Solar Energy Potential Assessment On Façades Using Geo-referenced Digital Elevation Models
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In modern city centres, which are comprised of tall buildings with limited rooftop space, installing solar energy technologies on the facades can effectively respond to the current barriers to their deployment on rooftops. However, since there is a dearth of efficient façade solar potential assessment models, feasibility analysis of such projects within a reasonable computation time has become a major challenge. Area-based geographic solar potential assessment models are commonly employed in such environments. They use Digital Elevation Models (DEM) that contain the precise geo-referenced elevation. A comprehensive literature review has shown that these models use an approach that first requires disintegration of the façades into a large number of virtual surfaces. Then, each of these surfaces is analyzed, involving a large amount of computation time. Also, these models do not use skymaps (pre-processed solar radiation data) and the management of computational processes together. These two approaches combined were found to be very useful in reducing the analysis time in models for rooftop solar potential assessment. This research gap in the literature indicated a need to develop a façade solar potential assessment model that completely avoids façade disintegration and incorporates skymaps and management of computational processes. Hence, this research focused on developing such a novel model and comparing its performance with the existing model. For the purpose, the proposed model was broken down into four sub-models. The first was the discretization-independent scanning algorithm, which takes into account the DEM and the sun position and provides details of shadows on the facades. The results from this sub-model were compared with results obtained from a 3D geometric model developed in the Google SketchUp program and were found to be in good agreement with each other. Then, these results were fed into the other three sub-models, which evaluated the beam, diffuse and anisotropic diffuse solar potential, respectively. On analyzing a hypothetical layout to yield results at various levels of detail, the performance of the developed model in terms of accuracy and speed was found to be far better than the existing model. Also, incorporation of scalable architecture using multi-processing and cloud-computing drastically improved the speed. The results showed very close agreement when the AUT city campus, 80,000 m2 in area, was analysed. The use of the proposed model to identify suitable locations for installing solar energy technologies on the facades of AUT buildings was also presented. In summary, the proposed model has shown remarkable performance in terms of speed when compared with the conventional model. With the help of this model, solar potential assessments for façades can be performed at much faster speeds than existing models.