发表机构
School of Computer Science, University of Galway（戈尔韦大学计算机科学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种LLM辅助的混合框架，将有界行为准则和结构化情景规范整合到爱尔兰奶牛场太阳能PV采用的ABM中，实现约13%的采用增长，为能源政策分析提供可控且可复现的增强方法。
AI 中文摘要
大型语言模型（LLM）的最新进展为丰富基于模拟的能源政策分析创造了机会，特别是通过支持结构化行为假设和探索性技术经济情景。然而，直接用LLM推理替代采用模型会引发可解释性、可复现性和行为有效性方面的担忧。本文提出了一种LLM辅助的规范设计混合框架，将有界行为准则和结构化情景规范整合到经校准的爱尔兰奶牛场太阳能光伏（PV）采用的基于智能体的模型（ABM）中。该方法保留了原始的技术经济采用机制，同时通过有界行为调制和情景驱动的不确定性分析对其进行增强。行为效应通过可解释的保守、平衡和乐观准则表示，而未来政策和市场条件则通过固定的、经规则验证的情景规范进行探索。在多种政策设置、蒙特卡洛模拟和随机种子下的实验结果表明，行为稳定且符合经济规律，采用结果在各行为机制中保持有界且单调。与对应的逻辑案例相比，该框架实现了约13%的行为采用增长，且未产生不稳定或不现实的饱和动态。结果表明，LLM辅助的规范可受控、可复现且符合政策相关性地整合到经校准的能源ABM中。
英文摘要
Recent advances in large language models (LLMs) create opportunities to enrich simulation-based energy policy analysis, particularly by supporting structured behavioural assumptions and exploratory techno-economic scenarios. However, directly replacing adoption models with LLM reasoning raises concerns regarding interpretability, reproducibility, and behavioural validity. This paper proposes a hybrid framework for LLM-assisted specification design, integrating bounded behavioural rubrics and structured scenario specifications into a calibrated agent-based model (ABM) of solar photovoltaic (PV) adoption by Irish dairy farms. The proposed approach preserves the original techno-economic adoption mechanism while augmenting it with bounded behavioural modulation and scenario-driven uncertainty analysis. Behavioural effects are represented through interpretable conservative, balanced, and optimistic rubrics, while future policy and market conditions are explored through fixed, rule-validated scenario specifications. Experimental results across multiple policy settings, Monte Carlo worlds, and random seeds demonstrate stable and economically plausible behaviour, with adoption outcomes remaining bounded and monotonic across behavioural regimes. The framework achieves up to approximately 13% behavioural adoption increase relative to the corresponding logistic case without producing unstable or unrealistic saturation dynamics. The results demonstrate that LLM-assisted specifications can be integrated into calibrated energy ABMs in a controlled, reproducible, and policy-relevant manner.
