Speaker
Description
We will share our journey and evolution of constructing pragmatic Machine Learning (ML) and Artificial Intelligence (AI) models for ComEd’s grid. The focus will be on how this process necessitated the establishment of both general and narrow AI foundational models. We learned the most effective approach is to concentrate on impactful problems which then guides the selection and development of the most suitable tools. Despite the stringent industrial constraints that demand practical applications at each stage, our vision was guided towards both Narrow AI and General multimodal AI. This journey has led us to unanticipated theoretical depths. For instance, we have recognized the need for a higher dimensional graph topological framework. This framework, equipped with a multimodal dynamic federated learning, contributes to the creation of digital twins. These digital twins are key to unlocking solutions for critical practical grid applications, such as next-generation conditional maintenance and investment optimizations. We will explore the balance between practicality and theoretical depth in the realm of Grid AI, and how this balance can lead to innovative and efficient solutions.
