用大模型增强能源政策模拟中的行为与场景，保持可解释性与稳定性。
LLM-Assisted Behavioural and Scenario Augmentation for Agent-Based Energy Adoption Models
- 结合可控行为规则与规则验证的场景，改进农场光伏采纳模型。
- 行为模式下光伏采纳率最高提升约13%，且无异常饱和现象。
- 适合关注能源政策模拟、可解释性建模的研究者使用。
大型语言模型（LLMs）为基于仿真的能源政策分析提供了新机遇，尤其在构建结构化行为假设和探索性技术经济情景方面。然而，直接以LLM推理替代采纳模型会引发可解释性、可复现性和行为有效性问题。本文提出一种混合框架，将受限的行为范式与结构化场景设定融入校准后的基于代理的模型（ABM），用于模拟爱尔兰乳牛农场的光伏（PV）采纳行为。该方法保留原有技术经济采纳机制，同时通过保守、平衡、乐观三类可解释的行为范式进行调节，并通过固定且规则验证的场景开展不确定性分析。在多种政策设置、蒙特卡洛世界及随机种子下的实验表明，行为表现稳定且经济合理，采纳结果在不同行为模式间保持有界且单调。相比无行为调节的逻辑回归情形，采纳率最高提升约13%，未出现不稳定的饱和动态。结果表明，LLM辅助的规范可被可控、可复现且政策相关地集成到校准的能源ABM中。
原文摘要 · Abstract (English)
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.
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