Edge-based Parametric Digital Twins for Intelligent Building Indoor Climate Modeling
<|reference_start|>Edge-based Parametric Digital Twins for Intelligent Building Indoor Climate Modeling: 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 \"Osterg\"otland, 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.<|reference_end|>
@article{ni2024edge-based,
    title={Edge-based Parametric Digital Twins for Intelligent Building Indoor
  Climate Modeling},
    author={Zhongjun Ni (1), Chi Zhang (2), Magnus Karlsson (1), Shaofang Gong (1)
  ((1) Department of Science and Technology, Link"oping University, Campus
  Norrk"oping, Norrk"oping, Sweden. (2) Department of Computer Science and
  Engineering, University of Gothenburg, Gothenburg, Sweden.)},
    journal={arXiv preprint arXiv:2403.04326},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.04326},
    primaryClass={eess.SY cs.AI cs.LG cs.SY}
}