Solar Energy Potential Assessment for entire Cities - a reusable and scalable approach

The recent technological advances in massive geospatial data collection assessing both the temporal and spatial dimensions of data add significant complexity to the data analysis process, provides new dimensions for data interpretation. Accordingly, geographical information systems (GIS) must evolve to represent, access, analyze and visualize big spatiotemporal data in a scalable integrated way. Often, sharing and transferring of such information through deep-dive and automated analysis cause scalability challenges at the software level that impacts the overall performance, throughput, and other performance parameters. In this paper, we demonstrated the whole implementation explaining some practical steps to scale solar irradiation calculations for entire cities at very high space-time resolution by using scalable tensor data structure and inherent parallelism offered by data-flow based implementation. We attempt to improve the understanding of the underlying equations and data structures from an analytical, a geometric and a dynamical systems perspective. The entire model is implemented in Tensorflow, an open source software library developed by the Google Brain Team using data flow graphs and the tensor data structure. To assess the performance and accuracy of our TensorFlow based implementation we compared to the well known r.sun from GRASS GIS and PVLIB from National Renewable Energy Laboratories (USA) implementation for solar irradiation simulations. Results show that we achieved noticeable and significant improvements in overall performance keeping accuracy at negligible differences.