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A hierarchical methodology for the mesoscale assessment of building integrated roof solar energy systems

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  • Jo, J.H.
  • Otanicar, T.P.

Abstract

Buildings and other engineered structures that form cities are responsible for a significant portion of the global and local impacts of climate change. Consequently, the installation of building integrated renewable energy sources such as photovoltaic or solar thermal systems on building rooftops is being widely investigated. Although the advantages for individual buildings have been studied, as yet there is little understanding of the potential benefits of urban scale implementation of such systems. Here we report the development of a new methodology for assessing the potential capacity and benefits of installing rooftop photovoltaic systems in an urbanized area. Object oriented image analysis and geographical information systems are combined with remote sensing image data to quantify the rooftop area available for solar energy applications and a renewable energy computer simulation is included to predict the potential benefits of urban scale photovoltaic system implementation. The new methodology predicts energy generation potential that can be utilized to meet Arizona’s Renewable Portfolio Standard 2025 renewable energy generation requirements.

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  • Jo, J.H. & Otanicar, T.P., 2011. "A hierarchical methodology for the mesoscale assessment of building integrated roof solar energy systems," Renewable Energy, Elsevier, vol. 36(11), pages 2992-3000.
    • Handle: RePEc:eee:renene:v:36:y:2011:i:11:p:2992-3000
    DOI: 10.1016/j.renene.2011.03.038
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    1. Sánchez-Lozano, Juan M. & Teruel-Solano, Jerónimo & Soto-Elvira, Pedro L. & Socorro García-Cascales, M., 2013. "Geographical Information Systems (GIS) and Multi-Criteria Decision Making (MCDM) methods for the evaluation of solar farms locations: Case study in south-eastern Spain," Renewable and Sustainable Energy Reviews, Elsevier, vol. 24(C), pages 544-556.
    2. Jo, J.H. & Loomis, D.G. & Aldeman, M.R., 2013. "Optimum penetration of utility-scale grid-connected solar photovoltaic systems in Illinois," Renewable Energy, Elsevier, vol. 60(C), pages 20-26.
    3. 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.
    4. Calvert, K. & Pearce, J.M. & Mabee, W.E., 2013. "Toward renewable energy geo-information infrastructures: Applications of GIScience and remote sensing that build institutional capacity," Renewable and Sustainable Energy Reviews, Elsevier, vol. 18(C), pages 416-429.
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    6. Capellán-Pérez, Iñigo & de Castro, Carlos & Arto, Iñaki, 2017. "Assessing vulnerabilities and limits in the transition to renewable energies: Land requirements under 100% solar energy scenarios," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 760-782.
    7. Tamer, Tolga & Gürsel Dino, Ipek & Meral Akgül, Cagla, 2022. "Data-driven, long-term prediction of building performance under climate change: Building energy demand and BIPV energy generation analysis across Turkey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    8. Xiaoyang Song & Yaohuan Huang & Chuanpeng Zhao & Yuxin Liu & Yanguo Lu & Yongguo Chang & Jie Yang, 2018. "An Approach for Estimating Solar Photovoltaic Potential Based on Rooftop Retrieval from Remote Sensing Images," Energies, MDPI, vol. 11(11), pages 1-14, November.
    9. Sarawut Ninsawat & Mohammad Dalower Hossain, 2016. "Identifying Potential Area and Financial Prospects of Rooftop Solar Photovoltaics (PV)," Sustainability, MDPI, vol. 8(10), pages 1-16, October.
    10. Babak Ranjgar & Alessandro Niccolai, 2023. "Large-Scale Rooftop Solar Photovoltaic Power Production Potential Assessment: A Case Study for Tehran Metropolitan Area, Iran," Energies, MDPI, vol. 16(20), pages 1-14, October.
    11. Hong, Taehoon & Lee, Minhyun & Koo, Choongwan & Jeong, Kwangbok & Kim, Jimin, 2017. "Development of a method for estimating the rooftop solar photovoltaic (PV) potential by analyzing the available rooftop area using Hillshade analysis," Applied Energy, Elsevier, vol. 194(C), pages 320-332.
    12. Sánchez-Lozano, Juan M. & Henggeler Antunes, Carlos & García-Cascales, M. Socorro & Dias, Luis C., 2014. "GIS-based photovoltaic solar farms site selection using ELECTRE-TRI: Evaluating the case for Torre Pacheco, Murcia, Southeast of Spain," Renewable Energy, Elsevier, vol. 66(C), pages 478-494.
    13. Koo, Choongwan & Si, Ke & Li, Wenzhuo & Lee, JeeHee, 2022. "Integrated approach to evaluating the impact of feed-in tariffs on the life cycle economic performance of photovoltaic systems in China: A case study of educational facilities," Energy, Elsevier, vol. 254(PB).
    14. Hannes Koch & Stefan Lechner & Sebastian Erdmann & Martin Hofmann, 2022. "Assessing the Potential of Rooftop Photovoltaics by Processing High-Resolution Irradiation Data, as Applied to Giessen, Germany," Energies, MDPI, vol. 15(19), pages 1-17, September.
    15. Schallenberg-Rodríguez, Julieta, 2013. "Photovoltaic techno-economical potential on roofs in regions and islands: The case of the Canary Islands. Methodological review and methodology proposal," Renewable and Sustainable Energy Reviews, Elsevier, vol. 20(C), pages 219-239.
    16. De Schepper, Ellen & Van Passel, Steven & Manca, Jean & Thewys, Theo, 2012. "Combining photovoltaics and sound barriers – A feasibility study," Renewable Energy, Elsevier, vol. 46(C), pages 297-303.
    17. Gassar, Abdo Abdullah Ahmed & Cha, Seung Hyun, 2021. "Review of geographic information systems-based rooftop solar photovoltaic potential estimation approaches at urban scales," Applied Energy, Elsevier, vol. 291(C).

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