Edge-based Parametric Digital Twins for Intelligent Building Indoor Climate Modeling
This work addresses building operation optimization for facility managers, but it is incremental as it combines existing technologies like edge computing and deep learning in a specific domain.
The study tackled indoor climate modeling in buildings by integrating edge computing, digital twins, and deep learning, resulting in a time-series dense encoder model that performed competitively in multi-horizon forecasts of temperature and humidity with low computational costs.
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 Östergötland, 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.