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The Searchable Grid: Building a GraphRAG-Powered Digital Twin for Real-Time Insights

Session Track: Knowledge Graphs & GraphRAG

Session Time:

Session description

Electrical networks generate massive streams of heterogeneous data, from GIS topology and AMI telemetry to transformer health, DER output, and outage events. Yet utilities still depend on static diagrams and disconnected systems, limiting their ability to diagnose issues, understand real-time conditions, or make fast, data-driven decisions. This session introduces a practical and transformative approach: using Neo4j Knowledge Graphs combined with GraphRAG to build a searchable, intelligent Digital Twin of the distribution grid.

We demonstrate how distribution assets-substations, feeders, switches, transformers, meters, and DERs-can be unified into a single, semantically rich knowledge graph that captures topology, dependencies, operational states, and temporal behavior. On top of this Digital Twin, we layer a GraphRAG-powered natural-language engine that does more than translate text into Cypher. It dynamically retrieves the most relevant subgraphs, historical patterns, and state information, enabling engineers and operators to ask complex, domain-specific questions in plain language and receive precise, contextual answers.

From tracing fault propagation and identifying overloaded nodes to analyzing DER hosting capacity and simulating switching operations, the system offers real-time intelligence with explanation and reasoning. This integrated graph + AI architecture empowers utilities with faster outage response, improved situational awareness, reduced engineering effort, and more reliable planning across MV and LV networks.

Attendees will learn:
✔ How to build a real-time, queryable Digital Twin using Neo4j asset models and topology graphs
✔ How GraphRAG fuses LLM reasoning with graph traversal for accurate, context-aware answers
✔ How natural-language querying democratizes grid intelligence for planners, operators, and field teams
✔ How this approach unlocks fault tracing, outage prediction, DER impact assessment, and grid optimization

This talk demonstrates how GraphRAG and Digital Twins together can make the electrical grid truly searchable, explainable, and intelligent.

Speaker

photo of Lijimol George

Lijimol George

Data Scientist and Researcher, Tata Consultancy Services

Lijimol George is a Senior Data Scientist at Tata Consultancy Services, with over 18 years of experience in Artificial Intelligence, Machine Learning, Natural Language Processing, and Sentiment Analysis. Her expertise spans Predictive and Prescriptive Analytics, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Graph Data Science. She has successfully led numerous high-impact projects, delivering AI-driven innovative solutions across IoT, digital engineering, retail, and aerospace domains. Lijimol is currently pursuing a Ph.D. in Data Science and actively shares her research insights at leading international conferences.