Digital transformation in buildings accumulates massive operational data, which calls for smart solutions to utilize these data to improve energy performance. This study has proposed a solution, namely Deep Energy Twin, for integrating deep learning and digital twins to better understand building energy use and identify the potential for improving energy efficiency. Ontology was adopted to create parametric digital twins to provide consistency of data format across different systems in a building. Based on created digital twins and collected data, deep learning methods were used for performing data analytics to identify patterns and provide insights for energy optimization. As a demonstration, a case study was conducted in a public historic building in Norrk\"oping, Sweden, to compare the performance of state-of-the-art deep learning architectures in building energy forecasting.

翻译：建筑领域的数字化转型积累了海量运行数据，需要利用这些数据提升能效的智能解决方案。本研究提出了一种名为“深度能源孪生”（Deep Energy Twin）的方案，将深度学习与数字孪生相结合，以更深入地理解建筑能耗情况，并识别提升能效的潜力。采用本体论构建参数化数字孪生模型，确保建筑内不同系统间数据格式的一致性。基于构建的数字孪生及采集的数据，运用深度学习方法进行数据分析，识别能耗模式并提供能源优化策略。为验证该方案，在瑞典诺尔雪平一栋历史公共建筑中开展了案例研究，对比了当前最先进的深度学习架构在建筑能耗预测中的性能表现。