首页|Optoelectronic devices informatics:optimizing DSSC performance using random-forest machine learning al-gorithm
Optoelectronic devices informatics:optimizing DSSC performance using random-forest machine learning al-gorithm
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This paper provides an attempt to utilize machine learning algorithm,explicitly random-forest algorithm,to optimize the performance of dye sensitized solar cells(DSSCs)in terms of conversion efficiency.The optimization is imple-mented with respect to both the mesoporous TiO2 active layer thickness and porosity.Herein,the porosity impact is reflected to the model as a variation in the effective refractive index and dye absorption.Database set has been estab-lished using our data in the literature as well as numerical data extracted from our numerical model.The random-forest model is used for model regression,prediction,and optimization,reaching 99.87％accuracy.Perfect agreement with experimental data was observed,with 4.17％conversion efficiency.
Omar Al-Sabana、Sameh O.Abdellatif
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Electrical Engineering Department,Faculty of Engineering and FabLab in the Centre for Emerging Learning Tech-nology(CELT),The British University in Egypt,Cairo 11387,Egypt
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