محفوظ في:
| المؤلفون الرئيسيون: | , , , , , |
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| التنسيق: | Recurso digital |
| اللغة: | |
| منشور في: |
Zenodo
2025
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://doi.org/10.5281/zenodo.16410228 |
| الوسوم: |
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جدول المحتويات:
- <p>Solar photovoltaic (PV) panels were a broadly implemented renewable energy source but their efficiency was<br>substantially influenced by dust accumulation which hindered sunlight absorption and reduced power output Regular cleaning<br>and monitoring were essential to sustain their performance Traditional cleaning mechanisms such as manual or semiautomatic<br>cleaning were often inefficient labor-intensive and costly which demanded the development of automated solutions This research<br>introduced an IoTbased cleaning and monitoring system designed to enhance the efficiency of solar PV panels The system<br>combined realtime data acquisition through IoT sensors to detect dust accumulation and environmental conditions activating an<br>automated cleaning mechanism when necessary Additionally machine learning algorithms analyzed historical data to optimize<br>cleaning schedules maintaining minimal energy loss and improved reliability A review of prevalent dust removal techniques such<br>as passive coatings electrostatic cleaning and robotic solutions revealed that many methods were either highmaintenance or not<br>costeffective for largescale deployment IoTbased solutions when integrated with predictive analytics provided a potential<br>substitute by enabling realtime monitoring and analytical decisionmaking for panel maintenance The outlined methodology<br>enhanced energy output while reducing operational costs and minimizing manual intervention making solar energy systems<br>more efficient and sustainable This innovation contributed to the prolonged effectiveness of solar power by addressing one of its<br>key operational challenges thereby fostering a cleaner and more reliable renewable energy future.</p>