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，亟需智能解决方案以充分利用这些数据提升能效性能。本研究提出了一种名为"深度能效孪生"的解决方案，通过融合深度学习与数字孪生技术，深入理解建筑能源使用模式并识别能效提升潜力。采用本体论方法创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模型与采集数据，运用深度学习方法进行数据分析，识别模式特征并为能源优化提供决策依据。通过瑞典诺尔雪平一座历史公共建筑的案例研究，对比分析了当前主流深度学习架构在建筑能耗预测中的性能表现。