Rooftop solar potential based on LiDAR data: Bottom-up assessment at neighbourhood level

Wong

Energy Science & Engineering

et al. 2019

In this paper, a spatial photovoltaic (PV) potential evaluation method based on the combination of aerial photogrammetry and Geographic Information System (GIS) is proposed for PV potential evaluation of built environment (BE). The method can be applied to PV potential evaluation of not only building roofs, but also built environments such as carpark. The point cloud model of the studied field is established by processing images captured by unmanned aerial vehicles (UAV), which are then imported into GIS to estimate the available area for PV installation based on sunlight analysis after offsetting the identified borders of the urban and ecological infrastructures. The PV potential is determined based on the installation configurations of PV modules. With the help of the PVSYST software simulation, the proposed method in this paper is used to evaluate the PV potential of a carpark and a rooftop of a building in Singapore.

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation of the photovoltaic potential in built environment using spatial data captured by unmanned aerial vehicles

Wong

Energy Science & Engineering

et al. 2019

“…There is considerable previous literature regarding estimating solar potential in urban areas such as a university. Some studies have focused on building roofs [12][13][14][15][16][17][18][19]. These mainly studied the extraction of high potential roofs of buildings considering roof orientation and shadowing.…”

Section: Introductionmentioning

confidence: 99%

“…These mainly studied the extraction of high potential roofs of buildings considering roof orientation and shadowing. Some have focused on the detection of building roofs [12][13][14][15] and various scales of this method were applied from specific detailed building roofs [12,16] to the national scale [17]. This method is appropriate for urban areas with low buildings.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Solar Panel Orientation Considering Temporal Volatility and Scenario-Based Photovoltaic Potential: A Case Study in Seoul National University

Park

2019

Energies

University campuses accommodate large numbers of people and are suitable places to organize a microgrid. The solar potential in the university area is estimated and the optimal orientation of solar panels is presented in this study. The optimal orientation is analyzed considering temporal volatility to increase the stability of the grid. Several variables are selected and scenarios are designed to consider various investments and technologies. Scenario-specific photovoltaic potentials were estimated using Geographic Information Systems analysis technology. Analysis of temporal volatility was conducted based on the difference between demand and supply of electricity. Optimal panel orientations were presented according to project objectives, such as highest efficiency or low volatility. As a result, the total potential of the study area was tens to hundreds of GWh/year depending on the scenario. The university has an advantage in hourly volatility, but has some problems in monthly volatility. The optimal orientation varies according to objectives and solar power supply ratio. The results of this study are expected to help researchers and technicians in the solar energy industry and assist in urban planning.

“…They found that the small PV modules area helps to increase the energy yield but it increases the model-level and system-level cost per watt. From the geospatial perspective, the studies and simulation tools in References [10][11][12][13][14][15] estimated the effects of solar radiation, air temperature and wind speed to the PV energy yield. Unfortunately, their geospatial data are interpolated partly from satellite measurements, which reduces the reliability of the resulting model.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Contribution of PV Array and Inverter Configurations to Rooftop PV System Energy Yield Using Machine Learning Techniques

Benjapolakul

2019

Energies

Rooftop photovoltaics (PV) systems are attracting residential customers due to their renewable energy contribution to houses and to green cities. However, customers also need a comprehensive understanding of system design configuration and the related energy return from the system in order to support their PV investment. In this study, the rooftop PV systems from many high-volume installed PV systems countries and regions were collected to evaluate the lifetime energy yield of these systems based on machine learning techniques. Then, we obtained an association between the lifetime energy yield and technical configuration details of PV such as rated solar panel power, number of panels, rated inverter power, and number of inverters. Our findings reveal that the variability of PV lifetime energy is partly explained by the difference in PV system configuration. Indeed, our machine learning model can explain approximately 31 % ( 95 % confidence interval: 29–38%) of the variant energy efficiency of the PV system, given the configuration and components of the PV system. Our study has contributed useful knowledge to support the planning and design of a rooftop PV system such as PV financial modeling and PV investment decision.