Exploring uncertainty in district heat demand through a probabilistic building characterization approach

Rui Guo, Mohammad Haris Shamsi, Mohsen Sharifi and Dirk Saelens

Applied Energy, 2025, vol. 377, issue PA, No S030626192401794X

Abstract: As urban areas continue to expand, understanding and managing the uncertainties in district energy demand becomes crucial for sustainable development. This study employs a probabilistic approach to characterize the building thermal performance parameters of U-values and window-to-wall ratios using the Flemish energy performance certificate dataset, enhancing the accuracy of energy demand forecasts in urban districts. By utilizing the linear quantile regression approach, this study identifies the optimal combination of explanatory variables (e.g., postal code, roof area) of dwellings to estimate the marginal distributions (without considering correlations between parameters) of the building parameters. Using these marginal distributions, the C-vine copula method proved to be the best for developing multivariate distributions by comparing various copula methods to capture the correlations between building parameters. This robust framework can accurately estimate realistic distributions of building parameters, which are then used to analyze the impact of uncertainty in building parameters on energy demand at dwelling and district levels through building energy modeling. The findings emphasize the importance of considering correlations between building parameters to improve the accuracy of energy demand forecasts. Discrepancies in the heat demand probability distributions at both the dwelling and district levels occur if these correlations are overlooked. Furthermore, the effect of stochastic residential occupant behavior on energy demand is also considered in the simulation. The results show that occupant behavior introduces more variability in heat demand than the building parameters. The findings advocate for integrating the probabilistic building characterization approach into urban building energy modeling, enabling more accurate energy demand forecasting with low-quality data like energy performance certificates and supporting the planning and design of sustainable and energy-efficient urban environments.

Keywords: Probabilistic building characterization; Quantile regression; Copula methods; Building energy modeling; Energy demand forecasting (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S030626192401794X
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:appene:v:377:y:2025:i:pa:s030626192401794x

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2024.124411

Access Statistics for this article

Applied Energy is currently edited by J. Yan

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