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

journal contribution
posted on 2023-03-14, 00:57 authored by Md Saif Hassan Onim, Zubayar Mahatab Md Sakif, Adil Ahnaf, Ahsan Kabir, Abul Kalam Azad, Aman Maung Than OoAman Maung Than Oo, Rafina Afreen, Sumaita Tanjim Hridy, Mahtab Hossain, Taskeed Jabid, Md Sawkat Ali
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.

History

Journal

Energies

Volume

16

Article number

155

Pagination

1-19

Location

Basel, Switzerland

ISSN

1996-1073

eISSN

1996-1073

Language

English

Publication classification

C1.1 Refereed article in a scholarly journal

Issue

1

Publisher

MDPI

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