期刊
JOURNAL OF BUILDING PERFORMANCE SIMULATION
卷 13, 期 3, 页码 347-361出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/19401493.2020.1729862
关键词
Urban building energy modelling; simulation performance gap; geographic information system; sensitivity analysis; Bayesian calibration; spatial-temporal modelling
资金
- European Research Council (ERC) under the European Union's Horizon2020 Research& Innovation Programme [677312]
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 open-source data harmonization, sensitivity analysis, heating demand simulation at the postcode level to Bayesian calibration with six years of training data and two years of validation data. Comparing the baseline and the calibrated simulation results, the averaged absolute percentage errors of energy use intensity in the study area have significantly improved from 25.0% to 8.3% and from 19.9% to 7.7% in two validation years, while and . The overall methodology is extendable to other urban contexts.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据