The power industry must transform to accommodate the green transition, adopting innovative tools and methods to revolutionize how we perform power system analyses. Extracting data from power system models modeled according to the Common Information Model (CIM) standards (IEC 61970, 61968, 62325) is an integral part of this analytical work.
However, crafting the required graph queries often exceeds the capabilities of many power system engineers due to their complexity. Inspired by advancements in AI, such as Chat-GPT, we envision a future where power system engineers can receive trustworthy and explainable answers through natural language queries.
Our vision is to create a system that allows power system engineers to:
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Query complex models in natural language: Eliminate the need for specialized query language expertise.
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Receive trustworthy answers: Ensure results are reliable, transparent, and align with established industry standards.
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Accelerate decision-making: Streamline workflows to support the green energy transition.
This repository supports R&D efforts to accelerate progress toward this vision. Specifically, it focuses on:
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Enabling natural language interfaces for querying CIM-based power system models.
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Ensuring queries and results are explainable, traceable, and compliant with standards.
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Bridging the gap between power system engineering and advanced AI methodologies.
This work leverages the following key standards and technologies:
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Common Information Model (CIM): Standards IEC 61970, IEC 61968, and IEC 62325.
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Graph Databases and Queries: Supporting the querying of CIM models stored as graphs.
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AI and Natural Language Processing (NLP): Inspired by large language models like Chat-GPT.
This work is licensed under the Apache License, Version 2.0.