Neural Network Modeling of Degradation of Solar Cells

Journal Article · Wed May 25 00:00:00 EDT 2011 · AIP Conference Proceedings

DOI:https://doi.org/10.1063/1.3586995· OSTI ID:21513229

Gupta, Himanshu; Ghosh, Bahniman ^[1]; Banerjee, Sanjay K ^[2]

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1. Department of Electrical Engineering, Indian Institute of Technology, Kanpur, 208016 (India)
2. Department of Electrical and Computer Engineering, University of Texas at Austin, Austin, TX, 78758 (United States)

Neural network modeling has been used to predict the degradation in conversion efficiency of solar cells in this work. The model takes intensity of light, temperature and exposure time as inputs and predicts the conversion efficiency of the solar cell. Backpropagation algorithm has been used to train the network. It is found that the neural network model satisfactorily predicts the degradation in efficiency of the solar cell with exposure time. The error in the computed results, after comparison with experimental results, lies in the range of 0.005-0.01, which is quite low.

• OSTI ID: 21513229
• Journal Information: AIP Conference Proceedings, Vol. 1341, Issue 1; Conference: Escinano2010: 2010 international conference on enabling science and nanotechnology, Kuala Lumpur (Malaysia), 1-3 Dec 2010; Other Information: DOI: 10.1063/1.3586995; (c) 2011 American Institute of Physics; ISSN 0094-243X
• Country of Publication: United States
• Language: English

Related Subjects

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY
97 MATHEMATICAL METHODS AND COMPUTING
75 CONDENSED MATTER PHYSICS
SUPERCONDUCTIVITY AND SUPERFLUIDITY
ALGORITHMS
COMPARATIVE EVALUATIONS
CONVERSION
EFFICIENCY
ERRORS
NEURAL NETWORKS
SILICON
SIMULATION
SOLAR CELLS
VISIBLE RADIATION
DIRECT ENERGY CONVERTERS
ELECTROMAGNETIC RADIATION
ELEMENTS
EQUIPMENT
EVALUATION
MATHEMATICAL LOGIC
PHOTOELECTRIC CELLS
PHOTOVOLTAIC CELLS
RADIATIONS
SEMIMETALS
SOLAR EQUIPMENT