Modelling Spatial Characteristics of Silicon Solar Cell: Artificial Neural Network Approach
| Authors | R.S. Kamath1 , R.K. Kamat2 |
| Affiliations |
1Department of Computer Studies, Chhatrapati Shahu Institute of Business Education and Research, University Road, Kolhapur 416004, India 2Department of Electronics, Shivaji University, Kolhapur 416004, India |
| Е-mail | |
| Issue | Volume 12, Year 2020, Number 3 |
| Dates | Received 03 February 2020; revised manuscript received 15 June 2020; published online 25 June 2020 |
| Citation | R.S. Kamath, R.K. Kamat, J. Nano- Electron. Phys. 12 No 3, 03021 (2020) |
| DOI | https://doi.org/10.21272/jnep.12(3).03021 |
| PACS Number(s) | 85.30. – z, 88.40.fc |
| Keywords | Artificial neural network, Silicon solar cell (6) , Efficiency (24) , R Programming.R. |
| Annotation |
This research presents Artificial Neural Network (ANN) modelling of silicon solar cells’ spatial characteristics. The dataset for the present study is acquired from the research on silicon solar cells carried out at Shivaji University, India. The silicon-based solar cells are exceptionally popular due to their high efficiency and longer lifetime. An ANN is a mathematical model based on biological neural systems skilled to capture relationship in data to provide higher forecast accuracy. The present investigation aimed at building best possible ANN architecture by tweaking the parameters such as learning algorithm, activation function and number of neurons in hidden neurons. Thus, derived ANN architecture involves three neurons in the hidden layer and logistic activation function for supervised learning. Root Mean Square Error (RMSE) estimate of error rate is used here for assessing the performance of the model. |
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