Leveraging Deep Learning and Digital Twins to Improve Energy Performance of Buildings
This work addresses energy efficiency for building operators, but it appears incremental as it applies existing deep learning methods to a new digital twin framework.
The study tackled the problem of improving building energy performance by integrating deep learning and digital twins, resulting in a demonstration using a public historic building in Sweden to compare deep learning architectures for energy forecasting.
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 Norrköping, Sweden, to compare the performance of state-of-the-art deep learning architectures in building energy forecasting.