Prediction of solar radiation on the horizon using neural network methods, ANFIS and RSM (case study: Sarpol-e-Zahab Township, Iran)

Abstract

Solar energy is one of the clean and healthy energies. Due to the high cost of required equipment to convert solar energy into the desired form, the economic facets must be addressed and the equipment should be installed in areas with higher accessible solar energy. However, due to the complex and time-consuming process of calculating solar radiation, it seems necessary to develop more simple models with higher estimation capability. Therefore, the present study investigated the prediction of solar radiation on the horizon using neural network methods, ANFIS and RSM, in Sarpol-e-Zahab Township, Kermanshah, Iran. In this respect, the meteorological data of this township were collected. Then, the key parameters were selected by performing sensitivity analysis, and models were designed and optimized using ANFIS, ANN, and RSM methods. Moreover, respective correlation coefficients and mean square errors of each method were obtained (ANFIS (0.993 and 0.0005), ANN (0.996 and 0.00029), and RSM (0.996 and 0.00027), respectively). Also, the neural network and response surface methodology were superior to the ANFIS Model in terms of performance, simplicity, and speed. In short, the performance of the response surface methodology was slightly better than that of the neural network.

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Acknowledgements

The authors would like to acknowledge the financial support from Ministry of Science, Research and Technology, Tehran, Iran and the Vice Chancellor for Research and Technology of Razi University of Kermanshah.

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Correspondence to Leila Naderloo.

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Communicated by N V Chalapathi Rao

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Naderloo, L. Prediction of solar radiation on the horizon using neural network methods, ANFIS and RSM (case study: Sarpol-e-Zahab Township, Iran). J Earth Syst Sci 129, 148 (2020). https://doi.org/10.1007/s12040-020-01414-z

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Keywords

  • ANFIS
  • neural network
  • neuro-genetics
  • solar radiation