A proposed hybrid model of ANN and KNN for solar cell defects detection and temperature prediction using fuzzy image segmentation
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27360%2F24%3A10255653" target="_blank" >RIV/61989100:27360/24:10255653 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S2405844024078058?via%3Dihub#coi0010" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2405844024078058?via%3Dihub#coi0010</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.heliyon.2024.e31774" target="_blank" >10.1016/j.heliyon.2024.e31774</a>
Alternative languages
Result language
angličtina
Original language name
A proposed hybrid model of ANN and KNN for solar cell defects detection and temperature prediction using fuzzy image segmentation
Original language description
This paper presents a novel hybrid model employing Artificial Neural Networks (ANN) and Mathematical Morphology (MM) for the effective detection of defects in solar cells. Focusing on issues such as broken corners and black edges caused by environmental factors like broken glass cover, dust, and temperature variations. This study utilizes a hybrid model of ANN and K-Nearest Neighbor (KNN) for temperature prediction. This hybrid approach leverages the strengths of both models, potentially opening up new avenues for improved accuracy in temperature forecasting, which is critical for solar energy applications. The significance lies in the interconnectedness of temperature fluctuations and solar cell efficiency, leading to defects. The proposed model aims to predict temperatures accurately, providing insights into potential solar cell efficiency problems. Subsequently, this work studies the transitions to defect detection using Fuzzy C-Means (FCM) clustering and MM techniques. The hybrid model demonstrates accurate temperature prediction with Mean Absolute Percentage Error (MAPE) values of 0.92 %, 0.72 %, and 1.3 % for average, maximum, and minimum temperatures, respectively. The defect detection process yields a detection accuracy (CR) of 96 % and sensitivity of detection (SD) of 89 %. This work is validated compared to the literature work done and by using K-fold cross validation technique. The proposed work emphasizes the improvement in defect detection accuracy and the overall quality enhancement of solar cells.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20704 - Energy and fuels
Result continuities
Project
<a href="/en/project/EH22_008%2F0004631" target="_blank" >EH22_008/0004631: Materials and technologies for sustainable development</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Heliyon
ISSN
2405-8440
e-ISSN
2405-8440
Volume of the periodical
10
Issue of the periodical within the volume
11
Country of publishing house
GB - UNITED KINGDOM
Number of pages
15
Pages from-to
"e31774"
UT code for WoS article
001247677200001
EID of the result in the Scopus database
2-s2.0-85193909300