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.

We are now gradually entering the era of big data - maybe a bit too much of a buzzword, but it is not lied. Technology is evolving fast, enabling faster and more efficient data acquisition, storage, retrieval and processing. Point cloud datasets are such a type which relies on large files and lots of processing power. The rather fast evolutions in technology enable the shared idea between Delft University of Technology and Fugro of an ‘Open Point Cloud Map’. This Open Point Cloud Map aims at making point cloud datasets easily available to the public, even letting them performsimple analysis. Both Fugro and TU Delft want to take lead in development of such an environment; three student teams from TU Delft thus form a partnership with Fugro to kick-off three in-depth researches which would result in one step closer to the vision of OPCM. The ChronoCity team will focus on the time-component (Figure 1.1) in the acquainted point clouds, while the other two teams focus on location-dependency and different scales and granularities of the datasets. The Chronocity-team strives to create an online interactive tool which gives the user the ability to view, explore and analyze massive point cloud datasets on-the-fly. Since the limited timespan in which the project should take place this would not yield a fully optimized application, but at least the general principles are defined and evaluated on for a more defined future in the development of the OPCM. A large portion of the efforts will go into making the data and analyses available to the public - in an interactive and user-friendly way; because without this availability, the underlying principles are not brought to the public also. Regarding these underlying principles the most important one is change detection. During the project a suitable algorithm is designed and evaluated for detecting new, removed and changed geometric points.