A probabilistic building characterization method for district energy simulations

A probabilistic building characterization method for district energy simulations

De Jaeger, Ina (Flemish Institute for Technological Research; Katholieke Universiteit Leuven; EnergyVille)
Lago, Jesus (TU Delft Team Bart De Schutter; Flemish Institute for Technological Research; EnergyVille)
Saelens, Dirk (Katholieke Universiteit Leuven; EnergyVille)

2021

To assess the impact of implementing energy efficiency and renewable energy measures, urban building energy models are emerging. In these models, due to the lack of data, the natural variability of the existing building stock is often highly underestimated and uncertainty on the simulated energy use arises. Therefore, this work proposes a probabilistic building characterization method to model the variability of the existing residential building stock. The method estimates realistic distributions of five input variables: U-values of the floor, external walls, windows and roof as well as window-to-wall ratio, based on known data (location, geometry and construction year). First, quantile regression has been implemented to generate the uncorrelated distributions based on the Flemish energy performance certificates database. The accuracy of the marginal distributions is good, as the empirical coverage on the 50%, 80%, 90% and 98% prediction interval deviates 0.6% at most. However, it is needed to include the correlations between these variables. Hence, three main methods to build multivariate distributions from marginal distributions and to draw correlated samples are implemented and extensively compared. The Gaussian copula method is put forward as the preferred method. Considering the mean-maximum discrepancy (MMD), this method performs eight times better than the uncorrelated case (MMD of 0.0027 versus 0.0228).

District energy simulation
Input data
Multivariate probability distribution
Scenario generation
Uncertainty
Urban building energy modelling

http://resolver.tudelft.nl/uuid:da8d625a-2949-4609-95fa-3d792c7e7e13

https://doi.org/10.1016/j.enbuild.2020.110566

2021-04-16

0378-7788

Energy and Buildings, 230

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

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journal article

© 2021 Ina De Jaeger, Jesus Lago, Dirk Saelens