Figure 12 - available via license: Creative Commons Attribution 4.0 International
Content may be subject to copyright.
Tested points with the trained SVM model. Red points are classified as covered and the yellow points are classified as uncovered. Black points are the projected DSM points.
Source publication
Accurate simulation of solar irradiance on facades and roofs in a built environment is a critical step for determining photovoltaic yield and solar gains of buildings, which in an urban setting are often affected by the surroundings. In this paper, a new modelling method is introduced that uses Digital Surface Model (DSM) point clouds as shading ge...
Citations
... With the involvement of different stakeholders, geographical coverage, and proliferation of an increasing breadth of use cases, 3D city models are scattered around the world and developed in disconnected initiatives, which leads to heterogeneous characteristics (Bognár et al., 2021). Currently, governments are the major provider of 3D city models, but efforts by universities and research institutions also present a fair share in contributing to the availability of datasets, while crowdsourcing has been gaining currency as well (Wu & Biljecki, 2022). ...
3D city models are omnipresent in urban management and simulations. However, instruments for their evaluation have been limited. Furthermore, current instances are scattered worldwide and developed independently, hampering their comparison and understanding practices. While there are developed assessment frameworks in open data, such efforts are generic and not applied to geospatial data. We establish a holistic and comprehensive 4-category framework '3D City Index', encompassing 47 criteria to identify key properties of 3D city models, enabling their assessment and benchmarking, and suggesting usability. We evaluate 40 authoritative 3D city models and derive quantitative and qualitative insights. The framework implementation enables a comprehensive and structured understanding of the landscape of semantic 3D geospatial data, as well as doubles as an evaluated collection of open 3D city models. For example, datasets differ substantially in their characteristics, having heterogeneous properties influenced by their different purposes. There are further applications of this first endeavour to standardise the characterisation of 3D data: monitoring developments and trends in 3D city modelling, and enabling researchers and practitioners to find the most appropriate datasets for their needs. The work is designed to measure datasets continuously and can also be applied to other instances in spatial data infrastructures.
... Solar energy has the potential as a renewable energy source as it can be used to efficiently heat water and generate power in buildings Asfour, 2018;Kibaara, Ng, Mithraratne, & Kua, 2013;Ekoe A Akata, Njomo, & Agrawal, 2017;Skandalos & Tywoniak, 2019). Limited research has been undertaken to assess the impact of PVSD on the energy efficiency of buildings, encompassing factors such as PV electricity production and the energy consumption associated with building heating, cooling, and lighting (Al Garni, Awasthi, & Wright, 2019;Bognár, Loonen, & Hensen, 2021;Fernández-Ahumada, Ramírez-Faz, López-Luque, Márquez-García, & Varo-Martínez, 2019;Noorzai et al., 2023;Redweik, Catita, & Brito, 2013;Sun, Heng, Tay, Chen, & Reindl, 2021). The methodologies used in these studies were experimental and/or computational. ...
... Raytracing, integrated in Radiance daylight simulation engines, is the dominant method but requires an accurate surface model of the scene as input, making it difficult to be applied to complex urban areas (Tian et al., 2022). In contrast, the computational point cloud projection method, used by the Pyrano simulation engine (Bognár et al., 2021), is more flexible for urban applications as it only requires point cloud data as input and no additional effort to obtain accurate surface models. However, a major limitation of 2-phase methods is their tendency to treat all types of obstructions identically and predict shading impacts using fixed simulation hyperparameters, like the Radiance ambient parameters in raytracing-based simulation (Kharvari, 2020), and the engine-based parameters (gamma, C) required in Pyrano calculation (Bognár et al., 2021). ...
... In contrast, the computational point cloud projection method, used by the Pyrano simulation engine (Bognár et al., 2021), is more flexible for urban applications as it only requires point cloud data as input and no additional effort to obtain accurate surface models. However, a major limitation of 2-phase methods is their tendency to treat all types of obstructions identically and predict shading impacts using fixed simulation hyperparameters, like the Radiance ambient parameters in raytracing-based simulation (Kharvari, 2020), and the engine-based parameters (gamma, C) required in Pyrano calculation (Bognár et al., 2021). This approach introduces extra epistemic uncertainty into the results, which will be discussed further in the model development section of this paper. ...
... The fraction of within a given sky segment is the exact cover ratio of that segment. Figure 4: Diagram of Pyrano cover ratio prediction on the discretized sky hemisphere, wherein black pointsprojected obstruction point cloud; red region-test points that predicted as being shaded; yellow region-test points that predicted as will not be shaded, adapted from (Bognár et al., 2021). ...
... Point clouds of trees can be directly used in modeling solar PV potential in urban areas [17][18][19]49] and on building surfaces [50,51] under tree shading impacts. Bognár et al. [52] proposed a comprehensive point cloud-based method for urban irradiance simulation that accounts for shading impacts from solar obstructions, including trees. Compared to (semi-)solid tree models that tend to simplify the shape of tree canopies, point cloud models preserve more detailed geometric information, such as leaf density and irregular organic shapes. ...
