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Optimization of Solar Panel Installation Potential Mapping Based on Convolutional Neural Network
Corresponding Author(s) : Maulisa Oktiana
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol. 11, No. 2, May 2026
Abstract
The increasing global energy demand and the depletion of fossil fuel resources have accelerated the transition toward renewable energy. Solar energy is considered one of the most promising sustainable energy sources. However, identifying suitable locations for solar panel installation remains challenging due to geographic and environmental variability across different regions. This study proposes a Convolutional Neural Network (CNN)-based approach to map potential solar panel installation areas using high-resolution satellite imagery. The model is designed to extract spatial features from land surfaces, including land cover characteristics, building density, and reflectance patterns derived from Sentinel-2 imagery obtained through Google Earth Engine. The proposed framework utilizes a VGG19-based architecture with transfer learning to improve feature extraction and classification performance. Experimental results demonstrate that the proposed model achieves an accuracy of 94.2% in classifying areas suitable for solar panel installation. These findings indicate that deep learning–based spatial analysis can provide an effective approach to support large-scale solar energy planning and decision-making.
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- Q. Feng, H. Zhang, X. Liu, L. Wang, Y. Xu, and X. Li, “A 10-m national-scale map of ground-mounted photovoltaic power stations in China of 2020,” Scientific Data, vol. 11, no. 1, pp. 1–15, 2024. https://doi.org/10.1038/s41597-024-02994-x
- D. Kereush and I. Perovych, “Determining criteria for optimal site selection for solar power plants,” Geomatics, Land Management and Landscape, no. 4, pp. 39–49, 2017. https://doi.org/10.15576/GLL/2017.4.39
- B. Dwinata, G. G. Tabah, and B. Triasdian, “Pemetaan potensi energi listrik tenaga surya berdasarkan luas area permukiman,” eprints.itenas.ac.id, pp. 15–22, 2020.
- S. Zambrano-Asanza, J. Quiros-Tortos, and J. F. Franco, "Optimal site selection for photovoltaic power plants using a GIS-based multi-criteria decision making and spatial overlay with electric load," Renew. Sustain. Energy Rev., 2021. https://doi.org/10.1016/j.rser.2021.110853
- V.-S. Hudis, Teanu et al., "Impact of Temperature on the Efficiency of Monocrystalline and Polycrystalline Photovoltaic Panels: A Comprehensive Experimental Analysis for Sustainable Energy Solutions," Sustainability, vol. 16, p. 10566, 2024. https://doi.org/10.3390/su162310566
- Y. Jiang and B. Yi, "An Assessment of the Influences of Clouds on the Solar Photovoltaic Potential over China," Remote Sens., vol. 15, p. 258, 2023. https://doi.org/10.3390/rs15010258
- A. Kabré, D. Bonkoungou, and Z. Koalaga, "Analysis of the Effect of Temperature and Relative Humidity on the Reliability of a Photovoltaic Module," Adv. Mater. Phys. Chem., vol. 14, pp. 165–177, 2024. https://doi.org/10.4236/ampc.2024.148013
- P. Rousseau and H. Nouri, “ANSYS investigation of solar photovoltaic temperature distribution for improved efficiency,” International Journal of Advanced and Applied Sciences, vol. 12, pp. 293–300, 2023. http://doi.org/10.11591/ijaas.v12.i3.pp293-300
- T. De Jong, J. Smith, C. Wang, and H. Liu, “Monitoring spatial sustainable development: semi-automated analysis of satellite and aerial images for energy transition and sustainability indicators,” arXiv preprint, 2020. https://doi.org/10.48550/arXiv.2009.05738
- R. L. Curier et al., "Monitoring spatial sustainable development: Semi-automated analysis of satellite and aerial images for energy transition and sustainability indicators. OVERVIEW OF THE AVAILABLE DATASETS," Eurostat Grant Agreement: 08143.2017.001-2017.408, 2018.
