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

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