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Erschienen in:
2024 | OriginalPaper | Buchkapitel
Improving Solar Panel Efficiency: A CNN-Based System for Dust Detection and Maintenance
verfasst von : Aditta Ghosh, Sadia Afrin, Rifat Sultana Tithy, Fayjul Nahid, Farhana Alam, Ahmed Wasif Reza
Erschienen in: Proceedings of the 2nd International Conference on Big Data, IoT and Machine Learning
Verlag: Springer Nature Singapore
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Abstract
The demand for renewable energy has increased steadily in recent years as people become more aware of their carbon footprint. This has led to a growing need for energy sources that are both sustainable and environmentally friendly. Solar power has emerged as a popular option for generating electricity but has challenges. One of the biggest problems facing solar panels is dust and other garbage buildup, which can reduce their efficiency and output. While keeping solar panels clean around the clock is difficult, automated detection and cleaning systems can help. In this paper, we propose an image processing-based approach that uses a convolutional neural network (CNN) with the popular AlexNet architecture to detect dust on solar panels. Our model achieved an 85% recognition rate for dust detection, which could significantly improve solar panel efficiency. By automating the detection and cleaning process, we can maximize electricity generation and make solar power a viable option for sustainable energy production.