- M. Wang, L. Yang, J. Zhang, Y. Li, and J. Xu, “Photovoltaic panel extraction from very high-resolution aerial imagery using region–line primitive association analysis and template matching,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 141, pp. 100–111, 2018. https://doi.org/10.1016/j.isprsjprs.2018.04.010
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- J. M. Malof, L. M. Collins, and K. Bradbury, “A deep convolutional neural network, with pre-training, for solar photovoltaic array detection in aerial imagery,” in Proc. 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017. https://doi.org/10.1109/IGARSS.2017.8127092
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- S. P. Pereira, A. Rafiee, and S. Lhermitte, “Automated rooftop solar panel detection through convolutional neural networks,” Canadian Journal of Remote Sensing, vol. 50, no. 1, 2024. https://doi.org/10.1080/07038992.2024.2363236
- R. Castello et al., "Deep learning in the built environment: automatic detection of rooftop solar panels using Convolutional Neural Networks," J. Phys.: Conf. Ser., vol. 1343, no. 1, p. 012034, 2019. https://doi.org/10.1088/1742-6596/1343/1/012034
- M. V. C. V. d. Costa et al., "Remote Sensing for Monitoring Photovoltaic Solar Plants in Brazil Using Deep Semantic Segmentation," Energies, vol. 14, no. 10, p. 2960, 2021. https://doi.org/10.3390/en14102960
- J. M. Malof, L. M. Collins, and K. Bradbury, "A deep convolutional neural network, with pre-training, for solar photovoltaic array detection in aerial IMAGERY," 2017. https://doi.org/10.1109/IGARSS.2017.8127092
- N. T. Suprova, M. A. R. Zidan, and A. R. M. H. Rashid, "Optimal Site Selection for Solar Farms Using GIS and AHP: A Literature Review," in Proc. Int. Conf. Ind. & Mech. Eng. Oper. Manag., Dhaka, Bangladesh, 2020.
- T. Kaur and T. K. Gandhi, “Automated brain image classification based on VGG-16 and transfer learning,” in Proc. IEEE Int. Conf. Signal Process., Image Process. Pattern Recognit., 2019, pp. 1–6. https://doi.org/10.1109/ICIT48102.2019.00023
- K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv Prepr. arXiv1409.1556, 2014. https://doi.org/10.48550/arXiv.1409.1556
- Q. Zhang and Q. Zhang, “Facial expression recognition in VGG network based on LBP feature extraction,” in Proc. 2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE), 2020.
- D. Kusumawati, A. A. Ilham, A. Achmad, and I. Nurtanio, “UAV forest fire detection based on CNN and InceptionV3,” in Proc. 2022 6th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE), Yogyakarta, Indonesia, Nov. 29–30, 2022, pp. 241–246. https://doi.org/10.1109/ICITISEE56436.2022.10057748
- Y. Tao, “Image style transfer based on VGG neural network model,” in Proc. 2022 IEEE International Conference on Advances in Electrical Engineering and Computer Applications (AEECA), Dalian, China, Aug. 12–14, 2022, pp. 301–304. https://doi.org/10.1109/AEECA55875.2022.9918891
- M. Fitria, Y. Elma, M. Oktiana, K. Saddami, and R. Novita, “The deep learning model for decayed-missing-filled teeth detection: A comparison between YOLOv5 and YOLOv8,” The Jordanian Journal of Computers and Information Technology (JJCIT), vol. 10, no. 3, pp. 335–349, 2024. https://doi.org/10.5455/jjcit.71-1710834785
References
Q. Feng, H. Zhang, X. Liu, L. Wang, Y. Xu, and X. Li, “A 10-m national-scale map of ground-mounted photovoltaic power stations in China of 2020,” Scientific Data, vol. 11, no. 1, pp. 1–15, 2024. https://doi.org/10.1038/s41597-024-02994-x
D. Kereush and I. Perovych, “Determining criteria for optimal site selection for solar power plants,” Geomatics, Land Management and Landscape, no. 4, pp. 39–49, 2017. https://doi.org/10.15576/GLL/2017.4.39
B. Dwinata, G. G. Tabah, and B. Triasdian, “Pemetaan potensi energi listrik tenaga surya berdasarkan luas area permukiman,” eprints.itenas.ac.id, pp. 15–22, 2020.
S. Zambrano-Asanza, J. Quiros-Tortos, and J. F. Franco, "Optimal site selection for photovoltaic power plants using a GIS-based multi-criteria decision making and spatial overlay with electric load," Renew. Sustain. Energy Rev., 2021. https://doi.org/10.1016/j.rser.2021.110853
V.-S. Hudis, Teanu et al., "Impact of Temperature on the Efficiency of Monocrystalline and Polycrystalline Photovoltaic Panels: A Comprehensive Experimental Analysis for Sustainable Energy Solutions," Sustainability, vol. 16, p. 10566, 2024. https://doi.org/10.3390/su162310566
Y. Jiang and B. Yi, "An Assessment of the Influences of Clouds on the Solar Photovoltaic Potential over China," Remote Sens., vol. 15, p. 258, 2023. https://doi.org/10.3390/rs15010258
A. Kabré, D. Bonkoungou, and Z. Koalaga, "Analysis of the Effect of Temperature and Relative Humidity on the Reliability of a Photovoltaic Module," Adv. Mater. Phys. Chem., vol. 14, pp. 165–177, 2024. https://doi.org/10.4236/ampc.2024.148013
P. Rousseau and H. Nouri, “ANSYS investigation of solar photovoltaic temperature distribution for improved efficiency,” International Journal of Advanced and Applied Sciences, vol. 12, pp. 293–300, 2023. http://doi.org/10.11591/ijaas.v12.i3.pp293-300
T. De Jong, J. Smith, C. Wang, and H. Liu, “Monitoring spatial sustainable development: semi-automated analysis of satellite and aerial images for energy transition and sustainability indicators,” arXiv preprint, 2020. https://doi.org/10.48550/arXiv.2009.05738
R. L. Curier et al., "Monitoring spatial sustainable development: Semi-automated analysis of satellite and aerial images for energy transition and sustainability indicators. OVERVIEW OF THE AVAILABLE DATASETS," Eurostat Grant Agreement: 08143.2017.001-2017.408, 2018.
