%Aigaion2 BibTeX export from Idiap Publications
%Saturday 23 November 2024 08:58:26 AM

@ARTICLE{DemirDilsiz_BUILD.SIMUL._2022,
         author = {Demir Dilsiz, Aysegul and Ng, Kaitlynn and K{\"{a}}mpf, J{\'{e}}r{\^{o}}me and Nagy, Zolt{\'{a}}n},
       keywords = {building stocks, energy modelling, global sensitivity analysis, parameter screening, Sobol’ indices, Sobol’ method, sustainable urban planning, urban energy modeling},
       projects = {Idiap},
          month = dec,
          title = {Ranking parameters in urban energy models for various building forms and climates using sensitivity analysis},
        journal = {Building Simulation},
           year = {2022},
            doi = {10.1007/s12273-022-0961-5},
       abstract = {Urban Building Energy Modelling (UBEM) allows us to simulate buildings’ energy performances at a larger scale. However, creating a reliable urban-scale energy model of new or existing urban areas can be difficult since the model requires overly detailed input data, which is not necessarily publicly unavailable. Model calibration is a necessary step to reduce the uncertainties and simulation results in order to develop a reliable and accurate UBEM. Due to the concerns over computational resources and the time needed for calibration, a sensitivity analysis is often required to identify the key parameters with the most substantial impact before the calibration is deployed in UBEM. Here, we study the sensitivity of uncertain input parameters that affect the annual heating and cooling energy demand by employing an urban-scale energy model, CitySim. Our goal is to determine the relative influence of each set of input parameters and their interactions on heating and cooling loads for various building forms under different climates. First, we conduct a global sensitivity analysis for annual cooling and heating consumption under different climate conditions. Building upon this, we investigate the changes in input sensitivity to different building forms, focusing on the indices with the largest Total-order sensitivity. Finally, we determine First-order indices and Total-order effects of each input parameter included in the urban building energy model. We also provide tables, showing the important parameters on the annual cooling and heating demand for each climate and each building form. We find that if the desired calibration process require to decrease the number of the inputs to save the computational time and cost, calibrating 5 parameters; temperature set-point, infiltration rate, floor U-value, avg. walls U-value and roof U-value would impact the results over 55\% for any climate and any building form.}
}