Deep Learning-Based Dust Detection on Solar Panels: A Low-Cost Sustainable Solution for Increased Solar Power Generation

Aadel Mohammed Alatwi, Hani Albalawi, Abdul Wadood (), Hafeez Anwar and Hazem M. El-Hageen
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Aadel Mohammed Alatwi: Renewable Energy and Environmental Technology Center, University of Tabuk, Tabuk 47913, Saudi Arabia
Hani Albalawi: Renewable Energy and Environmental Technology Center, University of Tabuk, Tabuk 47913, Saudi Arabia
Abdul Wadood: Renewable Energy and Environmental Technology Center, University of Tabuk, Tabuk 47913, Saudi Arabia
Hafeez Anwar: Department of Computer Science, National University of Computer and Emerging Science (NUCES-FAST), Jamrud Road 160 Industrial Estate Road, Phase 1 Hayatabad, Peshawar 25100, Khyber Pakhtunkhwa, Pakistan
Hazem M. El-Hageen: Renewable Energy and Environmental Technology Center, University of Tabuk, Tabuk 47913, Saudi Arabia

Sustainability, 2024, vol. 16, issue 19, 1-16

Abstract: The world is shifting towards renewable energy sources due to the harmful effects of fossils fuel-based power generation in the form of global warming and climate change. When it comes to renewable energy sources, solar-based power generation remains on top of the list as a clean and carbon cutting alternative to the fossil fuels. Naturally, the sites chosen for installing solar parks to generate electricity are the ones that get maximum solar radiance throughout the year. Consequently, such sites offer challenges for the solar panels such as increased temperature, humidity and high dust levels that negatively affect their power generation capability. In this work, we are more concerned with the detection of dust from the images of the solar panels so that the cleaning process can be done in time to avoid power loses due to dust accumulation on the surface of solar panels. To this end, we utilize state-of-art deep learning-based image classification models and evaluate them on a publicly available dataset to identify the one that gives maximum classification accuracy for dusty solar panel detection. We utilize pre-trained models of 20 deep learning models to encode the images that are then used to train and validate four variants of a support vector machine. Among the 20 models, we get the maximum classification of 86.79% when the images are encoded with the pre-trained model of DenseNet169 and then use these encodings with a linear SVM for image classification.

Keywords: renewable energy; image classification; artificial intelligence; deep learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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