发表机构
University of Arizona（亚利桑那大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出集成大语言模型的上下文工程框架，生成适配家庭独特特征的生态反馈，经实证验证其准确率达92.0%、数据引用准确率95.7%，可支撑居住者与建筑的上下文感知交互，助力提升生活质量与可持续性。
AI 中文摘要
本研究旨在展示通过集成大语言模型的框架生成考虑家庭独特上下文信息的生态反馈（即上下文感知生态反馈）的潜力。过往研究已提出个性化生态反馈，但其大多依赖家庭能源使用模式，常未反映家庭的独特特征，包括用户画像或不可协商的日常惯例，导致生态反馈效果不佳且有时流于表面。为解决这些局限，我们引入了上下文工程框架，该框架利用家庭能源分析数据、公用事业费率结构和特征信息，通过结合思维链提示的自一致性方法生成生态反馈。我们开展了严格的实证验证和组合评估分析以系统评估该框架：前者旨在通过与参考干预措施对比，测试框架针对给定上下文生成准确、数据驱动的定制化生态反馈的能力；后者旨在通过调查上下文感知生态反馈的变化情况，揭示框架在不同家庭上下文间的适应性。关键发现如下：在不同家庭配置下，我们提出的框架生成的生态反馈与参考解决方案的平均一致性准确率达92.0%，且在利用提供的家庭数据生成反馈时的数据引用准确率为95.7%；此外，该框架对不同家庭上下文具有较强适应性，会显著调整目标电器和节能策略。最终，本研究为实现居住者与建筑之间更高层次的上下文感知交互做出贡献，为提升居住者生活质量和可持续发展铺平道路。
英文摘要
The objective of this study is to demonstrate the potential of generating context-aware eco-feedback - eco-feedback that reflects a household's contextual characteristics alongside its energy use patterns - through a large language model-integrated framework. Previous studies have introduced personalized eco-feedback, mostly relying on household energy use patterns; however, they frequently did not reflect distinct household characteristics, including their persona or non-negotiable routines, leaving eco-feedback ineffective and sometimes superficial. To address these limitations, we introduce a contextual engineering framework that generates eco-feedback using a self-consistency with chain-of-thought prompt, leveraging household energy analysis data, utility rate structures, and household characteristic information. We conducted a rigorous empirical validation and a combinatorial evaluation analysis to assess this framework systematically. The former tested the framework's ability to generate accurate and contextually grounded eco-feedback for three households by comparing its output against reference interventions independently derived from the same household data. The latter examined the framework's adaptability across 400 scenarios spanning 50 households, two utility rate structures, and four behavioral personas. Our framework generated eco-feedback that aligned with reference interventions at a mean rate of 92.0% and grounded its recommendations in the provided household data with 95.7% citation accuracy. It also proved highly adaptive, shifting both the appliances targeted and the energy-saving strategies recommended in response to rate structure and household context. Ultimately, this study contributes to realizing the next level of context-aware interactions between occupants and buildings which paves the way for higher occupant living quality and sustainability.
Comments41 pages, 12 figures, 12 tables. Accepted manuscript. The peer-reviewed and published version appears in Energy and Buildings 370 (2026) 118038
Journal refEnergy and Buildings 370 (2026) 118038
DOI：10.1016/j.enbuild.2026.118038
