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A Retrieval-Augmented Generation Approach for Data-Driven Energy Infrastructure Digital Twins

Paper presenting a data-driven energy digital-twin framework and architecture.

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A Retrieval-Augmented Generation Approach for Data-Driven Energy Infrastructure Digital Twins

By Saverio Ieva, Davide Loconte, Giuseppe Loseto, Michèle Ruta, Floriano Scioscia, Davide Marche, Marianna NotarnicolaSmart Cities
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The paper proposes a novel data-driven and knowledge-based energy digital-twin framework, integrating machine learning with a knowledge graph to support a retrieval-augmented generation approach.

This enhances a conversational virtual assistant for user decision support in asset management and maintenance. A prototype framework was implemented using commercial-off-the-shelf tools and tested on a case study.

Abstract

The paper proposes a novel data-driven and knowledge-based energy digital-twin framework, integrating machine learning with a knowledge graph to support a retrieval-augmented generation approach. This enhances a conversational virtual assistant for user decision support in asset management and maintenance. A prototype framework was implemented using commercial-off-the-shelf tools and tested on a case study.

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digital-twinsenergy-infrastructuremachine-learningknowledge-graphretrieval-augmented-generationKnowledge GraphsStructured ContentContent EngineeringAI Agents
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