Can Large Language Models Simulate Human Responses? A Case Study of Stated Preference Experiments in the Context of Heating-related Choices

Stated preference (SP) surveys are a key method to research how individuals make trade-offs in hypothetical, also futuristic, scenarios. In energy context this includes key decarbonisation enablement contexts, such as low-carbon technologies, distributed renewable energy generation, and demand-side response [1,2]. However, they tend to be costly, time-consuming, and can be affected by respondent fatigue and ethical constraints. Large language models (LLMs) have demonstrated remarkable capabilities in generating human-like textual responses, prompting growing interest in their application to survey research. This study investigates the use of LLMs to simulate consumer choices in energy-related SP surveys and explores their integration into data analysis workflows. A series of test scenarios were designed to systematically assess the simulation performance of several LLMs (LLaMA 3.1, Mistral, GPT-3.5 and DeepSeek-R1) at both individual and aggregated levels, considering contexts factors such as prompt design, in-context learning (ICL), chain-of-thought (CoT) reasoning, LLM types, integration with traditional choice models, and potential biases. Cloud-based LLMs do not consistently outperform smaller local models. In this study, the reasoning model DeepSeek-R1 achieves the highest average accuracy (77%) and outperforms non-reasoning LLMs in accuracy, factor identification, and choice distribution alignment. Across models, systematic biases are observed against the gas boiler and no-retrofit options, with a preference for more energy-efficient alternatives. The findings suggest that previous SP choices are the most effective input factor, while longer prompts with additional factors and varied formats can cause LLMs to lose focus, reducing accuracy.