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Bayesian calibration at the urban scale: a case study on a large residential heating demand application in Amsterdam
2020
Journal of Building Performance Simulation, Taylor & Francis
A bottom-up building energy modelling at the urban scale based on Geographic Information System and semantic 3D city models can provide quantitative insights to tackle critical urban energy challenges. Nevertheless, incomplete information is a common obstacle to produce reliable modelling results. The residential building heating demand simulation performance gap caused by input uncertainties is discussed in this study. We present a data-driven urban scale energy modelling framework from
doi:10.1080/19401493.2020.1729862
fatcat:sudscbdilzfifcut2qxcwr6uem