Photovoltaic potential in a Lisbon suburb using LiDAR data

Brito, M.C.; Gomes, Nuno Miguel Pereira; Santos, Teresa; Tenedório, José António

doi:10.1016/j.solener.2011.09.031

Cited by 160 publications

(71 citation statements)

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“…The population within this boundary is approximately 750,000 representing 1.2% of the UK population in 2011. Low-resolution LiDAR data was obtained from Landmap [25] with a grid resolution of 2 m and an elevation error margin of 0.15 m. There are 48 × 10 6 LiDAR points within the boundary. Building footprint data was obtained from Digimap [26].…”

Section: Application To the City Region Of Leeds (Uk)mentioning

confidence: 99%

“…The LiDAR points lying within each building footprint were filtered using a GIS-based system with output datasets in a text-based format. This resulted in a total of 5 × 10 6 LiDAR points under consideration. The total rooftop area available in the assessed region is 5.0 × 10 6 m 2 on small buildings and 11.3 × 10 6 m 2 on large buildings.…”

Section: Application To the City Region Of Leeds (Uk)mentioning

confidence: 99%

“…Previously reported methods to calculate the potential PV capacity over a city region include image analysis of geometrically-corrected high-resolution aerial photography [4,5], statistical approaches based on correlations between building class, population, and roof profile [6][7][8], and roof profile reconstruction from light detection and ranging (LiDAR) point clouds [9][10][11][12][13][14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

“…An accurate assessment of the potential roof-mounted PV capacity in city regions is an essential component for establishing regional and national carbon reduction policies and informing investment decisions [3]. However, such assessments are not straightforward because of the range in size, orientation, pitch, and geometric complexity typically found in roof profiles.Previously reported methods to calculate the potential PV capacity over a city region include image analysis of geometrically-corrected high-resolution aerial photography [4,5], statistical approaches based on correlations between building class, population, and roof profile [6][7][8], and roof profile reconstruction from light detection and ranging (LiDAR) point clouds [9][10][11][12][13][14][15][16][17][18][19][20].Methods that utilise LiDAR data usually employ an error-minimising plane-fitting algorithm that divides each roof in to an arbitrary set of planes, which are referred to as roof segments. While such methods report high accuracy for large geometrically simple roofs, such as warehouses, they invariably require high-resolution LiDAR data to achieve accurate results for small buildings, such as residential properties, with inherently more complex roof profiles.…”

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confidence: 99%

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Methodology for the assessment of PV capacity over a city region using low-resolution LiDAR data and application to the City of Leeds (UK)

et al. 2014

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An assessment of roof-mounted PV capacity over a local region can be accurately calculated by established roof segmentation algorithms using high-resolution light detection and ranging (LiDAR) datasets. However, over larger city regions often only low-resolution LiDAR data is available where such algorithms prove unreliable for small rooftops. A methodology optimised for low-resolution LiDAR datasets is presented, where small and large buildings are considered separately. The roof segmentation algorithm for small buildings, which are typically residential properties, assigns a roof profile to each building from a catalogue of common profiles after identifying LiDAR points within the building footprint. Large buildings, such as warehouses, offer a more diverse range of roof profiles but geometric features are generally large, so a direct approach is taken to segmentation where each LiDAR point within the building footprint contributes a separate roof segment. The methodology is demonstrated by application to the city region of Leeds, UK. Validation by comparison to aerial photography indicates that the assignment of an appropriate roof profile to a small building is correct in 81% of cases.Keywords: PV capacity; PV output; LiDAR; Roof profile; Solar resource; City region IntroductionPhotovoltaics (PV) are viewed as a key climate change mitigation technology. To achieve this potential will require the large scale installation of PV, either on rooftops or as ground mounted arrays [1]. Installing highly distributed PV within city environments, such as on building rooftops and facades, locates electricity generation close to electricity end use, reducing the requirement for modifications to the electricity distribution network and minimizing transmission losses. Roof mounted PV also avoids the cost and competition for land, and the possible social and environmental impacts associated with large arrays of ground mounted panels [2]. An accurate assessment of the potential roof-mounted PV capacity in city regions is an essential component for establishing regional and national carbon reduction policies and informing investment decisions [3]. However, such assessments are not straightforward because of the range in size, orientation, pitch, and geometric complexity typically found in roof profiles.Previously reported methods to calculate the potential PV capacity over a city region include image analysis of geometrically-corrected high-resolution aerial photography [4,5], statistical approaches based on correlations between building class, population, and roof profile [6][7][8], and roof profile reconstruction from light detection and ranging (LiDAR) point clouds [9][10][11][12][13][14][15][16][17][18][19][20].Methods that utilise LiDAR data usually employ an error-minimising plane-fitting algorithm that divides each roof in to an arbitrary set of planes, which are referred to as roof segments. While such methods report high accuracy for large geometrically simple roofs, such as warehouses, they invariably requir...

