SolNet: A Convolutional Neural Network for Detecting Dust on Solar Panels

SolNet: A Convolutional Neural Network for Detecting Dust on Solar Panels

Electricity production from photovoltaic (PV) systems has accelerated in the last few decades. Numerous environmental factors, particularly the buildup of dust on PV panels have resulted in a significant loss in PV energy output. To detect the dust and thus reduce power loss, several techniques are...

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Bibliographic Details
Published in:Energies (Basel) Vol. 16; no. 1; p. 155
Main Authors: Onim, Md Saif Hassan, Sakif, Zubayar Mahatab Md, Ahnaf, Adil, Kabir, Ahsan, Azad, Abul Kalam, Oo, Amanullah Maung Than, Afreen, Rafina, Hridy, Sumaita Tanjim, Hossain, Mahtab, Jabid, Taskeed, Ali, Md Sawkat
Format: Journal Article
Language:English
Published: Basel MDPI AG 01-01-2023
Subjects:
Algorithms
Benchmarks
Cameras
Classification
CNN
Datasets
Deep learning
Dust
Efficiency
Electric power generation
Electric power loss
Electricity
Electricity distribution
Energy output
Environmental factors
Experiments
Humidity
Image processing
Neural networks
Photovoltaic cells
Photovoltaics
Radiation
Solar energy
solar panel
Solar panels
SolNet
Thermal imaging
China
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Summary:Electricity production from photovoltaic (PV) systems has accelerated in the last few decades. Numerous environmental factors, particularly the buildup of dust on PV panels have resulted in a significant loss in PV energy output. To detect the dust and thus reduce power loss, several techniques are being researched, including thermal imaging, image processing, sensors, cameras with IoT, machine learning, and deep learning. In this study, a new dataset of images of dusty and clean panels is introduced and applied to the current state-of-the-art (SOTA) classification algorithms. Afterward, a new convolutional neural network (CNN) architecture, SolNet, is proposed that deals specifically with the detection of solar panel dust accumulation. The performance and results of the proposed SolNet and other SOTA algorithms are compared to validate its efficiency and outcomes where SolNet shows a higher accuracy level of 98.2%. Hence, both the dataset and SolNet can be used as benchmarks for future research endeavors. Furthermore, the classes of the dataset can also be expanded for multiclass classification. At the same time, the SolNet model can be fine-tuned by tweaking the hyperparameters for further improvements.
ISSN:1996-1073
1996-1073
DOI:10.3390/en16010155