The growth of generative AI (GenAI) has introduced new opportunities for electric utilities. We will explore how Duke Energy and Amazon Web Services (AWS) have partnered to build applications with GenAI that enable engineers to seamlessly connect enterprise data from different sources to perform meaningful analysis for use cases such as transformer health analysis or DER siting. We will also highlight how implementing AI and ML has helped Duke Energy better identify grid assets and fleet electrification opportunities. 

We will show how recent advancements in the GenAI space have enabled a deeper and more robust user experience, allowing weeks' worth of hard-to-do analytics to be completed in minutes via an interactive experience. Users can perform analysis against a variety of sources without worrying about data integrations, data joins, or AI/ML model selection and tuning at scale. This includes tabular data such as historical grid data accessed via SQL databases, asset maintenance reports in an enterprise knowledge base/vendor software systems, real-time telemetry data such as AMI/SCADA accessed via APIs, or external data such as county-provided real-estate parcel data.

Such analytics, made easier and faster with GenAI, is a critical enabler for adding gigawatts of DERs within the next five years, which is orders of magnitude more than the capacity added to date.