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).

To assess the feasibility of district energy systems as well as to design them in an optimal way, district energy simulations are often deployed, requiring an accurate spatial and temporal quantification of the district energy demand. Geographical information models and systems can provide input data to quantify the district energy demand, but the available levels of detail (LOD) of these data vary significantly between regions. Therefore, this work investigates the usability of LOD1 and LOD2 representations as well as the impact of building geometry within district energy simulations, by quantifying the differences in geometrical and energy characteristics between five variants of LOD1 or LOD2 representations. The most detailed LOD2 representation is thereby used as a reference. The results show that the significantly decreasing accuracy using LOD1 models may be compensated by assuming the roof shape from regional statistics. Also, aggregation of wall and roof components into a limited number of orientations significantly reduces simulation time, while maintaining the accuracy. It is concluded that geographical information models contain a significant amount of useful data, but the error that results from the deployed level of detail must be kept in mind when assessing the simulation results.

This paper presents the first steps towards a District Energy Simulation Test (DESTEST), which is part of IBPSA Project 1. The goal is to develop a test sequel for district energy simulations, inspired by principles of the BESTEST. It aims at providing a means to validate District Energy System models. The description of the DESTEST cases and the simulation results of extensively verified models will be available as a reference for verification. By presenting the research plan, goal and first results, the district energy simulation community is informed about the project's intentions, offering a chance for feedback and collaboration.