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

Md Saif Hassan Onim, Zubayar Mahatab Md Sakif, Adil Ahnaf, Ahsan Kabir, Abul Kalam Azad (), Amanullah Maung Than Oo, Rafina Afreen, Sumaita Tanjim Hridy, Mahtab Hossain, Taskeed Jabid and Md Sawkat Ali ()
Additional contact information
Md Saif Hassan Onim: Department of Electrical, Electronic and Communication Engineering, Military Institute of Science and Technology, Mirpur Cantonment, Dhaka 1216, Bangladesh
Zubayar Mahatab Md Sakif: Department of Computer Science and Engineering, East West University, Dhaka 1212, Bangladesh
Adil Ahnaf: Department of Electrical, Electronic and Communication Engineering, Military Institute of Science and Technology, Mirpur Cantonment, Dhaka 1216, Bangladesh
Ahsan Kabir: Department of Electrical, Electronic and Communication Engineering, Military Institute of Science and Technology, Mirpur Cantonment, Dhaka 1216, Bangladesh
Abul Kalam Azad: School of Engineering and Technology, Central Queensland University, 120 Spencer Street, Melbourne, VIC 3000, Australia
Amanullah Maung Than Oo: School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland 1010, New Zealand
Rafina Afreen: Department of Computer Science and Engineering, East West University, Dhaka 1212, Bangladesh
Sumaita Tanjim Hridy: Department of Computer Science and Engineering, East West University, Dhaka 1212, Bangladesh
Mahtab Hossain: Department of Computer Science and Engineering, East West University, Dhaka 1212, Bangladesh
Taskeed Jabid: Department of Computer Science and Engineering, East West University, Dhaka 1212, Bangladesh
Md Sawkat Ali: Department of Computer Science and Engineering, East West University, Dhaka 1212, Bangladesh

Energies, 2022, vol. 16, issue 1, 1-19

Abstract: 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.

Keywords: CNN; SolNet; classification; deep learning; image processing; solar panel; PV; dust (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/1/155/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/1/155/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:16:y:2022:i:1:p:155-:d:1013075

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().