A quick-scan yield prediction method has been developed to assess rooftop photovoltaic (PV) potential. The method has three main parts. For each roof, first (i) virtual 3D roof segments were reconstructed using aerial imagery, then, (ii) PV modules were automatically fitted onto roof segments using a fitting algorithm and finally, (iii) expected annual yield was calculated. For each roof, the annual yield was calculated by three different quick yield calculation approaches. Two approaches are commercial software packages of Solar Monkey (SM) and Photovoltaic Geographical Information System (PVGIS) whereas the other one is the simplified skyline-based approach developed in photovoltaic material and devices (PVMD) group of Delft University of Technology. To validate the quick-scan method, a set of 145 roofs and 215 roof segments were chosen in urban areas in the Netherlands. For the chosen roofs, the number of fitted modules and calculated yield were compared with the actual modular layout and the measured yield of existing PV systems. Results showed a satisfactory agreement between the quick-scan yield prediction and measured annual yield per roof, with relative standard deviations of 7.2%, 9.1%, and 7.5% respectively for SM, PVGIS, and PVMD approaches. It was concluded that the obstacle-including approaches (e.g. SM and PVMD) outperformed the approaches which neglect the shading by surrounding obstacles (e.g. PVGIS). Results also showed that 3D roof segments had added value as input for the quick-scan PV yield prediction methods since the precision of yield prediction was significantly lower using only 2D land register data of buildings.

Knowledge of people's locations and related mobility patterns are important for many decision-making processes. How to efficiently use the available space, is a common problem in many fields. Wireless Local Area Networks (WLAN) are widely used for locating mobile devices within this network. This study attempts to identify movement from Wi-Fi log data on the Delft University of Technology campus. The proposed method automatically explores people’s movement by firstly, extract stay places, secondly discover movement and finally, identify movement patterns. This method is studied for two spatial levels: (1) at building level, movement between, from and to buildings can be detected, (2) at building-part level, movement between, from and to large indoor regions can be detected. For indoor analysis, the travelled path is estimated using a network graph of the underlying floorplan. This paper shows promising results for mining people’s movement patterns between buildings and indoor building-parts.