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.

翻译：建筑环境数字化转型产生了大量数据，可用于开发数据驱动模型以优化建筑运营。本研究提出一种集成解决方案，利用边缘计算、数字孪生和深度学习技术增强对建筑气候的理解。采用本体论构建的参数化数字孪生可确保不同建筑所配备的多样服务系统之间的数据表示一致性。基于已创建的数字孪生和采集数据，运用深度学习方法开发预测模型，以识别室内气候模式并提供洞察。参数化数字孪生和深度学习模型均部署于边缘端，以实现低延迟并满足隐私合规要求。为进行验证，在瑞典东约特兰省某历史建筑中开展案例研究，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室内温度与相对湿度的多步预测中展现出强劲竞争力且计算成本较低。