An unmanned aerial vehicle based investigation of roof patch suitability for solar panel installation

Year 2024, , 281 - 291, 28.07.2024

Nizar Polat , Abdulkadir Memduhoğlu

https://doi.org/10.26833/ijeg.1424400

Abstract

This study presents a Geographic Information Systems (GIS) and Unmanned Aerial Vehicle (UAV) based approach to determine suitable roof patches of buildings for solar panel installation in Harran University (Şanlıurfa) campus area. Initially, the Solar Radiation Potential (SRP) of the study area was calculated using a UAV-based Digital Surface Model (DSM) in GIS. Then, a correction process was applied to this theoretically calculated SRP by using an adjustment coefficient derived from 5-year measurements of the Solar Power Plant (SPP) located in the region. This coefficient was used to adjust the calculated SRP and compared with the SPP measurements at a concurrent period. The rooftop objects were segmented by textural analysis to determine the suitable panel installation patches on the buildings. Then, the obtained suitable patches are divided into four different classes considering the adjusted total SRP to find panel installation priority. Finally, the calculated electricity potential of the suitable roof patches could meet approximately 65% of the yearly consumption of campus buildings. This paper reveals that in GIS-based SRP studies, it is necessary to detect the rooftop objects to obtain the solar panel installation area more accurately, and a correction should be applied to approximate the theoretically calculated SRP values to the actual values.

Keywords

Solar radiation potential, Unmanned aerial vehicle, GIS, Image processing

Thanks

The authors would like to thank Harran University Renewable Energy Research Center (GAPYENEV) and Specialist M. Akif ILKHAN for providing data and their valuable contributions.

