Publication Type

Journal Article

Version

publishedVersion

Publication Date

3-2020

Abstract

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 C⁢V⁢R⁢M⁢S⁢E2016=11.5% and C⁢V⁢R⁢M⁢S⁢E2017=13.2%. The overall methodology is extendable to other urban contexts.

Keywords

Urban building energy modelling, simulation performance gap, geographic information system, sensitivity analysis, Bayesian calibration, spatial-temporal modelling

Discipline

Engineering | Geographic Information Sciences

Research Areas

Integrative Research Areas

Publication

Journal of Building Performance Simulation

Volume

13

Issue

3

First Page

347

Last Page

361

ISSN

1940-1493

Identifier

10.1080/19401493.2020.1729862

Publisher

Taylor and Francis Group

Additional URL

https://doi.org/10.1080/19401493.2020.1729862

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