FOR IMMEDIATE RELEASE | September 25, 2025 – Carnegie, PA
Department of Energy SBIR Phase I project will support development of GRIDview, an AI-driven platform for visualizing uncertainty and strengthening risk-informed electric grid decision-making.
VISIMO Wins DOE SBIR Phase I Award to Develop AI-Driven Grid Risk Visualization Platform
CARNEGIE, PA. – VISIMO, a Carnegie-based artificial intelligence and software engineering company, has been awarded a Department of Energy Small Business Innovation Research (SBIR) Phase I project to develop GRIDview, an AI-driven visualization platform designed to help electric grid operators better understand uncertainty, evaluate risk, and make more informed operational decisions.
The modern electric grid is becoming increasingly complex as renewable energy sources, distributed energy resources, shifting demand patterns, and extreme weather events introduce greater variability across both supply and demand. Traditional grid management tools often rely on deterministic forecasts and fixed operating assumptions, limiting an operator’s ability to evaluate the range of possible system conditions before risks escalate.
GRIDview is designed to address that challenge by combining probabilistic forecasting, machine learning, and intuitive visualization techniques into a risk-aware decision-support platform. Rather than presenting operators with a single-point forecast, GRIDview will help quantify and visualize uncertainty so users can assess confidence intervals, explore possible future scenarios, and evaluate the likelihood of critical events.
“Electric grid reliability depends on the ability to make decisions under uncertainty,” said James Julius, Founder and CEO of VISIMO. “GRIDview reflects the kind of applied AI work VISIMO is built for: turning complex data, uncertain conditions, and advanced models into tools that help people make better decisions when the stakes are high.”
Advancing Risk-Informed Grid Decision-Making
GRIDview will focus on improving how uncertainty is modeled, communicated, and used in electric grid operations. The project will explore probabilistic forecasting methods designed to estimate uncertainty in load forecasting, renewable generation variability, and other grid-relevant conditions.
The platform will also emphasize visualization. Dynamic risk maps, forecast confidence displays, scenario-based planning tools, and sensitivity analysis dashboards will be evaluated as ways to make uncertainty more understandable and actionable for operators. The goal is not simply to produce more advanced models, but to present model outputs in a way that improves situational awareness and supports transparent decision-making.
By helping operators understand a range of possible outcomes rather than relying on fixed predictions, GRIDview is intended to support more adaptive responses to grid instability, extreme weather, renewable variability, and other sources of operational risk.
Supporting Grid Reliability, Resilience, and Energy Modernization
A more resilient electric grid has broad public benefits. Improved risk visualization can help utilities and grid operators identify vulnerabilities earlier, optimize reserve planning, reduce emergency interventions, and better prepare for high-impact scenarios. These capabilities are increasingly important as the nation modernizes energy infrastructure and integrates more renewable and distributed energy resources.
GRIDview also aligns with broader public priorities around reliability, affordability, sustainability, and energy security. By improving how uncertainty is quantified and communicated, the platform can support more confident planning, better operational decisions, and a more resilient energy system for communities, businesses, and critical infrastructure.
Phase I Technical Objectives
During Phase I, VISIMO will establish the technical feasibility of GRIDview through a structured research and prototyping effort. The work will include development of a probabilistic forecasting engine, design of an uncertainty visualization framework, validation against deterministic forecasting baselines, simulated real-time data integration, and preparation of a Phase II pilot deployment roadmap.
The Phase I effort is intended to lay the foundation for future pilot deployments with utilities, grid operators, and other stakeholders. If successful, later phases would focus on refining the technology, integrating real-world data streams, and validating GRIDview in operational environments.
“GRIDview is about helping people trust what they are seeing in complex systems,” said Constantine “Dino” Mintas, Principal Investigator and Chief Data Scientist at VISIMO. “By pairing uncertainty-aware AI with clear visual decision support, we can give operators a better understanding of risk before it becomes an operational crisis.”
About VISIMO
VISIMO is a Carnegie, Pennsylvania-based AI and software engineering company founded in 2015. VISIMO builds applied machine learning, data science, and mission software solutions for federal agencies and commercial partners, with a focus on rapid prototyping, defensible AI, and decision-support systems. VISIMO specializes in transforming complex data and technical uncertainty into practical tools that support better decisions in high-consequence environments.
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Government Disclaimer:
This material is based upon work supported by the U.S. Department of Energy. Any opinions, findings, conclusions, or recommendations expressed in this material are those of VISIMO and do not necessarily reflect the views of the Department of Energy.