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A Comparative Assessment of Predicting Daily Solar Radiation Using Bat Neural Network (BNN), Generalized Regression Neural Network (GRNN), and Neuro-Fuzzy (NF) System: A Case Study

Author

Listed:
  • Mohammad Mehdi Lotfinejad

    (Department of Computer Engineering and Information Technology, Payame Noor University (PNU), Tehran 19395-4697, Iran)

  • Reza Hafezi

    (Technology Foresight group, Department of Management, Science and Technology, Amirkabir University of Technology (Tehran Polytechnic), Tehran 15916-34311, Iran
    Futures Studies Research Institute, Amirkabir University of Technology (Tehran Polytechnic), Tehran 15916-34311, Iran)

  • Majid Khanali

    (Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj 4111, Iran)

  • Seyed Sina Hosseini

    (Renewable Energies and Environment Department, Faculty of New Sciences and Technologies, University of Tehran, Tehran 14176-14418, Iran
    Hydrogen and Fuel Cell Laboratory, Faculty of New Sciences and Technologies, University of Tehran, Tehran 14176-14418, Iran)

  • Mehdi Mehrpooya

    (Renewable Energies and Environment Department, Faculty of New Sciences and Technologies, University of Tehran, Tehran 14176-14418, Iran
    Hydrogen and Fuel Cell Laboratory, Faculty of New Sciences and Technologies, University of Tehran, Tehran 14176-14418, Iran)

  • Shahaboddin Shamshirband

    (Department of Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh, Vietnam
    Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh, Vietnam)

Abstract

Highly accurate estimating of daily solar radiation by developing an intelligent and robust model has been a subject of prominent concern for many researchers in the past few years. The precise prediction of solar radiation is of great interest and importance to improve the incorporation of solar power plants. In this study, a novel multilayer framework for a particular combination of the bat algorithm (BA) and neural networks (NN) is proposed, which is called bat neural network (BNN), aimed at predicting daily solar radiation over Iran. For appraising the performance of the proposed BNN, daily solar radiation data from four cities of Iran including Jask, Kermanshah, Ramsar, and Tehran are analyzed. The results indicate that among the tested models, BNN gains the best performance in the prediction of daily solar radiation. Among various soft computing approaches, the BA, which is inspired by the nature of microbats’ behaviour, has a significant impact on the optimization of this study.

Suggested Citation

  • Mohammad Mehdi Lotfinejad & Reza Hafezi & Majid Khanali & Seyed Sina Hosseini & Mehdi Mehrpooya & Shahaboddin Shamshirband, 2018. "A Comparative Assessment of Predicting Daily Solar Radiation Using Bat Neural Network (BNN), Generalized Regression Neural Network (GRNN), and Neuro-Fuzzy (NF) System: A Case Study," Energies, MDPI, vol. 11(5), pages 1-15, May.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:5:p:1188-:d:145199
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    References listed on IDEAS

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    3. Horng, Shih-Cheng & Lin, Shieh-Shing, 2019. "Bat algorithm assisted by ordinal optimization for solving discrete probabilistic bicriteria optimization problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 166(C), pages 346-364.
    4. Hafezi, Reza & Akhavan, AmirNaser & Pakseresht, Saeed & A. Wood, David, 2021. "Global natural gas demand to 2025: A learning scenario development model," Energy, Elsevier, vol. 224(C).
    5. Reza Hafezi & Amir Naser Akhavan & Mazdak Zamani & Saeed Pakseresht & Shahaboddin Shamshirband, 2019. "Developing a Data Mining Based Model to Extract Predictor Factors in Energy Systems: Application of Global Natural Gas Demand," Energies, MDPI, vol. 12(21), pages 1-22, October.
    6. Fan, Junliang & Wu, Lifeng & Zhang, Fucang & Cai, Huanjie & Zeng, Wenzhi & Wang, Xiukang & Zou, Haiyang, 2019. "Empirical and machine learning models for predicting daily global solar radiation from sunshine duration: A review and case study in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 100(C), pages 186-212.
    7. Bellido-Jiménez, Juan Antonio & Estévez Gualda, Javier & García-Marín, Amanda Penélope, 2021. "Assessing new intra-daily temperature-based machine learning models to outperform solar radiation predictions in different conditions," Applied Energy, Elsevier, vol. 298(C).
    8. He Jiang, 2023. "Forecasting global solar radiation using a robust regularization approach with mixture kernels," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(8), pages 1989-2010, December.
    9. Mohammad Ehteram & Ali Najah Ahmed & Chow Ming Fai & Haitham Abdulmohsin Afan & Ahmed El-Shafie, 2019. "Accuracy Enhancement for Zone Mapping of a Solar Radiation Forecasting Based Multi-Objective Model for Better Management of the Generation of Renewable Energy," Energies, MDPI, vol. 12(14), pages 1-26, July.
    10. Ahmed Aljanad & Nadia M. L. Tan & Vassilios G. Agelidis & Hussain Shareef, 2021. "Neural Network Approach for Global Solar Irradiance Prediction at Extremely Short-Time-Intervals Using Particle Swarm Optimization Algorithm," Energies, MDPI, vol. 14(4), pages 1-20, February.

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