Investigating rooftop solar energy potential in coastal area with unmanned aerial vehicle technology

Abstract

Bengkulu has abundant direct sunlight all year round. Nonetheless, this region faces limited energy availability. Based on its potential, there is an immense opportunity for the development of electrical energy systems based on solar energy. In the coastal area, the operation of this energy system is still too limited and vulnerable. In order to fix energy requirements, a rooftop solar photovoltaic (PV) system can be implemented. The utilization of the rooftop requires preliminary studies related to solar mapping to identify the economic potential of the rooftop solar energy system. In this study, Unmanned Aerial Vehicle (UAV / Drone) technology has been adopted to map the potential of rooftop solar PV system. The drone is used to collect aerial photographic data on the rooftop, which is then processed to acquire a two-dimensional map. This map is used to obtain rooftop parameters such as area, tilt angle, and orientation of the roof. These rooftop parameters are favorable to estimate the potential of solar energy that can be generated. Based on these parameters, an estimate is made to assess the maximum solar energy that can be generated if the building rooftop is installed with a number of solar panels. To calibrate the calculated parameters of the rooftop, we compare the calculation results with the direct measurements. It has been proven that the drone technology can give promising results on high-resolution mapping of solar potential area. In addition, direct normal irradiance measurements are also performed in the case study area by using previously developed equipment.

Keywords

• Photovoltaic
• Renewable energy
• Rooftop mapping
• Rooftop solar system
• Unmanned aerial vehicle

References

1. [1] Rumbayan M, Abudureyimu A, Nagasaka K. Mapping of solar energy potential in Indonesia using artificial neural network and geographical information system. Renew Sustain Energy Rev. 2012;16:1437-1449. doi:10.1016/j.rser.2011.11.024.
2. [2] Cheng L, Li S, Zhang F, Ma J. Solar energy potential of urban buildings in 10 cities of China. Energy. 2020;196:C:1-16.doi:10.1016/j.energy.2020.117038.
3. [3] Ko L, Wang JC, Chen CY, Tsai HY. Evaluation of the development potential of rooftop solar photovoltaic in Taiwan. Renew Energy. 2015;76:582-595. doi:10.1016/j.renene.2014.11.077.
4. [4] Kouhestani FM, Byrne J, Johnson D, Spencer L, Hazendonk P, Brown B. Evaluating solar energy technical and economic potential on rooftops in an urban setting: the city of Lethbridge, Canada. Int J Energy Environ Eng. 2019;10:3-32. doi:10.1007/s40095-018-0289-1.
5. [5] Wiginton LK, Nguyen HT, Pearce JM. Quantifying rooftop solar photovoltaic potential for regional renewable energy policy. Comput Environ Urban Syst. 2010;34:345-357. doi:10.1016/j.compenvurbsys.2010.01.001.
6. [6] Green MA, Emery K, Hishikawa Y, Warta W. Solar cell efficiency tables (version 37). Prog Photovolt Res Appl. 2011;19:84-92. doi:10.1002/pip.1088.
7. [7] Jung J, Han S, Kim B. Digital numerical map-oriented estimation of solar energy potential for site selection of photovoltaic solar panels on national highway slopes. Appl Energy. 2018;242:57-68. doi:10.1016/j.apenergy.2019.03.101.
8. [8] Kumar V, Singh V, Umrao S, Parashar V, Abraham S, Singh A, Nath G, Saxena P, Srivastava A. Facile, rapid and upscaled synthesis of green luminescent functional graphene quantum dots for bioimaging. RSC Adv. 2014;4:21101-21107. doi:10.1039/c4ra01735h.

