Applying LIDAR datasets and GIS based model to evaluate solar potential over roofs: a review

Martín, Ana María Viéitez; Domínguez, Javier; Amador, Juan Antonio

doi:10.3934/energy.2015.3.326

Cited by 49 publications

(24 citation statements)

References 49 publications

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“…The size of the study area is an important variable in a solar potential analysis [18]. Vector cartographic maps, digital cadastral services, state geographic information systems, digital elevation and digital surface models, and aerial photos are different resources that are widely used in evaluating solar potential [18]. These resources provide required information about building shape, footprint, height, type, location, and other urban features [18].…”

Section: Datamentioning

confidence: 99%

“…These methods are more precise than constant-value methods and can be applied to much larger data sets compared with a manual selection approach [8]. Martin et al reviewed different procedures for the solar potential assessment in urban areas [18]. Singh and Banerjee used land use data and GIS-based satellite image analysis for estimating the building footprint area and the rooftop PV potential for the Indian city of Mumbai [19].…”

Section: Introductionmentioning

confidence: 99%

“…Jakubiec and Reinhart presented a method for estimating city-wide electricity gains from PV panels by creating 3D urban models using LiDAR data and ArcGIS, Daysim-based hourly radiation simulations, and hourly calculated rooftop temperatures [20]. Creating a 3D urban model is crucial when assessing PV rooftop potential in an urban environment [18]. A precise knowledge of the PV potential requires a comprehensive study of the spatial dimensions of the site [18].…”

Section: Introductionmentioning

confidence: 99%

“…Creating a 3D urban model is crucial when assessing PV rooftop potential in an urban environment [18]. A precise knowledge of the PV potential requires a comprehensive study of the spatial dimensions of the site [18]. The emergence of LiDAR technology has provided a great opportunity for dense urban area mapping [21].…”

Section: Introductionmentioning

confidence: 99%

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Evaluating solar energy technical and economic potential on rooftops in an urban setting: the city of Lethbridge, Canada

Kouhestani

Byrne

Johnson

et al. 2018

Int J Energy Environ Eng

110

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Solar energy deployment is gaining greater attention as a sustainable source of energy that could alleviate aspects of the current climate crisis. Knowledge of the characteristics and economics of the solar electricity sector is required to integrate it in the energy generation and utilization mix. Unlike energy generation from fossil fuels, renewable energy sources have relatively low geographic density and are spread unevenly over large areas. Therefore, especially in cities, where space has greater value and opportunity costs, finding suitable spaces for implementing solar systems are essential to promote the use of solar technologies. Using remote-sensing data, the intricate topography of cities can be modelled, and insolation incident at each location can be estimated. A multi-criteria approach based on geographic information systems (GIS) and light detection and ranging (LiDAR) is used in this research to estimate rooftop photovoltaic electricity potential of buildings in an urban environment, the city of Lethbridge. An economic assessment is conducted utilizing present market prices to determine economically attractive rooftop PV systems. The total rooftop photovoltaic (PV) electricity potential is evaluated and compared with the local electricity demand. Effective expansion of solar power systems in the city is achieved by determining the geographic distribution of the best locations for exploiting the systems. This study estimates that the rooftop PV electricity generation potential of the city of Lethbridge is approximately 301 ± 29 (SD) GWh annually (almost 38% of its annual electricity consumption in 2016), and about 96% of the recognized potential rooftop PV systems are economically feasible. The results can assist in making informed policy decisions about investment in deployment of renewable energy generation.

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Section: Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Evaluating solar energy technical and economic potential on rooftops in an urban setting: the city of Lethbridge, Canada

Kouhestani

Byrne

Johnson

et al. 2018

Int J Energy Environ Eng

110

View full text Add to dashboard Cite

show abstract

“…In contrast, surface elevation measurements obtained from airborne LiDAR are quicker and more effective than traditional photogrammetric techniques [17,18]. Since then, a number of studies have been conducted using LiDAR data on the urban 3D building modeling [19][20][21][22][23], urban vegetation identification and green volume estimation [24,25], and solar potential evaluation in the urban area [26][27][28][29][30]. For example, Yu, et al [26] demonstrated that the integration of urban Digital Surface Model (DSM) from LiDAR data and a solar radiation flux model is able to investigate the spatio-temporal variations of solar radiation in downtown Houston.…”

Section: Introductionmentioning

confidence: 99%

Estimating Roof Solar Energy Potential in the Downtown Area Using a GPU-Accelerated Solar Radiation Model and Airborne LiDAR Data

Huang

Chen

et al. 2015

Remote Sensing

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Solar energy, as a clean and renewable resource is becoming increasingly important in the global context of climate change and energy crisis. Utilization of solar energy in urban areas is of great importance in urban energy planning, environmental conservation, and sustainable development. However, available spaces for solar panel installation in cities are quite limited except for building roofs. Furthermore, complex urban 3D morphology greatly affects sunlit patterns on building roofs, especially in downtown areas, which makes the determination of roof solar energy potential a challenging task. The object of this study is to estimate the solar radiation on building roofs in an urban area in Shanghai, China, and select suitable spaces for installing solar panels that can effectively utilize solar energy. A Graphic Processing Unit (GPU)-based solar radiation model named SHORTWAVE-C simulating direct and non-direct solar radiation intensity was developed by adding the capability of considering cloud influence into the previous SHORTWAVE model. Airborne Light Detection and Ranging (LiDAR) data was used as the input of the SHORTWAVE-C model and to investigate the morphological characteristics of the study area. The results show that the SHORTWAVE-C model can accurately estimate the solar radiation intensity in a complex urban environment under cloudy conditions, and the GPU acceleration method can reduce the computation time by up to 46%. Two sites with different building densities and rooftop structures were selected to illustrate the influence of urban morphology on the solar radiation and solar illumination duration. Based on the findings, an object-based method was implemented to identify suitable places for rooftop solar panel installation that can fully utilize the solar energy potential. Our study provides useful strategic guidelines for the selection and assessment of roof solar energy potential for urban energy planning.

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Multivariate Analysis of Solar City Economics

Byrne

Taminiau

Kim

et al. 2019

Advances in Energy Systems

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Applying LIDAR datasets and GIS based model to evaluate solar potential over roofs: a review

Cited by 49 publications

References 49 publications

Evaluating solar energy technical and economic potential on rooftops in an urban setting: the city of Lethbridge, Canada

Evaluating solar energy technical and economic potential on rooftops in an urban setting: the city of Lethbridge, Canada

Estimating Roof Solar Energy Potential in the Downtown Area Using a GPU-Accelerated Solar Radiation Model and Airborne LiDAR Data

Multivariate Analysis of Solar City Economics

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