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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM62723
Volume:
10 |
5 |
May 2026
Pages:
1-7
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Electronics and Telecommunication Engineering
Priyadarshini Bhagwati College of Engineering, Nagpur, Maharashtra
Electronics and Telecommunication Engineering
Priyadarshini Bhagwati College of Engineering, Nagpur, Maharashtra
Electronics and Telecommunication Engineering
Priyadarshini Bhagwati College of Engineering, Nagpur, Maharashtra
Electronics and Telecommunication Engineering
Priyadarshini Bhagwati College of Engineering, Nagpur, Maharashtra
Electronics and Telecommunication Engineering
Priyadarshini Bhagwati College of Engineering, Nagpur, Maharashtra
Electronics and Telecommunication Engineering
Priyadarshini Bhagwati College of Engineering, Nagpur, Maharashtra
: Solar panels gradually lose efficiency when dust, bird droppings, and environmental debris accumulate on the surface. Manual cleaning is costly, time-consuming, and difficult to apply consistently across large or elevated installations. This paper presents an IoT-based solar panel cleaning robot that combines a water sprinkling mechanism, a motor-driven rotational mop, and an ESP32-WROOM controller with cloud connectivity. The system collects operating data from sensors, transmits the readings to a web-enabled platform, and uses an AI-assisted decision layer to estimate when cleaning is needed. When the panel efficiency falls below the chosen threshold, the user receives an automatic reminder through a web dashboard and can monitor the cleaning status in real time. The proposed architecture supports condition-based maintenance instead of fixed-interval cleaning, with the aim of improving photovoltaic output, reducing water usage, and lowering maintenance overhead.
KEYWORDS
This paper presents an integrated IoT-based solar panel cleaning robot with an AI-assisted cleaning decision layer and a web-enabled reminder system. The architecture unifies sensing, communication, actuation, and monitoring into a single workflow. By combining an ESP32 controller, a motor-driven cleaning mechanism, cloud connectivity, and condition-based maintenance, the proposed system offers a practical route toward maintaining solar panel efficiency with less manual intervention.
[1] Sayyah, A., Horenstein, M. N., & Mazumder, M. K., “Energy Yield Loss Caused by Dust Deposition on Photovoltaic Panels,” Solar Energy, Elsevier, vol. 107, pp. 576–604, 2014. [2] Mani, M., & Pillai, R., “Impact of Dust on Solar Photovoltaic (PV) Performance: Research Status, Challenges and Recommendations,” Renewable and Sustainable Energy Reviews, Elsevier, vol. 14, no. 9, pp. 3124–3131, 2010. [3] IEEE Standards Association, “Design and Development of Automatic Solar Panel Cleaning Systems,” IEEE Conference on Renewable Energy Systems, 2017. [4] Bansal, S. K., Sharma, P., & Verma, R., “Robotic Cleaning System for Solar Panels,” International Journal of Engineering Research and Technology (IJERT), vol. 7, no. 5, 2018. [5] Lee, J., Kim, H., & Park, S., “IoT-Based Smart Monitoring System for Solar Energy Generation,” IEEE Internet of Things Journal, vol. 6, no. 4, pp. 6434–6442, 2019. [6] Kumar, R., Singh, A., & Gupta, N., “Smart Solar Panel Monitoring System Using IoT,” International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, vol. 9, no. 3, 2020. [7] Zhang, Y., Wang, X., & Chen, L., “Machine Learning-Based Solar Panel Efficiency Prediction,” IEEE Access, vol. 9, pp. 112345–112356, 2021. [8] Alsharif, M. H., & Kim, J., “Autonomous Solar Panel Cleaning Robot for Large-Scale PV Plants,” International Journal of Robotics and Automation, vol. 37, no. 2, 2022. [9] Elsevier Renewable Energy Journal, “AI-Based Predictive Maintenance for Photovoltaic Systems,” Renewable Energy, vol. 198, pp. 450–462, 2023. [10] National Renewable Energy Laboratory (NREL), “Best Practices for Operation and Maintenance of Photovoltaic Systems,” NREL Technical Report, USA, 2024.
Publication Date
5/7/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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The authors declare no conflict of interest related to this research work.
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