Our Technology Philosophy
Why GridSeer Is Different
Conventional energy management tools rely on static models that can’t keep up with the volatility of modern power systems. GridSeer was developed to close that gap—bringing adaptive, research-based intelligence into operational practice. Our platform enables a shift from reactive to predictive energy management by embedding risk into every decision.
How the Technology Works
GridSeer is a software and analytics platform that combines weather-based risk, intrinsic asset risk—in the form of a digital twin—and historical data to feed a forecast generator which is made up of an AI engine, a prediction model and an error tracing system. In contrast to conventional modeling approaches which simply predict the most likely scenario for tomorrow and a variance around that forecast, we predict all possible states of the energy system for tomorrow, with a probability of occurrence assigned to each state. This approach allows us to manage risk much more effectively relative to legacy modeling methods, ultimately reducing cost. As we record actual results, we compare them to our predictions, and feed that back into the historical data set, creating a learning system that continuously improves its forecasts.
Key features of the gridseer technology platform
Probabilistic Forecasts and Advanced Machine Learning
Harnesses cutting-edge machine learning to generate detailed probabilistic forecasts that adapt to real-time weather and system conditions, ensuring high precision in demand and supply management. Produces detailed scenario-based forecasts of energy demand and renewable energy production, accounting for weather patterns and variability.
Risk-Adjusted Stochastic Unit Commitment (RAS-UC)
A core feature that strategically plans power generation a week in advance, factoring in uncertainty to optimize costs and minimize risk, thereby ensuring stability and reliability. Ensures optimal operation of renewable resources and large-scale energy storage systems.
Energy Management System (EMS)
Implements real-time resource dispatch based on advanced forecasts and risk assessments, maintaining a balance between generation and consumption, even under extreme conditions not ever seen in historical data.
Optimal Utilization of Renewable Resources and Large-Scale Energy Storage
Promotes environmental sustainability by maximizing the use of renewable energy and enhancing the efficiency of large energy storage systems, like pumped hydro.
Quantified Impact on System Risk
Employs a unique Risk Scoring Mechanism to quantify and manage the impacts of different grid resources on the system's risk profile, supporting informed decision-making and enhancing system resilience.
Built on Academic Excellence
Rooted in Research. Refined in Practice.
GridSeer was developed at Duke University’s GRACE Lab, where researchers have spent over a decade modeling grid uncertainty and building scalable solutions. That academic foundation ensures our platform remains rigorous, evidence-based, and responsive to a fast-changing energy landscape.