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Smartphone-based shade analysis using hemispherical fisheye imaging for local solar energy potential

Andersson, Jonas LU (2020) AEBM05 20202
Department of Architecture and Built Environment
Abstract
In the emerging trend of smartphone use and a rising demand for solar photovoltaic (PV) power worldwide, this master thesis project explores the potential to utilize commercially available fisheye lens optics in combination with a smartphone camera to capture and analyze local shading conditions. Photogrammetric studies involving hemispherical sky photos have since long been used for environmental studies and solar energy applications, although mostly with high-end costly equipment. This methodology is therefore developed as an alternative solution using available low-cost equipment for conducting shading analysis. The method overcomes challenging concepts such as lens distortion calibration and image segmentation to calculate plane of... (More)
In the emerging trend of smartphone use and a rising demand for solar photovoltaic (PV) power worldwide, this master thesis project explores the potential to utilize commercially available fisheye lens optics in combination with a smartphone camera to capture and analyze local shading conditions. Photogrammetric studies involving hemispherical sky photos have since long been used for environmental studies and solar energy applications, although mostly with high-end costly equipment. This methodology is therefore developed as an alternative solution using available low-cost equipment for conducting shading analysis. The method overcomes challenging concepts such as lens distortion calibration and image segmentation to calculate plane of array (POA) irradiance from the input image. Tested for both synthetic and real on-site shading analysis the developed method shows promising results in comparison to the more well-established PV system design software SAM. Ultimately it will challenge existing techniques with a more user-friendly, accurate and cost-effective shading analysis tool. (Less)
Popular Abstract
The electricity generated from solar panels is reduced due to local shading objects. To prevent this, measurements can be made on the surroundings to determine beforehand what location has the least shading throughout the year. This paper presents a new method for conducting such a shading analysis using smartphones.
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author
Andersson, Jonas LU
supervisor
organization
alternative title
Smartphone-based shading analysis
course
AEBM05 20202
year
type
H2 - Master's Degree (Two Years)
subject
keywords
solar power, shading analysis, smartphone-based, fisheye lens, image segmentation, watershed transformation
language
English
id
9028422
date added to LUP
2020-09-08 11:37:43
date last changed
2020-09-08 11:37:43
@misc{9028422,
  abstract     = {{In the emerging trend of smartphone use and a rising demand for solar photovoltaic (PV) power worldwide, this master thesis project explores the potential to utilize commercially available fisheye lens optics in combination with a smartphone camera to capture and analyze local shading conditions. Photogrammetric studies involving hemispherical sky photos have since long been used for environmental studies and solar energy applications, although mostly with high-end costly equipment. This methodology is therefore developed as an alternative solution using available low-cost equipment for conducting shading analysis. The method overcomes challenging concepts such as lens distortion calibration and image segmentation to calculate plane of array (POA) irradiance from the input image. Tested for both synthetic and real on-site shading analysis the developed method shows promising results in comparison to the more well-established PV system design software SAM. Ultimately it will challenge existing techniques with a more user-friendly, accurate and cost-effective shading analysis tool.}},
  author       = {{Andersson, Jonas}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Smartphone-based shade analysis using hemispherical fisheye imaging for local solar energy potential}},
  year         = {{2020}},
}