References

• Ackermann, T. (2012). Wind power in power systems. John Wiley & Sons.
• Chan, T. F., & Lai, L. L. (2007). An axial-flux permanent-magnet synchronous generator for a direct-coupled wind-turbine system. IEEE Transactions on Energy Conversion, 22(1), 86-94. https://doi.org/10.1109/TEC.2006.889546
• Elliott, D., Schwartz, M., Scott, G., Haymes, S., Heimiller, D., & George, R. (2003). Wind energy resource atlas of Sri Lanka and the Maldives (No. NREL/TP-500-34518). National Renewable Energy Lab. (NREL), Golden, CO (United States).
• Bansal, R. C. (2003). Bibliography on the fuzzy set theory applications in power systems (1994-2001). IEEE Transactions on Power Systems 18 (4) 1291-1299. https://doi.org/10.1109/TPWRS.2003.818595
• Wang, Z., Bui, Q., Zhang, B., Nawarathna, C. L. K., & Mombeuil, C. (2021). The nexus between renewable energy consumption and human development in BRICS countries: The moderating role of public debt. Renewable Energy, 165, 381-390. https://doi.org/10.1016/j.renene.2020.10.144
• Adjiski, V., Kaplan, G., & Mijalkovski, S. (2023). Assessment of the solar energy potential of rooftops using LiDAR datasets and GIS based approach. International Journal of Engineering and Geosciences, 8(2), 188-199. https://doi.org/10.26833/ijeg.1112274
• Yılmaz, O. S., Gülgen, F., & Ateş, A. M. (2023). Determination of the appropriate zone on dam surface for floating photovoltaic system installation using RS and GISc technologies. International Journal of Engineering and Geosciences, 8(1), 63-75. https://doi.org/10.26833/ijeg.1052556
• Demirgül, T., Demir, V., & Sevimli, M. F. (2023). Model-Ağacı (M5-tree) yaklaşımı ile HELIOSAT tabanlı güneş radyasyonu tahmini. Geomatik, 8(2), 124-135. https://doi.org/10.29128/geomatik.1137687
• Arca, D., & Çıtıroğlu, H. K. (2022). Güneş enerjisi santral (GES) yapım yerlerinin CBS dayalı çok kriterli karar analizi ile belirlenmesi: Karabük örneği. Geomatik, 7(1), 17-25. https://doi.org/10.29128/geomatik.803200
• Choi, Y., Suh, J., & Kim, S. M. (2019). GIS-based solar radiation mapping, site evaluation, and potential assessment: A review. Applied Sciences, 9(9), 1960. https://doi.org/10.3390/app9091960
• Nematollahi, O., & Kim, K. C. (2017). A feasibility study of solar energy in South Korea. Renewable and Sustainable Energy Reviews, 77, 566-579. https://doi.org/10.1016/j.rser.2017.03.132
• Martín, A. M., Domínguez, J., & Amador, J. (2015). Applying LIDAR datasets and GIS based model to evaluate solar potential over roofs: a review. Aims Energy, 3(3), 326-343. https://doi.org/10.3934/energy.2015.3.326
• Yalcin, M., Dereli, M. A., & Ugur, M. A. (2019). Modeling of solar energy potential with geographical information system and remote sensing integration: A case study for Bergama, Turkey. International Symposium on Applied Geoinformatics (ISAG-2019), 136–164.
• Huang, Y., Chen, Z., Wu, B., Chen, L., Mao, W., Zhao, F., ... & Yu, B. (2015). Estimating roof solar energy potential in the downtown area using a GPU-accelerated solar radiation model and airborne LiDAR data. Remote Sensing, 7(12), 17212-17233. https://doi.org/10.3390/rs71215877
• Kucuksari, S., Khaleghi, A. M., Hamidi, M., Zhang, Y., Szidarovszky, F., Bayraksan, G., & Son, Y. J. (2014). An Integrated GIS, optimization and simulation framework for optimal PV size and location in campus area environments. Applied Energy, 113, 1601-1613. https://doi.org/10.1016/j.apenergy.2013.09.002
• Verso, A., Martin, A., Amador, J., & Dominguez, J. (2015). GIS-based method to evaluate the photovoltaic potential in the urban environments: The particular case of Miraflores de la Sierra. Solar Energy, 117, 236-245. https://doi.org/10.1016/j.solener.2015.04.018
• Polat, N., & Uysal, M. (2018). An experimental analysis of digital elevation models generated with Lidar Data and UAV photogrammetry. Journal of the Indian Society of Remote Sensing, 46(7), 1135-1142. https://doi.org/10.1007/s12524-018-0760-8
• Toprak, A. S., Polat, N., & Uysal, M. (2019). 3D modeling of lion tombstones with UAV photogrammetry: a case study in ancient Phrygia (Turkey). Archaeological and Anthropological Sciences, 11(5), 1973-1976. https://doi.org/10.1007/s12520-018-0649-z
• Uysal, M., Toprak, A. S., & Polat, N. (2015). DEM generation with UAV Photogrammetry and accuracy analysis in Sahitler hill. Measurement, 73, 539-543. https://doi.org/10.1016/j.measurement.2015.06.010
• Polat, N., & Uysal, M. (2017). DTM generation with UAV based photogrammetric point cloud. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, 77-79. https://doi.org/10.5194/isprs-archives-XLII-4-W6-77-2017
• Shao, H., Song, P., Mu, B., Tian, G., Chen, Q., He, R., & Kim, G. (2021). Assessing city-scale green roof development potential using Unmanned Aerial Vehicle (UAV) imagery. Urban Forestry & Urban Greening, 57, 126954. https://doi.org/10.1016/j.ufug.2020.126954
