The Searchable Grid: Building a GraphRAG-Powered Digital Twin for Real-Time Insights
summary
Claimed system unifies GIS, AMI, asset health and DER into a semantic Neo4j graph and layers a GraphRAG-powered Digital Twin that answers plain-English queries by translating text to Cypher, traversing subgraphs and returning contextual results in seconds (typical <5 minutes, sometimes ~5 seconds). Implementation uses Neo4j, NVIDIA embeddings, LLMs (Gemini, ChatGPT, others), Python and a web UI; benchmarks include an IEEE bus model and demos that report 1,247, 5,756 and 53 affected customers in different scenarios. IEC 61968 schema and real-time traversal+embeddings drive fault tracing and restoration estimates.
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chapters (12)
- 0:00Introduction and overview
- 1:36Current challenges in utilities
- 2:44Limitations of static grids
- 3:26Proposed GraphRAG digital twin
- 5:55Why GraphRAG over other methods
- 6:17Architecture overview
- 8:23Real-world fault tracing use case
- 13:16Graph data model standards
- 17:51Data ingestion and enrichment
- 21:23Knowledge graph visualization
- 25:15Live demo fault detection
- 29:15Key takeaways and conclusion
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