Earticle

초록

영어

The accumulation of dust on the solar panels causes a significant decrease in the efficiency of the panels, particularly in dry and semi-arid climates, where the energy yield is affected. This paper discusses how the VGG16 DL model can be applied to the real-time detection of dust that covers the solar panel to enhance their maintenance and improve energy efficiency. Using the VGG16, pre-trained on large image datasets, fine-tune this model to classify between clean and dusty solar panels. The model is thus trained on a holistic dataset of solar panels with variation under different environmental conditions. This approach minimizes energy loss, reduces keep costs, and enhances and overall performance and lifespan of solar panels. The method holds considerable promise for solar farms to optimize cleaning schedules and maximize energy production, promoting more sustainable solar energy solutions.

목차

Abstract
I. INTRODUCTION
II. LITERATURE REVIEW
III. PROPOSED METHODOLOGY
IV. RESULTS AND DISCUSSION
V. CONCLUSION
REFERENCES

저자

• Syed Muhammad Ali [ School of Computer Science National College for Business Administration and Economics (NCBA&E) Lahore Pakistan ]
• Naila Sammar Naz [ School of Computer Science National College for Business Administration and Economics (NCBA&E) Lahore Pakistan ]
• Muhammad Saleem [ School of Computer Science National College for Business Administration and Economics (NCBA&E) Lahore Pakistan ]
• Gulraiz Sattar [ School of Computer Science National College for Business Administration and Economics (NCBA&E) Lahore Pakistan ]
• Fahad Ahmed [ School of Computer Science National College for Business Administration and Economics (NCBA&E) Lahore Pakistan ]
• Muhammad Adnan Khan [ School of Computer Science National College for Business Administration and Economics (NCBA&E) Lahore Pakistan ]

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