University of Twente Student Theses


Investigating 3D input data for solar photovoltaic potentials in The Netherlands

Amiranti, Arsha Yuditha (2020) Investigating 3D input data for solar photovoltaic potentials in The Netherlands.

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Abstract:Nowadays, the usage of 3D models extends beyond visualization purposes, serving as a representation to analyze the real world. Kadaster (the Dutch Land Registry and Mapping Agency) is interested in utilizing 3D models for different applications. This study aimed to explore the possibility to integrate two different point clouds to produce a unified dataset as the input data for 3D model generation that can suit many applications. The suitability of this dataset is tested on a use case of estimating solar photovoltaic analysis. This study used a mixed qualitative-quantitative method to gather and process the data. In this research, we used the LiDAR point cloud and point cloud derived from a dense image matching (DIM) technique. To gauge the perspectives of the users, we conducted semi-structured interviews and a focus group discussion. Our study found that the main problem when performing data integration is to correctly and accurately integrate the datasets when those datasets have different accuracy, density, and properties. The foundation to determine the quality of the 3D model is to assess the quality of the input data. Following three out of the six elements of data quality from ISO 19157: 2013 (ISO, 2013), we used completeness, temporal quality, and positional accuracy to determine the quality of the input data. These elements were used because those elements have a significant impact on the geometric aspect of 3D data. We integrated the LiDAR point cloud and the DIM point cloud using the Iterative Closest Point (ICP) algorithm. The major advantage of integrating these two point cloud datasets is to improve the temporal quality, completeness, and positional accuracy. During the semi-structured interview, these three factors were identified as the inadequacy of the quality of the currently used input data. We generated 3D models of 48 buildings semi-automatically using the integrated point cloud, building footprints and manually extracted rooflines using the RANSAC algorithm. The integrated point cloud and the 3D models were both converted into a digital surface model (DSM) as input data for solar photovoltaic potential. Several criteria were applied to determine the potential areas for solar photovoltaic installation that were identified during the semi-structured interview: roof slope, roof orientation and minimum threshold for solar irradiation. To assess the benefit of using the 3D model as input data for solar photovoltaic analysis, we compared the result from the two input data models. From the result of the experiment, the calculation results of the solar photovoltaic potential are different between the input data models. When using the converted 3D models as input data, the roof details are generalized and noise is removed. The details and noise remained when using the integrated point cloud DSM as input data for the analysis. According to the result of the group discussion, using a 3D model as input data for the solar photovoltaic potential analysis could avoid noise and data gaps. The discussion revealed a hidden benefit and perception from users when using the 3D model, that people prefer to view a representation of reality which 3D can provide for them. Therefore, these findings provide a new understanding that the solar photovoltaic analysis benefits from using the 3D model as the input data and as the visualization for the output.
Item Type:Essay (Master)
Faculty:ITC: Faculty of Geo-information Science and Earth Observation
Programme:Geoinformation Science and Earth Observation MSc (75014)
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