Modelling of Random Textured Tandem Silicon Solar Cells Characteristics: Decision Tree Approach

Author(s) R.S. Kamath1, R.K. Kamat2
Affiliations

1 Department of Computer Studies, Chhatrapati Shahu Institute of Business Education and Research, University Road, 416004 Kolhapur

2 Department of Electronics, Shivaji University, 416004 Kolhapur

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Issue Volume 8, Year 2016, Number 4
Dates Received 04 July 2016; revised manuscript received 22 November 2016; published online 29 November 2016
Citation R.S. Kamath, R.K. Kamat, J. Nano- Electron. Phys. 8 No 4(1), 04021 (2016)
DOI 10.21272/jnep.8(4(1)).04021
PACS Number(s) 85.30. – z, 88.40.fc
Key words Silicon Solar Cell (6) , Decision Tree, Efficiency (15) , Rattle.
Annotation We report decision tree (DT) modeling of randomly textured tandem silicon solar cells characteristics. The photovoltaic modules of silicon-based solar cells are extremely popular due to their high efficiency and longer lifetime. Decision tree model is one of the most common data mining models can be used for predictive analytics. The reported investigation depicts optimum decision tree architecture achieved by tuning parameters such as Min split, Min bucket, Max depth and Complexity. DT model, thus derived is easy to understand and entails recursive partitioning approach implemented in the “rpart” package. Moreover the performance of the model is evaluated with reference Mean Square Error (MSE) estimate of error rate. The modeling of the random textured silicon solar cells reveals strong correlation of efficiency with “Fill factor” and “thickness of a-Si layer”.

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