A Retrieval-Augmented Generation Approach for Data-Driven Energy Infrastructure Digital Twins
Digital-twin platforms are increasingly adopted in energy infrastructure management for smart grids. Novel opportunities arise from emerging artificial intelligence technologies to increase user trust by enhancing predictive and prescriptive analytics capabilities and by improving user interaction p...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-10-01
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| Series: | Smart Cities |
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| Online Access: | https://www.mdpi.com/2624-6511/7/6/121 |
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| author | Saverio Ieva Davide Loconte Giuseppe Loseto Michele Ruta Floriano Scioscia Davide Marche Marianna Notarnicola |
| author_facet | Saverio Ieva Davide Loconte Giuseppe Loseto Michele Ruta Floriano Scioscia Davide Marche Marianna Notarnicola |
| author_sort | Saverio Ieva |
| collection | DOAJ |
| description | Digital-twin platforms are increasingly adopted in energy infrastructure management for smart grids. Novel opportunities arise from emerging artificial intelligence technologies to increase user trust by enhancing predictive and prescriptive analytics capabilities and by improving user interaction paradigms. This paper presents a novel data-driven and knowledge-based energy digital-twin framework and architecture. Data integration and mining based on machine learning are integrated into a knowledge graph annotating asset status data, prediction outcomes, and background domain knowledge in order to support a retrieval-augmented generation approach, which enhances a conversational virtual assistant based on a large language model to provide user decision support in asset management and maintenance. Components of the proposed architecture have been mapped to commercial-off-the-shelf tools to implement a prototype framework, exploited in a case study on the management of a section of the high-voltage energy infrastructure in central Italy. |
| format | Article |
| id | doaj-art-9f7055dcb5f7472baf8d55ed9ea3de70 |
| institution | OA Journals |
| issn | 2624-6511 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Smart Cities |
| spelling | doaj-art-9f7055dcb5f7472baf8d55ed9ea3de702025-08-20T02:01:14ZengMDPI AGSmart Cities2624-65112024-10-01763095312010.3390/smartcities7060121A Retrieval-Augmented Generation Approach for Data-Driven Energy Infrastructure Digital TwinsSaverio Ieva0Davide Loconte1Giuseppe Loseto2Michele Ruta3Floriano Scioscia4Davide Marche5Marianna Notarnicola6Department of Electrical and Information Engineering, Polytechnic University of Bari, Via E. Orabona 4, I-70125 Bari, ItalyDepartment of Electrical and Information Engineering, Polytechnic University of Bari, Via E. Orabona 4, I-70125 Bari, ItalydonkeyPower S.r.l., Via E. Orabona 4, I-70125 Bari, ItalyDepartment of Electrical and Information Engineering, Polytechnic University of Bari, Via E. Orabona 4, I-70125 Bari, ItalyDepartment of Electrical and Information Engineering, Polytechnic University of Bari, Via E. Orabona 4, I-70125 Bari, ItalyLutech S.p.A., Via M. Gorki 30/32C, I-20092 Cinisello Balsamo, ItalyLutech S.p.A., Via M. Gorki 30/32C, I-20092 Cinisello Balsamo, ItalyDigital-twin platforms are increasingly adopted in energy infrastructure management for smart grids. Novel opportunities arise from emerging artificial intelligence technologies to increase user trust by enhancing predictive and prescriptive analytics capabilities and by improving user interaction paradigms. This paper presents a novel data-driven and knowledge-based energy digital-twin framework and architecture. Data integration and mining based on machine learning are integrated into a knowledge graph annotating asset status data, prediction outcomes, and background domain knowledge in order to support a retrieval-augmented generation approach, which enhances a conversational virtual assistant based on a large language model to provide user decision support in asset management and maintenance. Components of the proposed architecture have been mapped to commercial-off-the-shelf tools to implement a prototype framework, exploited in a case study on the management of a section of the high-voltage energy infrastructure in central Italy.https://www.mdpi.com/2624-6511/7/6/121digital twinenergy infrastructuresenergy managementretrieval-augmented generationnatural user interface |
| spellingShingle | Saverio Ieva Davide Loconte Giuseppe Loseto Michele Ruta Floriano Scioscia Davide Marche Marianna Notarnicola A Retrieval-Augmented Generation Approach for Data-Driven Energy Infrastructure Digital Twins Smart Cities digital twin energy infrastructures energy management retrieval-augmented generation natural user interface |
| title | A Retrieval-Augmented Generation Approach for Data-Driven Energy Infrastructure Digital Twins |
| title_full | A Retrieval-Augmented Generation Approach for Data-Driven Energy Infrastructure Digital Twins |
| title_fullStr | A Retrieval-Augmented Generation Approach for Data-Driven Energy Infrastructure Digital Twins |
| title_full_unstemmed | A Retrieval-Augmented Generation Approach for Data-Driven Energy Infrastructure Digital Twins |
| title_short | A Retrieval-Augmented Generation Approach for Data-Driven Energy Infrastructure Digital Twins |
| title_sort | retrieval augmented generation approach for data driven energy infrastructure digital twins |
| topic | digital twin energy infrastructures energy management retrieval-augmented generation natural user interface |
| url | https://www.mdpi.com/2624-6511/7/6/121 |
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