Driving sustainable energy transitions with a multi-source RAG-LLM system

Arslan, Muhammad; Mahdjoubi, Lamine; Munawar, Saba

doi:10.1016/j.enbuild.2024.114827

Energy and Buildings

2024

DOI: 10.1016/j.enbuild.2024.114827

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Driving sustainable energy transitions with a multi-source RAG-LLM system

Muhammad Arslan,

Lamine Mahdjoubi,

Saba Munawar

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Cited by 2 publications

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Application and prospects of large AI models in virtual power plants

Wang,

Peng,

Huang

2025

Electric Power Systems Research

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Application and prospects of large AI models in virtual power plants

Wang,

Peng,

Huang

2025

Electric Power Systems Research

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Exploring Data Analysis Methods in Generative Models: From Fine-Tuning to RAG Implementation

Guțu,

Popescu

2024

Computers

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The exponential growth in data from technological advancements has created opportunities across fields like healthcare, finance, and social media, but sensitive data raise security and privacy challenges. Generative models offer solutions by modeling complex data and generating synthetic data, making them useful for the analysis of large private datasets. This article is a review of data analysis techniques based on generative models, with a focus on large language models (LLMs). It covers the strengths, limitations, and applications of methods like the fine-tuning of LLMs and retrieval-augmented generation (RAG). This study consolidates, analyzes, and interprets the findings from the literature to provide a coherent overview of the current research landscape on this topic, aiming to guide effective, privacy-conscious data analysis and exploring future improvements, especially for low-resource languages.

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Driving sustainable energy transitions with a multi-source RAG-LLM system

Cited by 2 publications

References 52 publications

Application and prospects of large AI models in virtual power plants

Application and prospects of large AI models in virtual power plants

Exploring Data Analysis Methods in Generative Models: From Fine-Tuning to RAG Implementation

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