Solar shading and multi-zone thermal simulation: Parsimonious modelling at urban scale

Abstract

With the growing interest in urban scale simulation, urban building energy modelling tools (UBEM) are being developed. The purpose of these UBEM tools is to tackle multiple issues that can range from buildings retrofit potential to the sizing of thermal and/or electrical network or to the assessment of renewable energy sources potential. Unlike the modelling of a single building, collecting accurate building information data at district scale is nearly impossible and therefore the uncertainties increase. In this context, the selection of the most relevant and adequate models with regard to the available data and simulation objectives is challenging, and often a trade-off between accuracy and parametrization feasibility has to be found. A novel approach is proposed to choose the right level of model complexity for thermal heating demand simulation in buildings at district or city scale, by assessing in particular the impact of different thermal zoning methods with the UBEM tool DIMOSIM. This method for assessing the parsimony in modelling is applied to a selection of models from the literature in order to find a compromise between the available data, the different modelling levels of detail, the expected output and the computation time, according to district and buildings characteristics. By applying this approach, it has been found that a division per floor for building modelling is in most cases the most parsimonious choice for thermal heating demand simulation at district scale. Eventually, the coupling between thermal zoning models and solar shading models is analysed through the same methodology for assessing the modelling parsimony. The results show that the combination of simpler models (in particular a thermal zone per floor or mono-zone buildings for thermal zoning models, and a solar shading coefficient calculated per floor or facade for solar models) can be parsimonious enough to be used to determine the annual heating demand, speeding up strongly the simulation at the same time.