The concept and implementation of Smart Cities is an important approach to improve decision making as well as quality of life of the growing urban population. An essential part of this is the presentation of data from different sources within a digital city model. Wind flow at building scale has a strong impact on many health and energy issues in a city. For the analysis of urban wind, Computational Fluid Dynamics (CFD) has become an established tool, but requires specialist knowledge to prepare the geometric input during a time-consuming process. Results are available only as predefined selections of pictures or videos. In this article, a continuous, semi-automated workflow is presented, which ❶ speeds-up the preparation of CFD simulation models using a largely automated geometry optimization; and ❷ enables web-based interactive exploration of urban wind simulations to a large and diverse audience, including experts and layman. Results are evaluated based on a case study using a part of a district in Stuttgart in terms of: ➀ time saving of the CFD model preparation workflow (85% faster than the manual method), ➁ response time measurements of different data formats within the Smart City platform (3D Tiles loaded 30% faster than geoJSON using the same data representations) and ➂ protocols (3DPS provided much higher flexibility than static and 3D container API), as well as ➃ subjective user experience analysis of various visualization schemes of urban wind. Time saving for the model optimization may, however, vary depending on the data quality and the extent of the study area.

The assessment of regional bioenergy potentials from different types of natural land cover is an integral part of simulation tools that aim to assess local renewable energy systems. This work introduces a new workflow, which evaluates regional bioenergy potentials and its impact on water demand based on geographical information system (GIS)-based land use data, satellite maps on local crop types and soil types, and conversion factors from biomass to bioenergy. The actual annual biomass yield of crops is assessed through an automated process considering the factors of local climate, crop type, soil, and irrigation. The crop biomass yields are validated with historic statistical data, with deviation less than 7% in most cases. Additionally, the resulting bioenergy potentials yield between 10.7 and 12.0 GWh/ha compared with 13.3 GWh/ha from other studies. The potential contribution from bioenergy on the energy demand were investigated in the two case studies, representing the agricultural-dominant rural area in North Germany and suburban region in South Germany: Simulation of the future bioenergy potential for 2050 shows only smaller effects from climate change (less than 4%) and irrigation (below 3%), but the potential to cover up to 21% of the transport fuels demand in scenario supporting biodiesel and bioethanol for transportation.