Mersin Photogrammetry Journal 2687-654X Mersin University 10.53093/mephoj.1401603 Photogrammetry and Remote Sensing Fotogrametri ve Uzaktan Algılama Estimating the efficacy of solar photovoltaic panels in Lebanon using a digital surface model: A geospatial approach https://orcid.org/0000-0002-7472-8139 Doumit Jean Lebanese University 06 15 2024 6 1 22 31 12 07 2023 01 15 2024 Copyright © 2019, Mersin Photogrammetry Journal 2019 Mersin Photogrammetry Journal

With the escalating need for alternative energy sources due to economic crises and fossil fuel shortages in Lebanon, solar photovoltaic (PV) panels have emerged as an attractive solution. This study examines the capacity and efficacy of rooftop-installed PV solar panels. Using geospatial technologies, including Digital Surface Models drone-based photogrammetry, the study assesses geometric and solar characteristics, seasonal solar radiation, solar duration, and power for 40 PV units installed in the study area. This research presents specific quantitative values for optimal orientations that result in high solar radiation across various seasons and identifies varying slopes influencing the performance of PV solar panels. Employing the Agglomerative Hierarchical Clustering (AHC) technique, PV units are systematically classified into clusters labeled as Moderate, High, Low, and Very Low solar power, offering quantitative metrics regarding the effectiveness of distinct panels. The high-efficiency Cluster exhibits an average solar power of 1868.114 kWh/m² during the summer season, whereas the Very Low Cluster, comprising panels with minimal solar power output, averages 150.578 kWh/m² in the same season. In conclusion, the most effective PV solar panels within the study area are those oriented between 195 and 225 degrees, with shallow inclination angles and larger surface areas contributing to enhanced performance in capturing solar radiation and generating power. These precise quantitative insights contribute to informed decision-making for optimizing the placement of PV panels to enhance energy generation. The study's recommendations are substantiated by specific numerical data, guiding future solar installations to maximize solar energy generation.

 Solar radiation Solar duration Solar photovoltaic Photogrammetry GIS Milbrandt, A. R., Heimiller, D. M., & Schwabe, P. D. (2018). Techno-economic renewable energy potential on tribal lands. National Renewable Energy Laboratory, NREL/TP-6A20-70807. https://doi.org/10.2172/1459502 Charabi, Y., & Gastli, A. (2011). PV site suitability analysis using GIS-based spatial fuzzy multi-criteria evaluation. Renewable Energy, 36(9), 2554-2561. https://doi.org/10.1016/j.renene.2010.10.037 Gerbo, A., Suryabhagavan, K. V., & Kumar Raghuvanshi, T. (2022). GIS-based approach for modeling grid-connected solar power potential sites: a case study of East Shewa Zone, Ethiopia. Geology, Ecology, and Landscapes, 6(3), 159-173. https://doi.org/10.1080/24749508.2020.1809059 Strzalka, A., Alam, N., Duminil, E., Coors, V., & Eicker, U. (2012). Large scale integration of photovoltaics in cities. Applied Energy, 93, 413-421. https://doi.org/10.1016/j.apenergy.2011.12.033 Chaves, A., Bahill, A. T. (2010). Locating sites for photovoltaic solar panels pilot study uses DEM derived from LiDAR. ArcUser Fall 2010, 24-27 ESMAP. (2020). Global photovoltaic power potential by country. Washington, DC: World Bank Šúri, M., Huld, T. A., Dunlop, E. D., & Ossenbrink, H. A. (2007). Potential of solar electricity generation in the European Union member states and candidate countries. Solar Energy, 81(10), 1295-1305. https://doi.org/10.1016/j.solener.2006.12.007 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 Clifton, J., & Boruff, B. (2010). Site options for concentrated solar power generation in the Wheatbelt. Wheatbelt Development Commission. An, Y., Chen, T., Shi, L., Heng, C. K., & Fan, J. (2023). Solar energy potential using GIS-based urban residential environmental data: A case study of Shenzhen, China. Sustainable Cities and Society, 93, 104547. https://doi.org/10.1016/j.scs.2023.104547 Sun, Y. W., Hof, A., Wang, R., Liu, J., Lin, Y. J., & Yang, D. W. (2013). GIS-based approach for potential analysis of solar PV generation at the regional scale: A case study of Fujian Province. Energy Policy, 58, 248-259. https://doi.org/10.1016/j.enpol.2013.03.002 Charabi, Y., & Gastli, A. (2010). GIS assessment of large CSP plant in Duqum, Oman. Renewable and Sustainable Energy Reviews, 14(2), 835-841. https://doi.org/10.1016/j.rser.2009.08.019 Lara, E. G., & Garcia, F. S. (2021). Review on viability and implementation of residential PV-battery systems: Considering the case of Dominican Republic. Energy Reports, 7, 8868-8899. https://doi.org/10.1016/j.egyr.2021.11.208 Ramadhan, M., & Naseeb, A. (2011). The cost benefit analysis of implementing photovoltaic solar system in the state of Kuwait. Renewable Energy, 36(4), 1272-1276. https://doi.org/10.1016/j.renene.2010.10.004 Böhner, J., & Antonić, O. (2009). Land-surface parameters specific to topo-climatology. Developments in Soil Science, 33, 195-226. https://doi.org/10.1016/S0166-2481(08)00008-1 Dubayah, R., & Rich, P. M. (1996). GIS-based solar radiation modeling. GIS and Environmental Modeling: Progress and Research Issues, 129-134. Global Solar Atlas 2.0, Solaris database version 2.1. https://solargis.com/maps-and-gis-data/download/lebanon Mulherin, A. (2011). A spatial approach to determine solar PV potential for Durham homeowners. [Master’s Thesis, Duke University]. Carrión, J. A., Estrella, A. E., Dols, F. A., Toro, M. Z., Rodríguez, M., & Ridao, A. R. (2008). Environmental decision-support systems for evaluating the carrying capacity of land areas: Optimal site selection for grid-connected photovoltaic power plants. Renewable and Sustainable Energy Reviews, 12(9), 2358-2380. https://doi.org/10.1016/j.rser.2007.06.011 Reijenga, T., & Ruoss, D. (2005). Technologies and integration concepts. Designing with solar power: a source book for building integrated photovoltaics, 22-52. NREL (2022). 2022 Annual Technology Baseline. Golden, CO: National Renewable Energy Laboratory. https://atb.nrel.gov/electricity/2022/commercial_pv Kaufman, L., & Rousseeuw, P. J. (1990). Finding groups in data: An introduction to cluster analysis. Wiley, New Jersey.