Efficient Solar Potential Estimation of 3D Buildings: 3D BAG as use case

Master thesis (2022)

Authors

R.R.J. Hurkmans Architecture and the Built Environment

Contributors

H. Ledoux Urban Data Science - Architecture and the Built Environment (supervisor 1)
J.E. Stoter Urban Data Science - Architecture and the Built Environment (supervisor 2)
S. Vitalis Urban Data Science - Architecture and the Built Environment (supervisor 2)
Faculty
Architecture and the Built Environment
Copyright: © 2022 Robin Hurkmans
More Info
expand_more

Published Date

11-11-2022

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Architecture and the Built Environment

Abstract

Solar energy is an important renewable energy source that is already generated by millions of solar panels attached to building roofs throughout The Netherlands. Whether a roof is suitablefor solar panels relies on its type, orientation and whether it is put in shadow by a neighbouring object. This means, the higher the solar potential of a roof, the better.

A solar radiation model is used to determine the solar potential of a roof. Well-known Geographical Information Systems, such as ArcGIS and GRASS GIS provide solar radiation models for raster data. However, raster data does not model the 3D urban environment accurate enough. Vector data is better capable of representing 3D buildings models, but solar radiation models for vector data are not widespread available and are computationally inefficient, because of the shadow casting step.

This research aims at providing an efficient solar radiation model for processing large-scale 3D city models. The 3D BAG data set, containing all the buildings in The Netherlands as vector data, is taken as use case. Their building models are stored in CityJSON format, subdivided into smaller tiles based on the spatial extent. The implemented model in this research, called SolarBAG, takes one or multiple tiles as input, processes the building geometries to compute the solar potential, and outputs an enriched CityJSON file where each building roof consists of yearly solar potential values. Within the process, the building geometries are stored in an Rtree to allow fast retrieval when filtering neighbouring buildings that potentially cast a shadow over another building. The roofs of buildings are sampled into a grid of points to account for the variable solar radiation values on a roof surface caused by shadows of neighbouring buildings. A ray-box intersection method is used to find the neighbouring buildings casting a shadow on another building.

To assess the quality and scalability of the implemented solar radiation method, experiments are conducted. For the quality assessment, the solarpy module used to compute the beam solar radiation, is compared to ground truth values, and the solar radiation model is compared to the solar radiation tool in ArcGIS. For the scalability assessment, the solar radiation model is tested for an increasing number of tiles. Based on the assessments, it can be concluded that the implemented solar radiation model can successfully enrich building roofs with solar potential values for one or multiple CityJSON files. However, there are still some bugs and inconsistencies present in the solar radiation model, and performance gains could still be achieved by neighbour filtering improvements.