CommandCenter for Energy
A comprehensive view of the energy grid featuring live data visualization and AI-enhanced alerts. By incorporating environmental factors such as weather, CommandCenter predicts potential outages, optimizes performance during adverse conditions, and identifies maintenance needs before system failures occur.
A More Resilent Grid Intrastrucure
CommandCenter for Energy provides an aggregated geospatial view of the grid that identifies interdependencies. An integrated solution enables the visualization of complex data in an easily understandable way. Interactive dashboards display grid health, energy flows, weather forecasts, and potential climate risks and 3D simulations, allowing stakeholders to visualize the impact of different grid investment scenarios and climate change on infrastructure as part of the recommended approach.
AI-powered recommendations generate optimal courses of action for grid operators and decision-makers, contributing to a more resilient grid infrastructure driving a proactive vs. reactive approach to grid management.
AI for electric grid infrastructure
| Grid Resiliency: AI for predictive maintenance leveraging data, sensors, and devices to improve resiliency. Proactively predicting equipment failure that would impact grid stability. AI/ML algorithms to identify network traffic anomalies for detection of potential cyber threats.
Planning, Permitting, & Renewable Investment: AI data-driven and decision-making by analyzing regulatory data and geographical and environmental datasets for identifying optimal locations for renewable energy, taking into account permitting requirements and compliance obstacles. Climate Change Risk Mitigation: AI models to enhance understanding of historical weather data, enabling more accurate prediction of weather events even when changing due to climate change. |
AI for predictive maintenance leveraging data, sensors, and devices to improve resiliency. Proactively predicting equipment failure that would impact grid stability. AI/ML algorithms to identify network traffic anomalies for detection of potential cyber threats.
Planning, Permitting, & Renewable Investment: AI data-driven and decision-making by analyzing regulatory data and geographical and environmental datasets for identifying optimal locations for renewable energy, taking into account permitting requirements and compliance obstacles.
Climate Change Risk Mitigation: AI models to enhance understanding of historical weather data, enabling more accurate prediction of weather events even when changing due to climate change.