• Dewanto, B. G., Novitasari, D., Tan, Y. C., Puruhito, D. D., Fikriyadi, Z. A., & Aliyah, F. (2020). Application of web 3D GIS to display urban model and solar energy analysis using the unmanned aerial vehicle (UAV) data (Case study: National Cheng Kung university buildings). IOP Conference Series: Earth and Environmental Science, 520(1), 012017. https://doi.org/10.1088/1755-1315/520/1/012017
• Fuentes, J. E., Moya, F. D., & Montoya, O. D. (2020). Method for estimating solar energy potential based on photogrammetry from unmanned aerial vehicles. Electronics, 9(12), 2144. https://doi.org/10.3390/electronics9122144
• Turkish State Meteorological Service (2021). Turkish State Meteorological Service. https://mgm.gov.tr/kurumici/turkiye-guneslenme-suresi.aspx
• Global Solar Atlas (2021). Global Solar Atlas. https://globalsolaratlas.info/map?c=37.68382,36.112061,6.
• Rich, P., Dubayah, R., Hetrick, W., & Saving, S. (1994). Using viewshed models to calculate intercepted solar radiation: applications in ecology. American Society for Photogrammetry and Remote Sensing Technical Papers. American Society of Photogrammetry and Remote Sensing, 524-529.
• Fu, P., & Rich, P. M. (2002). A geometric solar radiation model with applications in agriculture and forestry. Computers and electronics in agriculture, 37(1-3), 25-35. https://doi.org/10.1016/S0168-1699(02)00115-1
• Kırcalı, Ş., & Selim, S. (2021). Site suitability analysis for solar farms using the geographic information system and multi-criteria decision analysis: the case of Antalya, Turkey. Clean Technologies and Environmental Policy, 23, 1233-1250. https://doi.org/10.1007/s10098-020-02018-3
• Nelson, J. R., & Grubesic, T. H. (2020). The use of LiDAR versus unmanned aerial systems (UAS) to assess rooftop solar energy potential. Sustainable Cities and Society, 61, 102353. https://doi.org/10.1016/j.scs.2020.102353
• Snavely, N., Seitz, S. M., & Szeliski, R. (2008). Modeling the world from internet photo collections. International journal of computer vision, 80, 189-210. https://doi.org/10.1007/s11263-007-0107-3
• Lucieer, A., Turner, D., King, D. H., & Robinson, S. A. (2014). Using an Unmanned Aerial Vehicle (UAV) to capture micro-topography of Antarctic moss beds. International journal of applied earth observation and geoinformation, 27, 53-62. https://doi.org/10.1016/j.jag.2013.05.011
• Li, B., Xu, X., Zhang, L., Han, J., Bian, C., Li, G., ... & Jin, L. (2020). Above-ground biomass estimation and yield prediction in potato by using UAV-based RGB and hyperspectral imaging. ISPRS Journal of Photogrammetry and Remote Sensing, 162, 161-172. https://doi.org/10.1016/j.isprsjprs.2020.02.013
• Yang, H., Hu, X., Zhao, J., & Hu, Y. (2021). Feature extraction of cotton plant height based on DSM difference method. International Journal of Precision Agricultural Aviation, 4(1), 59-69. https://doi.org/10.33440/j.ijpaa.20210401.151
• Boonpook, W., Tan, Y., & Xu, B. (2021). Deep learning-based multi-feature semantic segmentation in building extraction from images of UAV photogrammetry. International Journal of Remote Sensing, 42(1), 1-19. https://doi.org/10.1080/01431161.2020.1788742
• Haralick, R. M., Shanmugam, K., & Dinstein, I. H. (1973). Textural features for image classification. IEEE Transactions on systems, man, and cybernetics, (6), 610-621. https://doi.org/10.1109/TSMC.1973.4309314
• Šúri, M., & Hofierka, J. (2004). A new GIS‐based solar radiation model and its application to photovoltaic assessments. Transactions in GIS, 8(2), 175-190. https://doi.org/10.1111/j.1467-9671.2004.00174.x
• ESRI (2021). Modeling solar radiation. https://pro.arcgis.com/en/pro-app/latest/tool-reference/spatial-analyst/modeling-solar-radiation.htm
• ESRI (2021). How solar radiation is calculated. https://pro.arcgis.com/en/pro-app/latest/tool-reference/spatial-analyst/how-solar-radiation-is-calculated.htm
• Fu, P. (2000). A geometric solar radiation model with applications in landscape ecology. [ Doctoral dissertation, University of Kansas].
• Fröhlich, C., & Brusa, R. W. (1981). Physikalisch-meteorologisches observatoriurn, world radiation center, davos, switzerland. Sol Physics, 74, 16-19.
• Khanna, D. (2020). Estimate solar power potential. In: Learn ArcGIS. https://learn.arcgis.com/en/projects/estimate-solar-power-potential/#
• Palmer, D., Koumpli, E., Cole, I., Gottschalg, R., & Betts, T. (2018). A GIS-based method for identification of wide area rooftop suitability for minimum size PV systems using LiDAR data and photogrammetry. Energies, 11(12), 3506. https://doi.org/10.3390/en11123506
• Yang, Y., Campana, P. E., Stridh, B., & Yan, J. (2020). Potential analysis of roof-mounted solar photovoltaics in Sweden. Applied Energy, 279, 115786. https://doi.org/10.1016/j.apenergy.2020.115786
• Zhong, Q., & Tong, D. (2020). Spatial layout optimization for solar photovoltaic (PV) panel installation. Renewable energy, 150, 1-11. https://doi.org/10.1016/j.renene.2019.12.099