AI for Grid Forecasting and Outages

Artificial intelligence and advanced analytics are beginning to transform how electric utilities forecast demand, predict outages, and manage grid operations. As utilities deploy advanced metering infrastructure, sensor networks, and real-time monitoring systems, they are gaining access to unprecedented volumes of operational data that can support predictive decision-making. This course provides a practical overview of how utilities are applying AI-driven forecasting and predictive analytics to improve grid reliability, outage response, and asset management. Rather than focusing solely on technical AI concepts, the course examines the real operational decisions utilities must make, from forecasting electricity demand and predicting storm-related outages to monitoring asset health and planning grid upgrades.

Register now to join us in exploring AI-driven emerging forecasting challenges related to electrification, distributed energy resources, and increasingly complex generation interconnection processes.

1. Explain how electric utilities are using advanced analytics and artificial intelligence to improve forecasting, outage prediction, and grid reliability
2. Identify the operational data infrastructure required for predictive analytics, including SCADA systems, AMI/smart meters, weather feeds, asset registries, and sensor networks
3. Describe how machine learning models are applied to load forecasting, outage prediction, storm response planning, and grid resilience analysis
4. Evaluate how predictive analytics can improve asset health monitoring and maintenance planning for grid equipment such as transformers, lines, and substations
5. Examine how AI tools integrate with utility operational systems such as SCADA, outage management systems (OMS), and distribution management systems (DMS)
6. Discuss emerging forecasting and planning challenges facing utilities, including electrification, distributed energy resources (DERs), large data-center loads, and generator interconnection queue uncertainty
7. Recognize organizational, regulatory, and cybersecurity considerations when deploying AI in utility environments
8. Identify practical steps utilities can take to begin implementing predictive analytics and AI within grid operations