2020/04/17 by Arpan Mukherjee, Anna Kuechle Szweda, Mukherjee, Arpan +7
Decision Sciences · Engineering · Environmental Science · #Building Energy and Comfort Optimization #Computational Engineering #FOS: Computer and information sciences #Finance #Probabilistic and Robust Engineering Design #Wind and Air Flow Studies #and Science (cs.CE)
paper · pdf · doi:10.48550/arxiv.2004.08417
openalex publication_date 2020/04/17 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28
It is necessary to estimate the expected energy usage of a building to\ndetermine how to reduce energy usage. The expected energy usage of a building\ncan be reliably simulated using a Building Energy Model (BEM). Many of the\nnumerous input parameters in a BEM are uncertain. To ensure that the building\nsimulation is sufficiently accurate, and to better understand the impact of\nimprecisions in the input parameters and calculation methods, it is desirable\nto quantify uncertainty in the BEM throughout the modeling process. Uncertainty\nquantification (UQ) typically requires a large number of simulations to produce\nmeaningful data, which, due to the vast number of input parameters and the\ndynamic nature of building simulation, is computationally expensive.\nUncertainty Quantification (UQ) in BEM domain is thus intractable due to the\nsize of the problem and parameters involved and hence it needs an advanced\nmethodology for analysis. The current paper outlines a novel\nWeakly-Connected-Systems (WCSs) identification-based UQ framework developed to\npropagate the quantifiable uncertainty in the BEM. The overall approach is\ndemonstrated on the physics-based thermal model of an actual building in\nCentral New York.\n