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

    • Fulltext

       

        Click here to view fulltext PDF


      Permanent link:
      https://www.ias.ac.in/article/fulltext/jess/129/0148

    • Keywords

       

      ANFIS; neural network; neuro-genetics; solar radiation.

    • 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.

    • Author Affiliations

       

      LEILA NADERLOO1

      1. Department of Mechanical Biosystems Engineering, College of Agriculture and Natural Science, Razi University, Kermanshah 67571, Iran.
    • Dates

       
  • Journal of Earth System Science | News

    • Editorial Note on Continuous Article Publication

      Posted on July 25, 2019

      Click here for Editorial Note on CAP Mode

© 2021-2022 Indian Academy of Sciences, Bengaluru.