M. Wang, L. Yang, J. Zhang, Y. Li, and J. Xu, “Photovoltaic panel extraction from very high-resolution aerial imagery using region–line primitive association analysis and template matching,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 141, pp. 100–111, 2018. https://doi.org/10.1016/j.isprsjprs.2018.04.010
J. M. Malof et al., "Automatic Solar Photovoltaic Panel Detection in Satellite Imagery," in Int. Conf. on Renew. Energy Res. and Appl., 2015, pp. 1428–1431.https://doi.org/10.1109/ICRERA.2015.7418643
J. M. Malof, L. M. Collins, and K. Bradbury, “A deep convolutional neural network, with pre-training, for solar photovoltaic array detection in aerial imagery,” in Proc. 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017. https://doi.org/10.1109/IGARSS.2017.8127092
Q. Feng, B. Niu, Y. Ren, S. Su, J. Wang, H. Shi, J. Yang, and M. Han, “A 10-m national-scale map of ground-mounted photovoltaic power stations in China of 2020,” Scientific Data, vol. 11, no. 1, p. 198, 2024. https://doi.org/10.1038/s41597-024-02994-x
S. P. Pereira, A. Rafiee, and S. Lhermitte, “Automated rooftop solar panel detection through convolutional neural networks,” Canadian Journal of Remote Sensing, vol. 50, no. 1, 2024. https://doi.org/10.1080/07038992.2024.2363236
R. Castello et al., "Deep learning in the built environment: automatic detection of rooftop solar panels using Convolutional Neural Networks," J. Phys.: Conf. Ser., vol. 1343, no. 1, p. 012034, 2019. https://doi.org/10.1088/1742-6596/1343/1/012034
M. V. C. V. d. Costa et al., "Remote Sensing for Monitoring Photovoltaic Solar Plants in Brazil Using Deep Semantic Segmentation," Energies, vol. 14, no. 10, p. 2960, 2021. https://doi.org/10.3390/en14102960
J. M. Malof, L. M. Collins, and K. Bradbury, "A deep convolutional neural network, with pre-training, for solar photovoltaic array detection in aerial IMAGERY," 2017. https://doi.org/10.1109/IGARSS.2017.8127092
N. T. Suprova, M. A. R. Zidan, and A. R. M. H. Rashid, "Optimal Site Selection for Solar Farms Using GIS and AHP: A Literature Review," in Proc. Int. Conf. Ind. & Mech. Eng. Oper. Manag., Dhaka, Bangladesh, 2020.
T. Kaur and T. K. Gandhi, “Automated brain image classification based on VGG-16 and transfer learning,” in Proc. IEEE Int. Conf. Signal Process., Image Process. Pattern Recognit., 2019, pp. 1–6. https://doi.org/10.1109/ICIT48102.2019.00023
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv Prepr. arXiv1409.1556, 2014. https://doi.org/10.48550/arXiv.1409.1556
Q. Zhang and Q. Zhang, “Facial expression recognition in VGG network based on LBP feature extraction,” in Proc. 2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE), 2020.
D. Kusumawati, A. A. Ilham, A. Achmad, and I. Nurtanio, “UAV forest fire detection based on CNN and InceptionV3,” in Proc. 2022 6th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE), Yogyakarta, Indonesia, Nov. 29–30, 2022, pp. 241–246. https://doi.org/10.1109/ICITISEE56436.2022.10057748
Y. Tao, “Image style transfer based on VGG neural network model,” in Proc. 2022 IEEE International Conference on Advances in Electrical Engineering and Computer Applications (AEECA), Dalian, China, Aug. 12–14, 2022, pp. 301–304. https://doi.org/10.1109/AEECA55875.2022.9918891
M. Fitria, Y. Elma, M. Oktiana, K. Saddami, and R. Novita, “The deep learning model for decayed-missing-filled teeth detection: A comparison between YOLOv5 and YOLOv8,” The Jordanian Journal of Computers and Information Technology (JJCIT), vol. 10, no. 3, pp. 335–349, 2024. https://doi.org/10.5455/jjcit.71-1710834785