... Matrix-based methods are inherently suitable for detailed daylight modeling in 3D urban geometries and expedite long-term simulations [52,57]. Radiance [55] and Pyrano [52], employed by several reviewed studies [2,22,37,40,44,47,52,58], are considered the most representative state-of-the-art matrix-based methods. ...
... Matrix-based methods are inherently suitable for detailed daylight modeling in 3D urban geometries and expedite long-term simulations [52,57]. Radiance [55] and Pyrano [52], employed by several reviewed studies [2,22,37,40,44,47,52,58], are considered the most representative state-of-the-art matrix-based methods. Previous researches have demonstrated their superior performance in simulating direct and diffuse irradiance under dynamic sky conditions [37,[59][60][61][62]. ...
... DSMs are pre-processed point clouds that provide detailed elevation information of the urban landscape (Martha et al., 2010), which enable various applications in solar resource assessment (Teves et al., 2016;Bognár et al., 2021;Zheng et al., 2018;Tian et al., 2022). The value of DSMs for such applications can be enhanced with the use of (semantic) segmentation (Cao et al., 2019;Zhang et al., 2019), as it enables the identification of urban objects and their interrelationships. ...
... DSMs are pre-processed point clouds that provide detailed elevation information of the urban landscape (Martha et al., 2010), which enable various applications in solar resource assessment (Teves et al., 2016;Bognár et al., 2021;Zheng et al., 2018;Tian et al., 2022). The value of DSMs for such applications can be enhanced with the use of (semantic) segmentation (Cao et al., 2019;Zhang et al., 2019), as it enables the identification of urban objects and their interrelationships. ...
Deep learning-based segmentation of urban digital surface models (DSMs) endures challenges from limited features, class imbalance, and sparse data, which limits the application of DSMs in urban solar energy assessment. In this study we propose a dynamic graph CNN (DGCNN) based segmentation model and address abovementioned problems by adding artificial features, using adaptive-weighted loss function, and introducing a modified spatial transformation module. Our model achieved outstanding performance in predicting the test dataset with an average accuracy of 0.95 and F1 scores of 0.94 after 300 epochs of training. The presented approach inspired several potential applications for solar energy simulation. DSMs are pre-processed point clouds that provide detailed elevation information of the urban landscape (Martha et al., 2010), which enable various applications in solar resource assessment (Teves et al., 2016; Bognár et al., 2021; Zheng et al., 2018; Tian et al., 2022). The value of DSMs for such applications can be enhanced with the use of (semantic) segmentation (Cao et al., 2019; Zhang et al., 2019), as it enables the identification of urban objects and their interrelationships. While traditional analytical segmentation methods are effective for smooth and simple landscapes (Diab et al., 2022; Rizaldy et al., 2018), they struggle to handle the complexity of modern urban environments (Zhang et al., 2019). Recent research in urban landscape segmentation has focused on deep learning models, which offer advantages over traditional approaches in detecting small or non-intuitive features (Diab et al., 2022; Zhang et al., 2019). Among popular models, DGCNN stands out as particularly suitable for point cloud segmentation due to its computational efficiency and ability to capture local and global features (Wang et al., 2019; Xing et al., 2021). While DGCNN has shown promising results in various segmentation tasks (Diab et al., 2022; Gamal et al., 2021; Widyaningrum et al., 2021), its application to DSMs has been limited due to three main problems: • Limited features: DSMs have limited features, preserving only (x, y, z) coordinates, which may be inadequate for accurate predictions. • Class imbalance: Certain objects like building facades are underrepresented in DSMs, results in bias towards majority classes and lead to poor performance for minority classes (Sun et al., 2021). • Sparse data: Sparse data is common in DSMs of building facades (Yan et al., 2017), where few points in the region make it challenging for the model to learn robust features. This study proposes solutions to aforementioned challenges in accurately segmenting DSMs using DGCNN for urban solar assessment. We aim to segment DSMs into four classes: vegetation,
... The basis of this approach is the EnergyPlus shadow calculation module that allows to compute and export the sunlit fractions of each building's surface for each analysis timestep. Then, these exported data can be used in other analyses as a 'Schedule:File:Shading' EnergyPlus object (Bognár, Loonen, and Hensen 2021). The sunlit fraction (or sunlit area) describes the ratio between the unshaded area and the total area (unshaded + shaded) of each surface of the building and changes accordingly with the change of the sun position during the year (US Department of Energy 2021); hence, this ratio can range between 0 (fully shaded) and 1 (fully unshaded). ...
This paper proposes an innovative approach to analyse the energy behaviour of complex kinetic shading systems. Although several studies have analysed this topic, many are focused only on certain aspects or on simple shading systems due to a lack of tools for running reliable energy simulations on complex systems. This study aims to develop and validate a tool based on Python and EnergyPlus that can consider the continuous nature of the energy simulation and analyse complex kinetic systems. Simply providing an EnergyPlus model and a model of the shading configurations, the algorithm provides as output a comparison sheet to evaluate the performance of the system. The paper provides a description of the tools and studies focused on this topic; subsequently, a methodological insight is presented to explain the workflow, its validation, and the algorithm developed. Finally, the algorithm is tested on a case study to analyse a kinetic shading system.
