Improving the resolution of solar energy potential maps derived from global DSMs for rooftop solar panel placement using deep learning

This study focuses on generating high-resolution annual solar energy potential maps (ASMs) using global Digital Elevation Models (DEMs) to aid in solar panel placement, especially in urban areas. A framework was developed to enhance the resolution of these maps. Initially, the accuracy of ASMs derived from various DEMs was compared with LiDAR-derived ASMs. The evaluations indicated that the Copernicus DEM provided a highly accurate ASM. Subsequently, deep learning algorithms were trained to improve the resolution of the LiDAR-derived ASM. The results demonstrated that the Enhanced Deep Super-Resolution (EDSR) Network outperformed the U-Net-based model. The trained EDSR model was then utilized to enhance the resolution of the Copernicus ASM. Comparing the enhanced-resolution map of Copernicus respective to LiDAR showed that the EDSR model provided the necessary generalizability to improve the accuracy and resolution of the Copernicus ASM, particularly in urban areas. The investigations revealed that the improved resolution map with a resolution of 6 m, achieving RMSE of 35.75 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mfrac><mml:mrow><mml:mi>M</mml:mi> <mml:mi>W</mml:mi> <mml:mi>h</mml:mi></mml:mrow> <mml:mrow> <mml:msup><mml:mrow><mml:mi>m</mml:mi></mml:mrow> <mml:mrow><mml:mn>2</mml:mn></mml:mrow> </mml:msup> </mml:mrow> </mml:mfrac> </mml:math> and a correlation of 0.87 respective to LiDAR data, was capable of locating solar panels on buildings, whereas the original Copernicus-derived maps with a 30 m resolution had RMSE of 51.26 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mfrac><mml:mrow><mml:mi>M</mml:mi> <mml:mi>W</mml:mi> <mml:mi>h</mml:mi></mml:mrow> <mml:mrow> <mml:msup><mml:mrow><mml:mi>m</mml:mi></mml:mrow> <mml:mrow><mml:mn>2</mml:mn></mml:mrow> </mml:msup> </mml:mrow> </mml:mfrac> </mml:math> and a correlation of 0.72 for such placement purposes.

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Publication Details

Journal
Heliyon
Published
2024-12-13
DOI
https://doi.org/10.1016/j.heliyon.2024.e41193
Citations
7
Primary Topic
Solar Radiation and Photovoltaics
Type
article
Field-Weighted Citation Impact
2.43
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article

Improving the resolution of solar energy potential maps derived from global DSMs for rooftop solar panel placement using deep learning

Hossein Bagheri, Maryam Hosseini
7 citations
Heliyon
Solar Radiation and Photovoltaics
2.43
article

Improving the resolution of solar energy potential maps derived from global DSMs for rooftop solar panel placement using deep learning

Hossein Bagheri, Maryam Hosseini
article en
7 citations

Abstract

This study focuses on generating high-resolution annual solar energy potential maps (ASMs) using global Digital Elevation Models (DEMs) to aid in solar panel placement, especially in urban areas. A framework was developed to enhance the resolution of these maps. Initially, the accuracy of ASMs derived from various DEMs was compared with LiDAR-derived ASMs. The evaluations indicated that the Copernicus DEM provided a highly accurate ASM. Subsequently, deep learning algorithms were trained to improve the resolution of the LiDAR-derived ASM. The results demonstrated that the Enhanced Deep Super-Resolution (EDSR) Network outperformed the U-Net-based model. The trained EDSR model was then utilized to enhance the resolution of the Copernicus ASM. Comparing the enhanced-resolution map of Copernicus respective to LiDAR showed that the EDSR model provided the necessary generalizability to improve the accuracy and resolution of the Copernicus ASM, particularly in urban areas. The investigations revealed that the improved resolution map with a resolution of 6 m, achieving RMSE of 35.75 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mfrac><mml:mrow><mml:mi>M</mml:mi> <mml:mi>W</mml:mi> <mml:mi>h</mml:mi></mml:mrow> <mml:mrow> <mml:msup><mml:mrow><mml:mi>m</mml:mi></mml:mrow> <mml:mrow><mml:mn>2</mml:mn></mml:mrow> </mml:msup> </mml:mrow> </mml:mfrac> </mml:math> and a correlation of 0.87 respective to LiDAR data, was capable of locating solar panels on buildings, whereas the original Copernicus-derived maps with a 30 m resolution had RMSE of 51.26 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mfrac><mml:mrow><mml:mi>M</mml:mi> <mml:mi>W</mml:mi> <mml:mi>h</mml:mi></mml:mrow> <mml:mrow> <mml:msup><mml:mrow><mml:mi>m</mml:mi></mml:mrow> <mml:mrow><mml:mn>2</mml:mn></mml:mrow> </mml:msup> </mml:mrow> </mml:mfrac> </mml:math> and a correlation of 0.72 for such placement purposes.

HeliyonVol. 11(1)
University of Isfahan (IR)
Affordable and clean energy
Openalex Percentile: Top 9%
Solar Radiation and Photovoltaics
2.43
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Improving the resolution of solar energy potential maps derived from global DSMs for rooftop solar panel placement using deep learning — Hossein Bagheri, Maryam Hosseini · Heliyon (2024) | TGRS Research Map | TGRS