In recent years, the need to reduce the global warming of the planet has become more imperative than ever. Global warming and, at local scale, Urban Heat Island phenomena are among the primary effects of the increased building carbon emissions. Nevertheless, understanding and controlling the parameters which intensify, or mitigate, the increasing in temperatures in the surroundings of a building are pivotal, as sustainable design can significantly reduce the buildings’ energy demand. Micro-climate simulations can provide more accurate input for building energy simulations since they can accurately simulate the interactions between those parameters to calculate detailed weather data. Despite the increasing knowledge about the significance of the microclimate, energy simulation users still rely on derived, or interpolated weather data from sparsely located weather stations, located generally outside the urban environment. The reason behind this commonly adopted approach is that the generation of microclimate data is costly in terms of time, and currently standards for storing this generated data have not been developed. ENVI-met is a microclimatic simulation software that requires a model of an urban area and weather parameters on its boundaries to generate a large extent of data like air, temperature, relative humidity, wind speed etc. Constructing this model manually contains a number of significant limitations, such as high design cost in time and need for data collection from different sources – thus the chance of design errors is high. In this thesis a novel approach is introduced where the ENVI-met software is used for microclimatic simulations at district scale. However, the input model in this case is created by data extracted from a CityGML-based 3D city model. In addition, the generated microclimatic data is stored back to CityGML, where it can be re-used. The proposed methodology is implemented via a Graphics User Interface, divided in two main phases, serving the required bi-directional data flow. It was designed and implemented based on the following specifications: i) the user involvement in the whole process needs to be minimum, ii) the interface should create simulation-ready input models of various resolutions and iii) it must work with different CityGML datasets.
A data requirement analysis indicated that a CityGML-based city model can feed its data to ENVI-met by the interface, so that the input model required by ENVI-met can be constructed fully automatically. In return, the storage of the generated results data is also possible. Therefore, an automated data flow between a CityGML-based city model and ENVI-met can be achieved, offering the following advantages: i) the ENVI-met input model can be constructed fast and automatically and ii) ENVI-met outputs can be translated to real world coordinates – thus can be visualized and processed in GIS software and ultimately stored back into the CityGML-based 3d city model.

In urban planning, one of the common units of measurement for population density is the amount of households per hectare. However, the actual size of the households is seldom considered, neither in 2D nor in 3D. This thesis proposes a method to calculate the average size of the households from existing urban areas based on available open data and to use it as a design parameter for new urban developments. The proposed unit of measurement is comprehensive of outdoor and indoor spaces, the latter comprising both residential and non-residential space. As a test case, the Haven-Stad project in Amsterdam was chosen, specifically its second phase, Sloterdijk One, envisioned by the municipality of Amsterdam to host 11220 households and 7480 working places by 2040. The sizes of typical households, as well as a series of other KPI’s (Key Performance Indicators) were computed in different neighbourhoods of Amsterdam based on their similarities with the vision of Sloterdijk One. In the second part of this thesis, the resulting size of the households were used as a design parameter in a custom-made prototype tool to generate semi-automatically several design proposals (called scenarios) for urban developments. Additionally, each scenario can be converted into a Geo format to visualize it in web-based virtual platforms such as Google Earth and Cesium JS. Significant differences among the resulting design proposals based on this new unit of measurement were encountered, meaning that the average size of a household plays indeed a major role.

To cut down immense greenhouse gases emission and energy consumption in the rapidly urbanizing world, a holistic understanding and rethinking of our dynamic urban energy system are inevitable. Performing bottom-up building energy modelings at urban scale based on Geographic Information System (GIS) and semantic 3D city models could be a promising option to provide quantitative and integrated energy solutions.

Nevertheless, input uncertainties either caused by limited data accessibility in most cities or parameters with stochastic variability (e.g. house occupancy profile) become one of the biggest obstacles to produce reliable and acceptable building energy modeling results. This study aims to address the heating demand simulation performance gap caused by input uncertainties. In this case study based on Amsterdam residential building stock, parameter importance ranking of the 14 simulation inputs are first derived according to the sensitivity analysis. The selected key uncertain parameters are then modeled in a probabilistic distribution way at postcode 6 level (approximately or slightly more than 10 buildings). Model calibration is based on the Bayesian approach and given six years (2010-2015) of gas consumption data to infer parameter posterior distributions. After the training phase, the calibrated annual heating demand simulation results of the validation years show significant improvement in modeling accuracy. Comparing the baseline and the calibrated simulation results, the averaged absolute percentage errors of energy use intensity (EUI) among at least 84 valid postcodes have decreased from 24.96% to 8.31% in 2016 and from 19.93% to 7.70% in 2017 respectively.

The calibrated urban building energy model would be most interested by municipalities, urban planners, and engineering consultancies. It can be used to evaluate long-term energy supply and demand strategies, identify building renovation saving potential, perform large-scale building performance mapping, and carry out retrofit measures assessment.