预测建筑中能源相关行为的心理模型:认知参数优化

IF 1.2 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Cognitive Computation and Systems Pub Date : 2022-02-04 DOI:10.1049/ccs2.12042
Jörn von Grabe, Sepideh Korsavi
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引用次数: 1

摘要

建筑能耗是导致全球变暖的主要因素,因此已成为一个深入研究的领域。这种类型的能源消耗可以用两个维度来描述:基于设备的维度和基于行为的维度。为了解决基于行为的维度,最近的一项研究提出了一种基于实例学习范式的认知人类建筑交互模型。然而,由于通常用于模拟基于实验室的行为的标准认知参数的值不适合人类建筑交互的“现实世界”领域,因此本文旨在确定适应并适合该应用领域特定特征的认知参数值。为了实现这一目标,设计了一个虚拟测试环境——由一个被占用的房间和相应的模型任务组成——来测试模型的性能及其对一组基本认知参数的依赖性。提出了一种不依赖于经验数据而以模型预测一致性为参考的检验标准。基于理论和经验考虑,为每个参数预先选择了一系列值,然后根据评估标准进行测试。在整个参数化过程中,模型的性能得到了显着改善,并产生了可信的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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A psychological model for the prediction of energy-relevant behaviours in buildings: Cognitive parameter optimisation

Energy consumption in buildings is a major contributor to global warming and therefore has become a field of intensive research. This type of energy consumption can be described in two dimensions: an appliance-based dimension and a behaviour-based dimension. To address the behaviour-based dimension a recent study proposed a cognitive human-building interaction model that builds on the instance-based learning paradigm. However, since the values of the standard cognitive parameters commonly used for modelling lab-based behaviours are not suitable for the ‘real-world’ domain of human-building interaction, this paper aims to identify cognitive parameter values adapted to and suitable for the specific character of this application domain. To achieve this goal, a virtual test environment—consisting of an occupied room and a corresponding model task—was designed to test the performance of the model and its dependence on a set of fundamental cognitive parameters. A test criterion was developed that did not depend on empirical data but used the predictive consistency of the model as reference. A range of values was pre-selected for each parameter based on theoretical and empirical considerations, which was then tested against the evaluation criterion. The performance of the model was improved significantly throughout the parametrisation process and yielded plausible results.

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来源期刊
Cognitive Computation and Systems
Cognitive Computation and Systems Computer Science-Computer Science Applications
CiteScore
2.50
自引率
0.00%
发文量
39
审稿时长
10 weeks
期刊最新文献
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