Project Description (Short) - statnett/Talk2PowerSystem GitHub Wiki

Background

Power system data are getting more and more complex; the average experience of power engineers is dropping due to retirement. Electrical data must be used not only by power engineers with deep domain knowledge, IT skills and Common Information Model (CIM) knowledge, but also by other stakeholders and decision makers. Transitioning to a data-driven approach necessitates high-quality, well-defined metadata to ensure data clarity, consistency, and usability.

Goal

Use state-of-the-art semantic web and knowledge graph technologies to train large-language models (LLMs) to empower power system engineers and stakeholders to intuitively interact with complex CIM-based data using natural language (NLQ) supported by advanced AI methodologies.

Outcomes

Openly accessible CIM and Q&A datasets, tools for interfacing with LLMs, SPARQL & GraphQL querying, evaluation/validation framework, harmonization with other relevant standards (BIM, IEC 61850, W3C).

Expected Benefits

Intuitive and simplified access to CIM-based power system data. Enhanced decision-making through accurate, explainable, and trustworthy insights. Improved semantic interoperability across European and international standards. Increased operational efficiency and faster decision making to support the renewable and digital transition. Mitigation of knowledge loss due to expert retirement through systematic knowledge capture. Leverage semantic reasoning to infer implicit data relationships, reducing query complexity and manual modeling efforts.

TRL: 2-7 (Technology readiness level - level 7 means "System prototype demonstration in operational environment") Project period: Q1/2025 - Q2/2026