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Comparison of Wavelet Artificial Neural Network, Wavelet Support Vector Machine, and Adaptive Neuro-Fuzzy Inference System Methods in Estimating Total Solar Radiation in Iraq

Author

Listed:
  • Wongchai Anupong

    (Department of Agricultural Economy and Development, Faculty of Agriculture, Chiang Mai University, Chiang Mai 50200, Thailand)

  • Muhsin Jaber Jweeg

    (College of Technical Engineering, Al-Farahidi University, Baghdad 10001, Iraq)

  • Sameer Alani

    (The University of Mashreq, Baghdad 10001, Iraq)

  • Ibrahim H. Al-Kharsan

    (Computer Technical Engineering Department, College of Technical Engineering, The Islamic University, Najaf 54001, Iraq)

  • Aníbal Alviz-Meza

    (Grupo de Investigación en Deterioro de Materiales, Transición Energética y Ciencia de datos DANT3, Facultad de Ingenieria y Urbanismo, Universidad Señor de Sipán, Km 5 Via Pimentel, Chiclayo 14001, Peru)

  • Yulineth Cárdenas-Escrocia

    (GIOPEN, Energy Optimization Research Group, Energy Department, Universidad de la Costa (CUC), Cl. 58 ##55-66, Barranquilla 080016, Atlántico, Colombia)

Abstract

Estimating the amount of solar radiation is very important in evaluating the amount of energy that can be received from the sun for the construction of solar power plants. Using machine learning tools to estimate solar energy can be a helpful method. With a high number of sunny days, Iraq has a high potential for using solar energy. This study used the Wavelet Artificial Neural Network (WANN), Wavelet Support Vector Machine (WSVM), and Adaptive Neuro-Fuzzy Inference System (ANFIS) techniques to estimate solar energy at Wasit and Dhi Qar stations in Iraq. RMSE, EMA, R 2 , and IA criteria were used to evaluate the performance of the techniques and compare the results with the actual measured value. The results showed that the WANN and WSVM methods had similar results in solar energy modeling. However, the results of the WANN technique were slightly better than the WSVM technique. In Wasit and Dhi Qar stations, the value of R 2 for the WANN and WSVM methods was 0.89 and 0.86, respectively. The value of R 2 in the WANN and WSVM methods in Wasit and Dhi Qar stations was 0.88 and 0.87, respectively. The ANFIS technique also obtained acceptable results. However, compared to the other two techniques, the ANFIS results were lower, and the R 2 value was 0.84 and 0.83 in Wasit and Dhi Qar stations, respectively.

Suggested Citation

  • Wongchai Anupong & Muhsin Jaber Jweeg & Sameer Alani & Ibrahim H. Al-Kharsan & Aníbal Alviz-Meza & Yulineth Cárdenas-Escrocia, 2023. "Comparison of Wavelet Artificial Neural Network, Wavelet Support Vector Machine, and Adaptive Neuro-Fuzzy Inference System Methods in Estimating Total Solar Radiation in Iraq," Energies, MDPI, vol. 16(2), pages 1-14, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:2:p:985-:d:1036987
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    1. Luis O. Lara-Cerecedo & Jesús F. Hinojosa & Nun Pitalúa-Díaz & Yasuhiro Matsumoto & Alvaro González-Angeles, 2023. "Prediction of the Electricity Generation of a 60-kW Photovoltaic System with Intelligent Models ANFIS and Optimized ANFIS-PSO," Energies, MDPI, vol. 16(16), pages 1-26, August.
    2. Mohamed A. Ali & Ashraf Elsayed & Islam Elkabani & Mohammad Akrami & M. Elsayed Youssef & Gasser E. Hassan, 2023. "Optimizing Artificial Neural Networks for the Accurate Prediction of Global Solar Radiation: A Performance Comparison with Conventional Methods," Energies, MDPI, vol. 16(17), pages 1-30, August.

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    Keywords

    solar energy; WANN; WSVM; ANFIS;
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