Global sensitivity analysis as a support for the generation of simplified building stock energy models

摘要: Buildings are responsible for 40% of total final energy consumptions in Europe. Numerous bottom-up models were recently developed to support local authorities assessing the consumption large building stocks and reduction potentials. However, current rarely consider uncertainty associated usage characteristics within stock, resulting potentially biased results. This study presents a generic model simplification approach using propagation stochastic sensitivity analysis derive fast simplified (surrogate) estimate stock use improved urban planning. The methodology includes an engineering-based as input global (GSA) elementary effects (EE) screening Sobol’ method key parameters identification regression entire stocks. application housing Esch-sur-Alzette (Luxembourg) showed that explaining most variability heating domestic hot water floor area, set-point temperature, external walls U-values, windows system type. Results validated against measured data confirmed validity simple yet robust assessment considering variability.