics, and microgrid control software. Dr. Hugo Riggs, a senior engineer specializing in AI and machine learning at NextNRG, emphasized that the research is “built to translate directly into operational performance,” ensuring that methods are robust, repeatable, and scalable in real-world settings.
Research authored by NextNRG engineers, including Dr. Shahid Tufail and Dr. Riggs, has been published in notable technical forums such as Springer Nature conference proceedings. The studies address pressing challenges facing modern power systems, including demand forecasting accuracy, grid security, inverter fault detection, and renewable energy integration. Dr. Tufail, the lead author on several of these studies, stated that the consistent goal is to enhance forecasting accuracy, system reliability, and grid resilience across varying operational environments—critical factors for achieving widespread commercial adoption.
Key areas of focus in this research include improved short-term electricity demand forecasting using machine learning models, which aim to enhance dispatch decisions and cost efficiency in smart grids and microgrids. Additionally, the research explores the detection and classification of false data injection attacks in smart grids and solar photovoltaic systems to bolster cyber resilience and operational integrity. Comparative analyses of inverter fault-detection methodologies for grid-connected solar systems are also featured, aimed at improving asset reliability and reducing downtime. Furthermore, hybrid AI frameworks are discussed that enhance monitoring, fault detection, and anomaly classification in renewable and microgrid environments.
These peer-reviewed publications affirm the scientific and engineering basis for NextNRG’s commercial systems, mitigating execution risk for customers, infrastructure partners, and capital providers. The research promotes the deployment of practical intelligence and operational relevance, aligning with methodologies embedded in the company’s fielded technologies.
NextNRG integrates independently validated research into its product development, utilizing peer review and applied analysis to substantiate technical claims with reproducible results and rigorous evaluations, rather than marketing assertions. Alongside the published work, the engineering team is advancing research in photovoltaic-battery microgrids across various U.S. states, focusing on physics-aware deep-learning models for solar forecasting and adaptive control strategies for resilient microgrid dispatch. These ongoing efforts support NextNRG’s long-term product roadmap and its ambitions for large-scale infrastructure deployments.
“Customers and investors expect disciplined execution,” stated Michael D. Farkas, Executive Chairman and Chief Executive Officer of NextNRG. “This body of peer-reviewed research demonstrates that our platform is built on validated science and engineered for real-world performance.”
NextNRG, Inc. is engaged in integrating artificial intelligence and machine learning into utility infrastructure, battery storage, wireless electric vehicle charging, renewable energy, and mobile fuel delivery. The company’s core strategy revolves around the Next Utility Operating System®, designed to optimize both new and existing infrastructure across microgrids, utilities, and fleet operations. NextNRG’s smart microgrids serve a variety of sectors, including commercial, healthcare, educational, tribal, and government sites, delivering significant cost savings, reliability, and decarbonization benefits. Additionally, the company operates one of the nation’s largest on-demand fueling fleets and is pioneering wireless charging technologies to support fleet electrification.
For more information, visit www.nextnrg.com.
This press release contains forward-looking statements as defined by the Private Securities Litigation Reform Act of 1995. Any statements regarding NextNRG’s goals, expectations, projections, or intentions are forward-looking and should be regarded as at-risk statements. Words such as “expect,” “intends,” “will,” and similar expressions are intended to identify forward-looking statements. These statements are subject to risks and uncertainties, including those related to NextNRG’s business and macroeconomic conditions. For a detailed discussion of these risks, please refer to NextNRG’s filings with the Securities and Exchange Commission. NextNRG assumes no obligation to update any forward-looking statements unless required by law.
For investor relations inquiries, please contact: Sharon Cohen, NextNRG, Inc., email: [email protected].
See also
Bellini College Launches at USF, Pioneering AI and Cybersecurity Education for 5,000 Students
MIT Jameel Clinic Launches AI Study to Predict Breast Cancer Risk in Japan with Mirai Tool
CeriBell Achieves FDA Breakthrough Status for AI-Powered LVO Stroke Detection Solution
Reinforcement Learning Transforms Resource Management in Communication Networks
