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A new algorithm using a pyramid dataset for calculating shadowing in solar potential mapping

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  • Oh, Myeongchan
  • Park, Hyeong-Dong

Abstract

The efficiency of solar potential mapping is becoming increasingly important as solar energy technologies further develop. As digital surface models (DSMs) with improved spatial resolution become more available, the efficiency and accuracy of calculating solar potential need to be better improved. This study analyzes the algorithms available for calculating shadowing and proposes a new algorithm using a pyramid dataset. The available algorithms can be categorized as either shadow-based calculation algorithm (SBC) or Viewmap-based calculation algorithm (VBC). Relatively, SBC can generate simple results rapidly while VBC can generate detailed results slowly. VBC comprises three algorithm types: line scanning, all-data scanning, and the proposed pyramid dataset algorithms. The calculation time and accuracy of these algorithms were analyzed with respect to the spatial resolution of the DSMs and sky division resolution. The results show that the calculation time for each algorithm increases significantly as the resolution of the DSM increases. The proposed pyramid dataset algorithm showed high calculation speed and time complexity compared to previous VBCs. It is also able to generate a more detailed map than the SBC. The proposed algorithm showed high potential for further study as it can generate a detailed map of high resolution DSM rapidly.

Suggested Citation

  • Oh, Myeongchan & Park, Hyeong-Dong, 2018. "A new algorithm using a pyramid dataset for calculating shadowing in solar potential mapping," Renewable Energy, Elsevier, vol. 126(C), pages 465-474.
    • Handle: RePEc:eee:renene:v:126:y:2018:i:c:p:465-474
    DOI: 10.1016/j.renene.2018.03.068
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    References listed on IDEAS

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    1. Freitas, S. & Catita, C. & Redweik, P. & Brito, M.C., 2015. "Modelling solar potential in the urban environment: State-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 915-931.
    2. Suomalainen, Kiti & Wang, Vincent & Sharp, Basil, 2017. "Rooftop solar potential based on LiDAR data: Bottom-up assessment at neighbourhood level," Renewable Energy, Elsevier, vol. 111(C), pages 463-475.
    3. Brito, M.C. & Freitas, S. & Guimarães, S. & Catita, C. & Redweik, P., 2017. "The importance of facades for the solar PV potential of a Mediterranean city using LiDAR data," Renewable Energy, Elsevier, vol. 111(C), pages 85-94.
    4. Hofierka, Jaroslav & Kaňuk, Ján, 2009. "Assessment of photovoltaic potential in urban areas using open-source solar radiation tools," Renewable Energy, Elsevier, vol. 34(10), pages 2206-2214.
    5. Sarralde, Juan José & Quinn, David James & Wiesmann, Daniel & Steemers, Koen, 2015. "Solar energy and urban morphology: Scenarios for increasing the renewable energy potential of neighbourhoods in London," Renewable Energy, Elsevier, vol. 73(C), pages 10-17.
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    Cited by:

    1. Myeongchan Oh & Hyeong-Dong Park, 2019. "Optimization of Solar Panel Orientation Considering Temporal Volatility and Scenario-Based Photovoltaic Potential: A Case Study in Seoul National University," Energies, MDPI, vol. 12(17), pages 1-17, August.
    2. YoungHyun Koo & Myeongchan Oh & Sung-Min Kim & Hyeong-Dong Park, 2020. "Estimation and Mapping of Solar Irradiance for Korea by Using COMS MI Satellite Images and an Artificial Neural Network Model," Energies, MDPI, vol. 13(2), pages 1-19, January.
    3. Arias-Rosales, Andrés & LeDuc, Philip R., 2023. "Urban solar harvesting: The importance of diffuse shadows in complex environments," Renewable and Sustainable Energy Reviews, Elsevier, vol. 175(C).
    4. Arias-Rosales, Andrés & LeDuc, Philip R., 2022. "Shadow modeling in urban environments for solar harvesting devices with freely defined positions and orientations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 164(C).

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