ChatBot using Vertex AI Natural Language Query Explorer for Power Grids Author: Saiprashanth Vana Institution: George Mason University Overview The Natural Language Query Explorer for Power Grids (NLQE-PG) is an AI-powered system designed to simplify complex power grid data analysis using natural language queries. This system leverages Vertex AI and Google Cloud Platform (GCP) to translate user queries into SQL, allowing non-technical users like grid operators and analysts to access crucial data insights efficiently.

Key Features Natural Language Query Processing: Uses Vertex AI’s NLP capabilities to convert user queries into SQL commands. Semantic Search Integration: Enhances query understanding using machine learning embeddings. Real-Time Data Retrieval: Processes large-scale grid data in Google BigQuery and returns results instantly. Cloud-Based Architecture: Scalable and reliable deployment using Google Cloud services. Data Dictionary Mapping: Ensures accurate SQL translations for power grid datasets. System Architecture Natural Language Processing Module: Parses user queries using Vertex AI LLMs. Data Dictionary: Maps natural language terms to corresponding SQL database elements. Query Translation Engine: Converts processed queries into optimized SQL. Semantic Search Module: Uses embeddings to refine results and enhance relevance. Google Cloud Infrastructure: Utilizes BigQuery, Cloud Run, and GCP services for scalable data handling. Use Cases Real-Time Grid Monitoring: Tracks load distribution and identifies potential imbalances. Fault Detection: Identifies and isolates system failures efficiently. Energy Flow Optimization: Helps improve transmission efficiency and reduce energy loss. Performance Metrics Query Accuracy: 94% accuracy in translating natural language to SQL. Response Time: Average execution time of 1.8 seconds for standard queries. Scalability: Cloud-based implementation ensures high availability for large datasets. Future Enhancements Improved Context Understanding: Incorporating transformer models like BERT or GPT. Multi-Language Support: Expanding NLP capabilities for broader accessibility. Advanced Visualization: Integration with interactive dashboards for better insights. Cross-Domain Applications: Exploring applications in healthcare, transportation, and smart cities. How to Use Deploy the system on Google Cloud using BigQuery and Cloud Run. Enter natural language queries via the UI. The system processes the input using NLP, maps it to SQL, and retrieves results. Results are displayed in a structured format for easy interpretation. Acknowledgments Special thanks to George Mason University and Professor Dr. Hung-Ming Chou for providing guidance and access to cloud resources.

References Google Cloud Vertex AI Documentation Google Cloud BigQuery