NextNRG Engineers Publish Peer-Reviewed Research Validating AI-Driven Grid Platform

Rhea-AI Summary

NextNRG (NASDAQ:NXXT) announced that its engineering team published multiple peer-reviewed research papers in 2025 validating technical components of its AI-driven grid intelligence platform. The publications support core modules of the company's Utility Operating System, including forecasting engines, grid security analytics, and microgrid control software, and align with expanded deployments across healthcare, transportation, utility, and enterprise energy infrastructure.

The research covers improved short-term demand forecasting, detection of false data injection attacks, inverter fault-detection comparisons, and hybrid AI monitoring frameworks, and is published in Springer Nature conference proceedings and other technical forums.

Market Reality Check

$1.18 Last Close

Volume Volume 2,468,838 is 1.07x the 20-day average of 2,307,070 normal

Technical Price $1.34 is trading below the $2.20 200-day moving average

Peers on Argus 1 Down

NXXT was down 7.59% pre-news while key renewable peers were mixed: ELLO +3.88%, NRGV +4.94%, SUUN +4.86%, WAVE +2.03%, VGAS -7.01%. Momentum scans flagged only BNRG at -7.52% without news, suggesting NXXT’s move was more stock-specific than a broad sector rotation.

Historical Context

Date	Event	Sentiment	Move	Catalyst
Dec 31	Shareholder rewards	Positive	+2.1%	Announced EzFill fuel-discount token rewards program for shareholders.
Dec 29	Battery storage MOU	Positive	-8.9%	Signed MOU with A123 Systems for U.S.-made battery storage projects.
Dec 23	Outage commentary	Positive	+12.2%	Promoted AI Utility Operating System after major San Francisco outage.
Dec 19	Operational update	Positive	+14.8%	Highlighted record fuel volumes and strong Q4 2025 delivery trends.
Dec 16	Media feature	Positive	-10.6%	Forbes feature on technology for energy-intensive cold storage facilities.

Pattern Detected

Recent news has often been operationally positive, with three instances of aligned price gains but two notable selloffs on seemingly favorable announcements, indicating inconsistent news-to-price translation.

Recent Company History

Over the past few weeks, NXXT has issued a series of operational and strategic updates, from a shareholder rewards program on Dec 31, 2025 to a battery storage MOU and grid-resilience marketing tied to a San Francisco outage. Earlier, it highlighted record Q4 fuel volumes and Forbes coverage of its technology. Price reactions have alternated between sharp gains and selloffs, showing that even constructive developments do not consistently translate into positive short-term moves.

Market Pulse Summary

The stock moved -7.5% in the session following this news. A negative reaction despite technically validating AI research would fit prior instances where favorable news still preceded declines, such as selloffs after the Forbes feature and storage MOU. The stock traded about 63% below its 52-week high coming into this release and has a history of large swings around news and financing events. Insider net selling and prior dilution-related filings could also weigh on sentiment during drawdowns.

Key Terms

microgrid technical

"expansion of AI-enabled microgrid and grid-management deployments across healthcare"

A microgrid is a small, local electricity system that combines power sources (like solar panels, small generators) and storage (batteries) with controls so it can run either connected to the main utility grid or on its own during outages. For investors, microgrids matter because they reduce outage risk, can lower energy costs, enable new revenue streams (selling excess power or grid services), and reflect growing demand for resilient, decentralized energy infrastructure.

false data injection attacks technical

"Detection and classification of false data injection attacks in smart grids"

False data injection attacks are deliberate efforts to feed fake or altered information into a company’s data systems, sensors, or market feeds so automated systems and people make wrong decisions. For investors this matters because corrupted data can distort financial reports, trading algorithms, supply-chain signals or safety metrics—like swapping a car’s speedometer reading to cause bad reactions—leading to sudden price moves, unexpected losses, or hidden risks.

inverter fault detection technical

"Comparative analysis of inverter fault-detection methods for grid-connected solar"

Inverter fault detection is the system that spots malfunctions in power inverters — devices that convert stored or generated energy into usable electricity — and alerts operators so they can fix problems quickly. Like a smoke detector for electrical equipment, it matters to investors because fast, accurate detection reduces downtime, repair costs and safety risks, preserving revenue and the long‑term value of energy assets.

photovoltaic technical

"smart grids and solar photovoltaic systems, strengthening cyber resilience"

Photovoltaic describes the technology that converts sunlight directly into electricity using panels made of semiconductor materials; think of it like leaves turning sunlight into usable energy for a plant, but producing power for homes, factories and the grid. It matters to investors because photovoltaic systems represent assets, revenue sources, cost structures and regulatory exposure for energy and manufacturing companies, and their adoption rates and efficiency gains affect long-term profitability and market demand.

deep-learning technical

"physics-aware deep-learning models for solar forecasting, and adaptive control"

Deep learning is a type of artificial intelligence that uses layered computer models to learn patterns from large amounts of data, similar to how a person learns to recognize faces by seeing many examples. For investors, it matters because companies that successfully apply deep learning can automate tasks, improve products or services, cut costs, and uncover new revenue opportunities — but it also brings risks from mistakes, high development costs, and evolving regulations.

machine-learning technical

"specializing in AI- and machine-learning-based forecasting, explained, the company’s"

Machine-learning is a type of computer software that improves its performance by finding patterns in data rather than following fixed rules. Think of it like a digital apprentice that gets better at tasks — such as spotting customer trends, predicting demand, or automating routine work — the more examples it sees. Investors care because machine-learning can boost revenue, cut costs, create competitive advantages, and introduce new risks tied to data quality and model errors.

