@article{34620,
  author = {Donghun Kim and Yeonjin Bea and Sehyun Yun and James E Braun},
  title = {A methodology for generating reduced-order models for large-scale buildings using the Krylov subspace method},
  abstract = {<p>Developing a computationally efficient but accurate building energy simulation (BES) model is important<br />for many purposes. Model order reduction (MOR) methods are attractive and much more reliable than identification approaches, since it directly extract a lower-dimensional model from a detailed physics-based<br />model without any pre-simulations. However, because of computational and data storage requirements,<br />there are challenges of applying these methods to a large-scale building. To overcome the problem, this<br />paper introduces the Krylov subspace method to the building science field. Technical issues of applying<br />the method to building applications are addressed and a suitable algorithm that overcomes those challenges is presented. To demonstrate the reliability of the algorithm, comparisons between the resulted<br />reduced-order model (ROM) and a high-fidelity model from a commercial BES software for a 60-zone case<br />study building are provided. The ROM was a factor of 100 faster than the high fidelity model but with high<br />accuracy</p>},
  year = {2020},
  journal = {Journal of Building Performance Simulation},
  volume = {13},
  pages = {419 - 429},
  month = {03/2020},
  issn = {1940-1493},
  url = {https://www.tandfonline.com/doi/full/10.1080/19401493.2020.1752309https://www.tandfonline.com/doi/pdf/10.1080/19401493.2020.1752309},
  doi = {10.1080/19401493.2020.1752309},
  language = {eng},
}