... DSMs are widely available as many countries maintain open-source urban DSM databases. However, raw DSMs cannot be used for ray tracing operations due to the lack of defined surfaces [15,34]. Point clouds need to be processed and transformed into surface models that meet simulation requirements with the help of surface reconstruction algorithms [35]. ...
... DSMs consist of points that have been processed from raw LiDAR scanning data and resampled into a regular raster, with only one height z per x-y location [34,85]. Compared to full 3D point clouds, DSMs therefore tend to have a low number of points at vertical surfaces, which introduces difficulties when dealing with building facades. ...
Raytracing-based methods are widely used for quantifying irradiation on building surfaces. Urban 3D surface models are necessary input for raytracing simulations, which can be generated from open-source point cloud data with the help of surface reconstruction algorithms. In research and engineering practice, various algorithms are being used for this purpose; each leading to different mesh topologies and corresponding performance. This paper compares the impacts of four different reconstruction algorithms by investigating their performance using DAYSIM raytracing simulations. The analysis is carried out for five configurations with various urban morphologies. Results show that the reconstructed models consistently underestimate the shading influence due to geometrical shrinkages that emerge from the various model generation procedures. The explicit algorithms, with Generic Delaunay a notable example, have better performance with less embedded error than the implicit algorithms in both daily and annual simulations. Results also show that diffuse irradiance is responsible for larger contributions to the overall error than direct components. This effect becomes more prominent when modeling reflected irradiation in urban environments. Additionally, the work shows that solar elevation and shading geometry types also affect the error magnitude. The paper concludes by providing reconstruction algorithm selection criteria for photovoltaic practitioners and urban energy planners.
... Therefore, high levels of PV mismatch losses and high frequency PV power fluctuations are reported during partial cloudy conditions [16]. For distributed PV installations, the mismatch losses are further intensified due to the sharp shadows cast by surrounding obstacles in the field [17]. ...
While the performance of PV systems and its associated uncertainties are well understood for standard test conditions in the laboratory, there is still limited knowledge about the magnitude and mechanism of the PV performance variability under actual operating conditions. This paper aims to identify the performance variability between identical rooftop PV systems in the field and formulate risk mitigation strategies to reduce the error of annual yield prediction. To achieve the aim, long-term monitoring data of 246 identical rooftop PV systems in 19 sub-urban residential communities is analyzed. Through structured side-by-side comparisons, the mechanism of PV performance variations linked to location, module orientation, season, sky clearness, and system age is investigated. It is found that PV performance variability increases in the real built environment compared with the nameplate bandwidth declared by the manufacturers. Significant variation of PV operating performance is observed not only between different locations, but also between peer systems in the same neighborhood. Even in low-rise sub-urban settings, local shading and masking effects play a prominent role and can introduce great uncertainties. Due to the site-to-site and peer-to-peer uniqueness of PV performance, it is inappropriate to employ an identical empirical derate value for all cases. Commissioning and monitoring of PV systems in the field for at least one month with the largest range of solar elevation can significantly reduce PV yield prediction error and mitigate financial risks.
... In comparison to the roof, predicting the solar energy potential of the building's facades is rare (Desthieux et al., 2018;Jaugsch & Löwner, 2016). Few studies have been conducted on how to optimize solar potential focusing on the PV positioning on building facades (Ádám Bognár et al., 2021;Garni et al., 2019;Redweik et al., 2013;Sun et al., 2021). According to Sun et al. installation of PV technologies into building facades requires careful planning to maximize renewable energy production while maintaining the aesthetic quality of the buildings in their urban context. ...
Photovoltaic energy-generating has attracted widespread attention, because of its efficiency and environmental benefits. As the number of buildings floors increases, the area of the façade grows substantially larger than the roof, which led to increasing the potential for a solar system installed on the vertical walls, although they receive less solar radiation than roof surfaces. As a result, it has become critical to assess the solar installation possibilities on buildings’ roofs as well as facades in the early design stage. Accordingly, the aim of this paper is to present a method to evaluate the suitable area on the building surfaces for photovoltaic installation, taking Apartment buildings in Amman, Jordan as a case study. The methodology is based on the assessment of the incident solar radiation on different surfaces, considering the shading effect from surrounding buildings in the most common residential urban zone in Jordan, and architectural suitable areas for PV installation. Different simulation software was used, Autodesk Ecotect simulation software was used to calculate the incident solar radiation on the building surfaces and IDA ICE 4.8 simulation software to predict the overshadowing area. The main findings of this study show that conducting a solar potential in the early design stage is critical to defining the most suitable surfaces for PV installation. Moreover, the highest potential envelope part of installing the solar photovoltaic technologies is the roof because it is unshaded and received the highest solar radiation, followed by the south façade with about 40% less received solar radiation. This study can contribute to supporting energy and urban planners in determining the best locations for solar photovoltaic installations on building surfaces.