AI-generated analysis. Not financial advice.

Miami, FL, Jan. 05, 2026 (GLOBE NEWSWIRE) -- NextNRG, Inc. (NASDAQ:NXXT), a pioneer in AI-driven energy innovation transforming how energy is produced, managed, and delivered, today 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.

Key research areas include:

• Improved short-term electricity demand forecasting using machine-learning models to support more efficient dispatch decisions and cost control in smart grids and microgrids
• Detection and classification of false data injection attacks in smart grids and solar photovoltaic systems, strengthening cyber resilience and operational integrity
• Comparative analysis of inverter fault-detection methods for grid-connected solar photovoltaic systems, improving asset reliability and reducing downtime
• Hybrid AI frameworks that enhance system monitoring, fault detection, and anomaly classification in renewable and microgrid environments

These peer-reviewed publications validate the scientific and engineering basis behind NextNRG’s commercial systems and help reduce execution risk for customers, infrastructure partners, and capital providers. The research emphasizes deployable intelligence and operational relevance, aligning directly with methodologies embedded in the company’s fielded technologies.

NextNRG integrates independently validated research into its product development and disclosure practices, using peer review and applied analysis to support technical claims with reproducible results and rigorous evaluation rather than marketing assertions.

In addition to the published work, NextNRG’s engineering team continues to advance research in photovoltaic-battery microgrids across multiple U.S. states, physics-aware deep-learning models for solar forecasting, and adaptive control strategies for resilient microgrid dispatch. These efforts directly support the company’s long-term product roadmap and expansion into large-scale infrastructure deployments.

“Customers and investors expect disciplined execution,” said 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.”

About NextNRG, Inc.

NextNRG Inc. (NextNRG) is Powering What's Next by integrating artificial intelligence (AI) and machine learning (ML) into utility infrastructure, battery storage, wireless EV in-motion charging, renewable energy and mobile fuel delivery, to create a unified platform for modern energy management.

At the core of its strategy is the Next Utility Operating System®, which uses AI to optimize both new and existing infrastructure across microgrids, utilities, and fleet operations. NextNRG's smart microgrids serve commercial, healthcare, educational, tribal, and government sites delivering cost savings, reliability, and decarbonization. The company also operates one of the nation's largest on-demand fueling fleets and is advancing wireless charging to support fleet electrification.

To learn more, visit www.nextnrg.com.

Forward-Looking Statements

This press release includes forward-looking statements within the meaning of the Private Securities Litigation Reform Act of 1995. Any statement describing NextNRG's goals, expectations, financial or other projections, intentions, or beliefs is a forward-looking statement and should be considered an at-risk statement. Words such as "expect," "intends," "will," and similar expressions are intended to identify forward-looking statements. Such statements are subject to certain risks and uncertainties, including, but not limited to, those related to NextNRG's business and macroeconomic and geopolitical events. These and other risks are described in NextNRG's filings with the Securities and Exchange Commission from time to time. NextNRG's forward-looking statements involve assumptions that, if they never materialize or prove correct, could cause its results to differ materially from those expressed or implied by such forward-looking statements. Although NextNRG's forward-looking statements reflect the good faith judgment of its management, these statements are based only on facts and factors currently known by NextNRG. Except as required by law, NextNRG undertakes no obligation to update any forward-looking statements for any reason. As a result, you are cautioned not to rely on these forward-looking statements.

Investor Relations Contact
NextNRG, Inc.
Sharon Cohen
SCohen@nextnrg.com

FAQ

What did NextNRG (NXXT) announce on January 5, 2026 about its AI-driven grid platform?

NextNRG said its engineers published multiple peer-reviewed papers in 2025 validating components of its AI-driven grid intelligence platform and Utility Operating System.

Which technical areas do NextNRG's 2025 publications cover that could affect NXXT deployments?

The papers address short-term demand forecasting, detection of false data injection attacks, inverter fault detection, and hybrid AI monitoring frameworks.

How do NextNRG's peer-reviewed studies relate to its commercial deployments (NXXT)?

The company says the research underpins forecasting, grid security, and microgrid control modules used for repeatable commercial deployments across critical infrastructure sectors.

Where were NextNRG engineers' papers published and when (NXXT)?

NextNRG engineers published in Springer Nature conference proceedings and other technical forums during 2025.

What operational benefits does NextNRG (NXXT) claim the validated research provides to customers?

NextNRG says the research improves forecasting accuracy, strengthens cyber resilience, and enhances operational reliability for infrastructure-scale deployments.