Journal of Biosystems Engineering

Korean Society for Agricultural Machinery (한국농업기계학회)
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Multilayer Perceptron Model to Estimate Solar Radiation with a Solar Module

  • Kim, Joonyong (Research Institute for Agriculture and Life Sciences, Seoul National University) ;
  • Rhee, Joongyong (Research Institute for Agriculture and Life Sciences, Seoul National University) ;
  • Yang, Seunghwan (Environmental Materials & Components Center, Korea Institute of Industrial Technology) ;
  • Lee, Chungu (Department of Biosystems and Biomaterials Science & Engineering, Seoul National University) ;
  • Cho, Seongin (Research Institute for Agriculture and Life Sciences, Seoul National University) ;
  • Kim, Youngjoo (Environmental Materials & Components Center, Korea Institute of Industrial Technology)
  • Received : 2018.11.14
  • Accepted : 2018.11.29
  • Published : 2018.12.01
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Abstract

Purpose: The objective of this study was to develop a multilayer perceptron (MLP) model to estimate solar radiation using a solar module. Methods: Data for the short-circuit current of a solar module and other environmental parameters were collected for a year. For MLP learning, 14,400 combinations of input variables, learning rates, activation functions, numbers of layers, and numbers of neurons were trained. The best MLP model employed the batch backpropagation algorithm with all input variables and two hidden layers. Results: The root-mean-squared error (RMSE) of each learning cycle and its average over three repetitions were calculated. The average RMSE of the best artificial neural network model was $48.13W{\cdot}m^{-2}$. This result was better than that obtained for the regression model, for which the RMSE was $66.67W{\cdot}m^{-2}$. Conclusions: It is possible to utilize a solar module as a power source and a sensor to measure solar radiation for an agricultural sensor node.

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References

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