Human interactive behavior is accountable for most of the variance between the observed and predicted energy consumption of buildings, and is accordingly acknowledged as a major field of research into limiting building-related energy consumption. A thorough understanding of occupant behavior is critical to facilitate a more reliable prediction of energy consumption and identifying means by which pro-environmental behaviors can be promoted. Insights and models from psychology and sociology appear to be best suited to improving such understanding, and this article contributes to this end by developing and testing a cognitive model that serves as the core of a numerical human-building interaction model. The proposed implementation builds on instance-based learning, a well-established cognitive modeling paradigm, is integrated into a thermodynamic building model, and complemented by perception models for the approximation of the thermal and olfactory perception of the environment. The model successfully learns to interact plausibly with a set of elements of a model room—a heating system, a window, and the actor’s clothing—in order to establish predefined room conditions. Accumulation of context-specific instances in the declarative memory, which are retrieved and blended in a decision situation, provide the model with the flexibility to adapt its actions to very different climatic contexts, represented by the locations Stuttgart, Madrid, Stockholm, and Melbourne. Moreover, the model manages to find appropriate compromises if need satisfaction requires contradictory actions, such as in situations where satisfaction of the olfactory need requires opening the window and satisfaction of the thermal need requires keeping it closed. Despite its obvious complexity, the model must be considered to be a basic model, which restricts the immediate comparability of its results to human behavior data. However, the successfully applied plausibility checks clearly indicate the value of the cognitive approach to modeling human-building interaction.

中文翻译：

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使用基于实例的学习范例对建筑物中与能源相关的乘员行为建模

人类交互行为是造成建筑物能耗观察值与预测值之间大部分差异的原因，因此被认为是限制与建筑物相关的能耗的主要研究领域。对乘员行为的透彻了解对于促进更可靠的能耗预测和确定可促进环保行为的手段至关重要。来自心理学和社会学的见解和模型似乎最适合于增进这种理解，并且本文通过开发和测试作为数字人与人互动模型的核心的认知模型，为实现这一目标做出了贡献。拟议的实施以基于实例的学习为基础，这是一种公认​​的认知建模范例，集成到热力学建筑模型中，并通过感知模型进行补充，以近似估算环境的热和嗅觉。该模型成功地学习了与模型室的一组元素（加热系统，窗户和演员的衣服）进行合理交互以建立预定的房间条件的过程。声明性内存中特定于上下文的实例的积累（在决策情况下进行检索和混合）为模型提供了灵活性，可以使其行为适应非常不同的气候上下文，例如斯图加特，马德里，斯德哥尔摩和墨尔本。此外，如果需要满足需要采取相互矛盾的行动，则该模型会设法找到适当的妥协方案，例如在满足嗅觉需求需要打开窗户而满足热需求需要保持窗户关闭的情况下。尽管模型具有明显的复杂性，但必须将其视为基本模型，这限制了其结果与人类行为数据的直接可比性。但是，成功应用的合理性检查清楚地表明了认知方法对人与建筑相互作用建模的价值。