标题： 基于边的参数化数字孪生用于智能建筑室内气候建模 显示英文标题

标题： Edge-based Parametric Digital Twins for Intelligent Building Indoor Climate Modeling

摘要： 数字转型在建筑环境中产生了大量数据，用于开发数据驱动的模型以优化建筑运营。 本研究提出了一种集成解决方案，利用边缘计算、数字孪生和深度学习来增强对建筑内气候的理解。 使用本体论创建的参数化数字孪生确保了不同配备服务系统的建筑之间的一致数据表示。 基于创建的数字孪生和收集的数据，采用深度学习方法开发预测模型，以识别室内气候中的模式并提供见解。 参数化数字孪生和深度学习模型都部署在边缘端，以实现低延迟和隐私合规性。 作为示范，对瑞典Östergötland的一座历史建筑进行了案例研究，以比较五种深度学习架构的性能。 结果表明，时间序列密集编码器模型在进行室内温度和相对湿度的多时间范围预测方面表现出强大的竞争力，且计算成本较低。 显示英文摘要

摘要： Digital transformation in the built environment generates vast data for developing data-driven models to optimize building operations. This study presents an integrated solution utilizing edge computing, digital twins, and deep learning to enhance the understanding of climate in buildings. Parametric digital twins, created using an ontology, ensure consistent data representation across diverse service systems equipped by different buildings. Based on created digital twins and collected data, deep learning methods are employed to develop predictive models for identifying patterns in indoor climate and providing insights. Both the parametric digital twin and deep learning models are deployed on edge for low latency and privacy compliance. As a demonstration, a case study was conducted in a historic building in \"Osterg\"otland, Sweden, to compare the performance of five deep learning architectures. The results indicate that the time-series dense encoder model exhibited strong competitiveness in performing multi-horizon forecasts of indoor temperature and relative humidity with low computational costs.