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.

提出了 Deep Energy Twin 解决方案，将深度学习和数字孪生集成在一起来更好地理解建筑能源使用情况，提高能源效率。使用本体论创建参数化的数字孪生来提供建筑中不同系统之间数据格式的一致性。