%0 Journal Article
%A Donghun Kim
%A Yeonjin Bea
%A Sehyun Yun
%A James E Braun
%B Journal of Building Performance Simulation
%D 2020
%G eng
%N 4
%P 419 - 429
%R 10.1080/19401493.2020.1752309
%T A methodology for generating reduced-order models for large-scale buildings using the Krylov subspace method
%U https://www.tandfonline.com/doi/full/10.1080/19401493.2020.1752309https://www.tandfonline.com/doi/pdf/10.1080/19401493.2020.1752309
%V 13
%8 03/2020
%! Journal of Building Performance Simulation
%X <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>