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Section: Application To the City Region Of Leeds (Uk)mentioning

confidence: 99%

Section: Application To the City Region Of Leeds (Uk)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Methodology for the assessment of PV capacity over a city region using low-resolution LiDAR data and application to the City of Leeds (UK)

et al. 2014

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“…Agugiaro et al [7] employed r.sun to estimate solar radiation on building roofs in mountainous areas through a combination of a high-resolution DSM and a vector cadastral map that describes building footprints. Brito et al [8] performed a simple first-approximation estimation of the photovoltaic potential of a Lisbon urban area using SA on a one-meter DSM. Lukač and Žalik [9] leveraged state-of-the-art graphics processing unit (GPU)-based techniques to accelerate the computation of the solar potential of roofs using LiDAR data.…”

Section: Introductionmentioning

confidence: 99%

A Sparse Voxel Octree-Based Framework for Computing Solar Radiation Using 3D City Models

Liang

Gong

2017

IJGI

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An effective three-dimensional (3D) data representation is required to assess the spatial distribution of the photovoltaic potential over urban building roofs and facades using 3D city models. Voxels have long been used as a spatial data representation, but practical applications of the voxel representation have been limited compared with rasters in traditional two-dimensional (2D) geographic information systems (GIS). We propose to use sparse voxel octree (SVO) as a data representation to extend the GRASS GIS r.sun solar radiation model from 2D to 3D. The GRASS GIS r.sun model is nested in an SVO-based computing framework. The presented 3D solar radiation computing framework was applied to 3D building groups of different geometric complexities to demonstrate its efficiency and scalability. We presented a method to explicitly compute diffuse shading losses in r.sun, and found that diffuse shading losses can reduce up to 10% of the annual global radiation under clear sky conditions. Hence, diffuse shading losses are of significant importance especially in complex urban environments.

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Photovoltaic potential estimation for various surface components of urban residential buildings based on Industry Foundation Classes data

Zhou

et al. 2022

Energy Science & Engineering

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Installing photovoltaic (PV) equipment on the building's surface reduces greenhouse gas emissions and additional land acquisition. However, existing methods for estimating PV potential primarily focus on roofs and the assessment of facades and windows is not detailed. This study proposes a method based on the Building Information Model and its standard Industry Foundation Classes to effectively estimate the potential area and location of PV modules on the building's surface and explore the PV potential in detail.The method was applied to residential buildings in Xinyi County, China, and the results show that the annual average energy generated through windows (47,589.793 kWh/year), roofs (141,126.304 kWh), and facades (284,060.393 kWh) constitute a significant amount, particularly the facades, which show significant potential as sources of solar electricity even under the influence of shadows. The rationality and feasibility of this method were verified by comparing it with ArcGIS and other relevant studies available in the literature. Next, we assessed whether the installation of PV panels on the building's surface still has certain operability under the serious shadows. For this purpose, a building with serious shadow shading was selected to analyze the demand and supply of energy. The electricity consumption of the selected building was found to be 131,785.898 kWh/year, which is significantly lower than PV potential (310,632.775 kWh/year), as calculated in this study. Thus, the efficient use of the building's surface for PV generation can meet not only the local electrical energy demand but also the excess energy generated can be sold for economic gain. The method proposed can provide new insights to energy software designers and developers, and the results can provide a reference for energy investors, building owners, and engineers to focus on the installation of PV panels on facades and windows, and formulate complex schemes of PV modules on different building components.

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Photovoltaic potential in a Lisbon suburb using LiDAR data

Cited by 160 publications

References 10 publications

Methodology for the assessment of PV capacity over a city region using low-resolution LiDAR data and application to the City of Leeds (UK)

Methodology for the assessment of PV capacity over a city region using low-resolution LiDAR data and application to the City of Leeds (UK)

A Sparse Voxel Octree-Based Framework for Computing Solar Radiation Using 3D City Models

Photovoltaic potential estimation for various surface components of urban residential buildings based on Industry Foundation Classes data

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