Promotor / Supervisor
Samenvatting van het onderzoek / Summary of Research
Introduction / Objective
Urban building energy models allow to quantify the energy use of existing buildings on district or urban level, enabling to analyse the optimal design and operation of district energy systems amongst others. However, to quantify the energy use, a multitude of input data are required and are rarely available, causing a significant uncertainty on the model results. Therefore, the main objective of this work is to quantify the impact of uncertainty on the building energy use within districts as a result of the epistemic and aleatory uncertainty of input data within urban building energy modelling.
The methodology consists of three steps:
Analysing the uncertainty of input data based on an in-depth data analysis – e.g. a probabilistic method to allocate the U-values of walls, windows, roof and ground floor and the window-to-wall ratio in a correlated way is proposed
Assessing the impact of input data uncertainty on the building energy use – an uncertainty and sensitivity analysis of the simulated energy use for space heating and domestic hot water is performed based on a district of 615 dwellings
Examining the impact of building energy use uncertainty within districts for two applications – the optimal control of network flexibility within a district heating network and the renovation potential of an existing district are studied
Results & Conclusions
The need for more detailed and more qualitative data about the energy performance of buildings and districts is highlighted. It is found that the scarcely available data give rise to large input parameter variations on building level. As a result of these variations on building level and the variability between buildings, the building energy use for SH and DHW on district level is characterised by a large dispersion, both annually and in time.
The uncertainty in time is highly impacted by the occupant uncertainty. The uncertainty in space – i.e. on building level – is large, causing that district level decisions cannot be translated to building level action points. It is therefore highly recommended to include and to reduce uncertainty within urban building energy modelling.