Domain-Specific Large Language Model for Renewable Energy and Hydrogen Deployment Strategies

Hossam A. Gabber () and Omar S. Hemied
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Hossam A. Gabber: Faculty of Engineering and Applied Science, Ontario Tech University (UOIT), Oshawa, ON L1G 0C5, Canada
Omar S. Hemied: Faculty of Engineering and Applied Science, Ontario Tech University (UOIT), Oshawa, ON L1G 0C5, Canada

Energies, 2024, vol. 17, issue 23, 1-25

Abstract: Recent advances in large language models (LLMs) have shown promise in specialized fields, yet their effectiveness is often constrained by limited domain expertise. We present a renewable and hydrogen energy-focused LLM developed by fine-tuning LLaMA 3.1 8B on a curated renewable energy corpus (RE-LLaMA). Through continued pretraining on domain-specific data, we enhanced the model’s capabilities in renewable energy contexts. Extensive evaluation using zero-shot and few-shot prompting demonstrated that our fine-tuned model significantly outperformed the base model across renewable and hydrogen energy tasks. This work establishes the viability of specialized, smaller-scale LLMs and provides a framework for developing domain-specific models that can support advanced research and decision-making in the renewable energy sector. Our approach represents a significant step forward in applying LLMs to the renewable and hydrogen energy sector, offering potential applications in advanced research and decision-making processes.

Keywords: large language model; zero shot; few shots; renewable energy; artificial intelligence; hydrogen deployment; energy deployment strategies (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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