Changes in social, cultural, and economic paradigms have resulted in a collective shift from rural to urban areas over the last few decades. Cities are expanding to accommodate the ever-increasing population influx in urban areas. New buildings, roads, airports, and harbors are only a small part of urban development. It also includes the use of new agricultural lands and changes to water resources surrounding urban areas. These new developments invariably introduce complex geometry onto surfaces as well as a plethora of new materials into a field, which leads to the change in local albedo. Albedo indicates how well a surface reflects solar energy, and it is defined as the ratio of total reflected irradiation from a surface to total incident irradiation on the surface. Albedo has a wide range of applications such as estimating the total heat content of an area and assessing the global warming potential. In this project, we used our Geometric Spectral Albedo model developed in the PVMD research group and open source LiDAR data (AHN1 to AHN4 collected over roughly 25 years) to investigate how urban development affects the local albedo.

Integration of PV into urban environments will play a major role in the transition towards a more sustainable energy supply. However, PV installation on roof surfaces of buildings is not always easy. One of the challenges comes from the fact that monumental buildings are highly restricted on changing their appearance because of the social value of their architectural features. Visibility assessment of the roof surfaces of these monumental buildings to the public domain is therefore key to finding as much permitted roof surface area as possible that may be used for PV installation. This project aims to develop a large-scale visibility assessment tool that can assist in assessing whether a surface is visible, as well as assessing how visible it is. Additionally, this tool is combined with a module yield estimation algorithm to find the annual DC yield potential of the total permitted roof surface area.

The visibility assessment tool operates in a raster-based representation of the environment with a resolution of 0.5 by 0.5 meters, generated using the openly available height point cloud of the Netherlands (AHN3) along with cadastral data (BAG) and road network data that represents the public domain. The output of this tool contains multiple maps that include binary visibility (classifying whether a surface is visible or not), domain visibility (quantifying the length of visible roads), the average solid angle (quantifying the average relative size of the surface to the visible roads) and total visibility (a multiplication of domain visibility and average solid angle). To reduce the computational demand of this large-scale assessment algorithm, the amount of operations was reduced using an observer filter based on surface tilt and orientation. Additionally, to save time, a sensitivity analysis is included where the assessment terminates once the optimal assessment range and observer spacing is found for individual building roof surfaces. Given the raster resolution, the visibility assessment takes on average 0.619 s/m^2 for estimating all properties, or 0.063 s/m^2 if only binary visibility is needed.

The visibility assessment tool was cross-validated using the ArcGIS Pro software. It was found that the binary visibility accuracy of all analyzed buildings combined hit 99.70%. In the case of domain visibility, for all roof surfaces that were found visible by both models, 90.99% of surface cells have a domain visibility accuracy of above 90%, while 0.89% of surfaces have a domain visibility accuracy below 50%. The tool has been implemented on 15 monumental buildings on the TU Delft campus, doing visibility assessment on their building roof surfaces. 46563m^2 out of 62350m^2 of cumulative total roof surface area was found to be invisible. The invisible roof surface area has the potential to deliver a total of 4.743 GWh annual DC yield, assuming an economic threshold for specific yield of 650 kWh/kWp and a roof-integrated, fully covering layout of the modules on the found suitable roof surface area.