Document Type : Original Article


Department of Surveying Engineering, Golestan University, Aliabad Katul, Iran.


In recent years, the growing demand for energy and environmental requirements has focused much attention on solar energy as a renewable source. The building rooftops are the most suitable places for installing photovoltaic panels in urban and rural areas. In large districts, accurate estimation of radiation received by the rooftops requires the existence of detailed 3D information about them. This research aims to provide an efficient method to estimate solar energy production potential from the rooftops using the UAV photogrammetry method and GIS. The proposed method considers both the factors of the geometric features of the rooftops (slope and azimuth) and the shadow of the adjacent features. A threshold for minimum separated suitable rooftops for installing photovoltaic panels received radiation and rooftop area. Converting received radiation into electrical energy was made based on the average level of current world technology for solar panels. Providing a comparison between the amount of electricity produced during the four seasons and throughout the year as an effective parameter related to the consumption pattern is another achievement of this research. The findings of this research can be used in various fields, such as electricity and the construction industry, as well as macro planning, to benefit from clean energy. The results of implementing the proposed method for a rural area showed that out of a total of 543 existing roofs, 422 roofs are suitable for installing solar panels. Also, for these rooftops, the potential to produce 5741 MWh of electricity will be available in one year.


[1] Alhammad, A., Sun, Q., and Tao, Y. (2022). Optimal Solar Plant Site Identification Using GIS and Remote Sensing: Framework and Case Study. Energies, 15, 312.
[2] Teofilo, A., Radosevic, N., Tao, Y., Iringan, J., and Liu, C. (2021) Investigating potential rooftop solar energy generated by Leased Federal Airports in Australia: Framework and implications. J. Build. Eng, 41, 102390.
[3] Momenzadeh, Z., kalantari, S., Tazeh, M., and Taghizadeh, R. (2021). 'Zoning and locating solar power station using AHP and GIS in Yazd province', Journal of Environmental Science and Technology, 22(12), pp. 259-271. doi: 10.22034/jest.2020.37606.4373.
[4] Lukač, N., Seme, S., Žlaus, D., Štumberger, G., and Žalik, B., (2014). Buildings roofs photovoltaic potential assessment based on LiDAR (Light Detection And Ranging) data, energy, 66, PP. 598-609.
[5] Colak, H. E., Memisoglu, T., and Gercek, Y. (2020). Optimal site selection for solar photovoltaic (PV) power plants using GIS and AHP: a case study of Malatya Province, Turkey, Renew. Energy, 149, pp. 565-576, 10.1016/j.renene.2019.12.078.
[6] Holmberg, K. and Erdemir, A. (2017). Influence of tribology on global energy consumption, costs and emissions. Friction, 5, 263–284.
[7] HOFIERKA, J.; ZLOCHA, M. (2012). A New 3-D Solar Radiation Model for 3-D City Models. Transactions in GIS, 16 (5), 681-690.
[8] Doljak, D. et al. (2017). Photovoltaic Potential of the City of Pozarevac, Renewable and Sustainable Energy Reviews, 73, June, pp. 460-467.
[9] Ghebrezgabher, M. and Weldegabir, A. (2022). 'Estimating Solar Energy Potential in Eritrea: a GIS-based Approach', Renewable Energy Research and Applications, 3(2), pp. 155-164. doi: 10.22044/rera.2022.11737.1106.
[10] Nex, F. and Remondino, F. UAV for 3D mapping applications: A review. Appl. Geomat. 2014, 6, 1–15.
[11] Iglhaut, J.; Cabo, C.; Puliti, S.; Piermattei, L.; O’Connor, J.; and Rosette, J. Structure from motion photogrammetry in forestry: A review. Curr. For. Rep. 2019, 5, 155–168.
[12] Gasparini, M.; Moreno-Escribano, J.C.; and Monterroso-Checa, A. Photogrammetric Acquisitions in Diverse Archaeological Contexts Using Drones: Background of the Ager Mellariensis Project (North of Córdoba-Spain). Drones 2020, 4, 47.
[13] Kucharczyk, M. and Hugenholtz, C. H. (2021). Remote sensing of natural hazard-related disasters with small drones: Global trends, biases, and research opportunities. Remote Sensing of Environment, 264, 112577.
[14] Vasuki, Y., Holden, E., Kovesi, P., and Micklethwaite, S. (2014). Semi-automatic mapping of geological Structures using UAV-based photogrammetric data: An image analysis approach. Computers & Geosciences, 69, 22-32.
[15] Schunder, T., Yin, D.; Bagchi-Sen, S., and Rajan, K. (2020). A Spatial Analysis of the Development Potential of Rooftop and Community Solar Energy. Remote Sens. Appl. Soc. Environ., 19, 100355.
[16] Huang, X., Hayashi, K., Matsumoto, T., Tao, L., Huang, Y., and Tomino, Y. (2022). Estimation of Rooftop Solar Power Potential by Comparing Solar Radiation Data and Remote Sensing Data—A Case Study in Aichi, Japan. Remote Sens, 14, 1742.
[17] (Accessed: 10 November 2022).
[18] Esfahani, S. K., Karrech, A., Cameron, R., Elchalakani, M., Tenorio, R., and Jerez, F. (2021). Optimizing the solar energy capture of residential roof design in the southern hemisphere through Evolutionary Algorithm. Energy and Built Environment, 2(4), 406-424.
[19] (Accessed: 12 November 2022).
[20] Baghani, A., Valadan Zoej, M. J., and Mokhtarzade, M (2018) Automatic hierarchical registration of aerial and terrestrial image-based point clouds, European Journal of Remote Sensing, 51:1, 436-456, DOI: 10.1080/22797254.2018.1444946.
[21] Avtar, R.; Sahu, N., Aggarwal, A.K., Chakraborty, S., Kharrazi, A., Yunus, A.P., Dou, J.; and Kurniawan, T.A. (2019). Exploring Renewable Energy Resources Using Remote Sensing and GIS—a Review. Resources, 8, 149.
[22] Phap, V.M., Thu Huong, N.T., Hanh, P.T., Van Duy, P., and Van Binh, D. (2020). Assessment of Rooftop Solar Power Technical Potential in Hanoi City, Vietnam. J. Build. Eng, 32, 101528.
[23] (Accessed: 05 May 2021).