A Bayesian approach with urban-scale energy model to calibrate building energy consumption for space heating: A case study of application in Beijing

Wei Na and Mingming Wang

Energy, 2022, vol. 247, issue C

Abstract: Urban-scale energy model can be a crucial tool for evaluating and in turn propelling the implementation of energy efficiency and low carbon programs for buildings and cites. A variety of disputes on integrity and correctness lie in basic statistical data of China's building energy use. Inadequate information and performance gap caused by input uncertainties are the common obstacles to produce reliable energy modelling results. This paper develops a bottom-up approach to depict energy use intensity (EUI) for space heating of building in the urban level accurately, even if the data resource is inadequate and imprecise or the data are influenced by uncertain issues. A hybrid probability model is developed and the model parameters are calibrated by Bayesian inference and Markov Chain Monte Carlo simulation using data set of meter reading from 2062 stochastic-sampled heating substations in Beijing. Preparation and simulation efforts of using this energy model are discussed, following this framework and adjusting according to local climate conditions, typological building characteristics and morphological urban-scale parameters. The results show that the approach is reliable and efficient to calibrate parameters in building energy models. The urban-scale benchmarks of EUI for space heating in Beijing is successfully performed to demonstrate the proposed methodology.

Keywords: Building energy use intensity for space heating; Parameter calibration; Uncertainty analysis; Bayesian inference; Monte Carlo Markov chain (MCMC); Urban scale energy model (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544222002444
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:247:y:2022:i:c:s0360544222002444

DOI: 10.1016/j.energy.2022.123341

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().