Optimisation models used to forecast solar photovoltaic capacity installation can leverage high-quality data, including rooftop/facade space availability, future technology costs, and uncertainty of key parameters to help decision-makers obtain meaningful and robust solutions.

Using Singapore as our test case, our research shows that, without accurate information, such as the risk-aversion of stakeholders, model-based predictions can be unrealistic. For example, in theory all the available space could be used to install photovoltaic systems at the most convenient time period. But in reality, when including risk-aversion, we see that installation actually happens incrementally over each time period, which gives us a much more realistic result. Model results can also be used to help us come up with the incentives necessary to achieve solar capacity installation targets.

Solar power is intermittent and most often non-dispatchable. In addition, high penetration PV leads to the duck curve problem which hinders the deployment of photovoltaics. City-scale mobile phone data can be leveraged to understand where and when the energy can be stored in electric vehicles. This will also improve the energy quality and stability.

Our study aims to use mobility patterns to understand where and when the energy coming from photovoltaics can be stored in electric vehicles with V2G technology so as to decarbonise the city. Through analysing city-scale individual mobility data, we can see the charging demand and storage capacity needed in very fine temporal and spatial detail.

When looking at flooding, we integrate a number of different models, each having quite different levels of risk. One of our colleagues, for example, will use a ‘remote-sensing’ approach to understand the dynamics of urbanization and to see where key ecological infrastructures could be used to limit the risks associated with flooding. We can also use LIDAR technology, to understand the different structures of green spaces found in cities. Having different sources of data is a good start, but it is also important to integrate these different sources of information into an integrated framework. For this reason, we use point cloud models of cities. We use point cloud models to integrate flood risks and other sustainability aspects into integrated designs.

Simulation of development strategies and planning procedures enables us to predict future challenges, potential solutions, and possible outcomes, thus enhancing communication and discussion among stakeholders with a panoramic view.

Data can be leveraged to develop sustainable cyber-physical urban environments using advanced analytics and modelling techniques. Digital twins, which are virtual replicas of physical environments, can be used to simulate and test the impact of various sustainability initiatives before they are implemented in the real world. To create a digital twin at the urban scale, we are proposing a resource cadastre, where information from images, laser scans or other sensor devices which are increasingly becoming freely accessible at large scales can be aggregated (in this figure Spatial distribution of building materials for a subset of buildings in Zurich, Switzerland). A detailed inventory of materials in each building, combined with digital twin technology, can enable the creation of a circular economy where waste is minimized, and resources are optimized. This approach can leverage cyber-physical concepts to design, build, and manage sustainable urban environments that balance social, economic, and environmental aspects, paving the way for a more resilient and sustainable future.