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