9. [9] Mahtta R, Joshi PK, Kumar A. Solar power potential mapping in India using remote sensing inputs and environmental parameters. Renew Energy. 2014;71:255-262. doi:10.1016/j.renene.2014.05.037.
10. [10] Martinopoulos G. Are rooftop photovoltaic systems a sustainable solution for Europe? A life cycle impact assessment and cost analysis. Appl Energy. 2019;257:1-13. doi:10.1016/j.apenergy.2019.114035.
11. [11] Yousuf M, Siddiqui M, Rehman N. Solar energy potential estimation by calculating sun illumination hours and sky view factor on building rooftops using digital elevation model. J Renew Sustain Energy. 2018;10(1):1-13.doi:10.1063/1.4997888.
12. [12] Bódis K, Kougias I, Jäger-Waldau A, Taylor N, Szabó S. A high-resolution geospatial assessment of the rooftop solar photovoltaic potential in the European Union. Renew Sustain Energy Rev. 2019;114:1-13. doi:10.1016/j.rser.2019.109309.
13. [13] Fath K, Stengel J, Sprenger W, Rose H, Schultmann F, Kuhn TE. A method for predicting the economic potential of (building-integrated) photovoltaics in urban areas based on hourly Radiance simulations. Sol Energy. 2015;116:357-370. doi:10.1016/j.solener.2015.03.023.
14. [14] Creutzig F, Agoston P, Goldschmidt JC, Luderer G, Nemet GF, Pietzcker R. The underestimated potential of solar energy to mitigate climate change. Nat Energy. 2017;2(9):2-9. doi:10.1038/nenergy.2017.140.
15. [15] Kodysh J, Omitaomu OA, Bhaduri BS, Budhendra LN. Methodology for estimating solar potential on multiple building rooftops for photovoltaic systems. Sustain Cities Soc. 2013;31:34-38. doi:10.1016/j.scs.2013.01.002.
16. [16] Lee S, Iyengar S, Feng M, Shenoy P, Maji S. Deep Roof: A data-driven approach for solar potential estimation using rooftop imagery. In: KDD '19 Proc. 25th ACM SIGKDD Int Conf Knowl Discov Data Min. 2019:2105-2113. doi:10.1145/3292500.3330741.
17. [17] Effat HA. Mapping solar energy potential zones, using SRTM and spatial analysis, application in Lake Nasser Region, Egypt. Int J Sustain Land Use Urban Plan. 2016;3(1). doi:10.24102/ijslup.v3i1.551.
18. [18] Lazarenko I, Cenky M, Bendik J. A Simplified Urban-Scale Rooftop Photovoltaic Potential Estimation. In: Proceedings of the 2024 24th International Scientific Conference on Electric Power Engineering, EPE 2024, 1-6. doi:10.1109/EPE61521.2024.10559532.
19. [19] Lodhi MK, Tan Y, Wang X, Masum SM, Nouman KM, Ullah N. Harnessing rooftop solar photovoltaic potential in Islamabad, Pakistan: A remote sensing and deep learning approach. Energy. 2024;304(37):132256. doi:10.1016/j.energy.2024.132256.
20. [20] Ni H, Wang D, Zhao W, Jiang W, Mingze E, Huang C, Yao J. Enhancing rooftop solar energy potential evaluation in high-density cities: A Deep Learning and GIS based approach. Energy and Buildings. 2024;309:113743. doi:10.1016/j.enbuild.2023.113743.
21. [21] Sander L, Schindler D, Jung C. Application of Satellite Data for Estimating Rooftop Solar Photovoltaic Potential. Remote Sensing. 2024;16(12). doi:10.3390/rs16122205.
22. [22] Boccardo P, Chiabrando F, Dutto F, Tonolo FG, Lingua A. UAV deployment exercise for mapping purposes: Evaluation of emergency response applications. Sensors (Switzerland). 2015; 15(7):15717-15737. doi:10.3390/s150715717.
23. [23] Koeva M, Muneza M, Gevaert C, Gerke M, Nex F. Using UAVs for map creation and updating. A case study in Rwanda. Surv Rev. 2016;312:325. doi:10.1080/00396265.2016.1268756.
24. [24] Grubesic TH, Nelson JR. UAVs and Urban Spatial Analysis: An Introduction. 1st ed. Switzerland: Springer; 2020.
25. [25] Song X, Huang Y, Zhao C, Liu Y, Lu Y, Chang Y, Yang J. An approach for estimating solar photovoltaic potential based on rooftop retrieval from remote sensing images. Energies. 2018; 11(11):13172. doi:10.3390/en11113172.
26. [26] Jacobson MZ, Jadhav V. World estimates of PV optimal tilt angles and ratios of sunlight incident upon tilted and tracked PV panels relative to horizontal panels. Sol Energy. 2018; 55:166-169. doi:10.1016/j.solener.2018.04.030.
27. [27] Handoyo EA, Ichsani D, Prabowo. The optimal tilt angle of a solar collector. Phys Procedia. 2013; 166:175-182. doi:10.1016/j.egypro.2013.05.022.
28. [28] Jafarkazemi F, Saadabadi SA. Optimum tilt angle and orientation of solar surfaces in Abu Dhabi, UAE. Renew Energy. 2013; 44:49-56. doi: 10.1016/j.renene.2012.10.036.
29. [29] Le Roux WG. Optimum tilt and azimuth angles for fixed solar collectors in South Africa using measured data. Renew Energy. 2016; 60:612-619. doi: 10.1016/j.renene.2016.05.003.
30. [30] Yadav AK, Chandel SS. Tilt angle optimization to maximize incident solar radiation: A review. Renew Sustain Energy Rev. 2013; 503:512-523. doi: 10.1016/j.rser.2013.02.027.
31. [31] Moshksar E, Ghanbari T. Real-time estimation of solar irradiance and module temperature from maximum power point condition. IET Sci Meas Technol. 2018; 12(6). doi:10.1049/iet-smt.2017.0476.
32. [32] Habibullah AD, Lidiawati L, Ekawita R. A simple and inexpensive irradiance monitoring system using photovoltaic panel. AIP Conf Proc. 2021; 2320. doi: 10.1063/5.0038334.