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Forecasting Daily Solar Radiation Using CEEMDAN Decomposition-Based MARS Model Trained by Crow Search Algorithm

Author

Listed:
  • Mohammad Rezaie-Balf

    (Department of Civil Engineering, Graduate University of Advanced Technology, Kerman 76318-18356, Iran)

  • Niloofar Maleki

    (Department of Civil Engineering, Pardisan University, Freidoonkenar 74715-47516, Iran)

  • Sungwon Kim

    (Department of Railroad Construction and Safety Engineering, Dongyang University, Yeongju 36040, Korea)

  • Ali Ashrafian

    (Department of Civil Engineering, Tabari University of Babol, Babol 47139-75689, Iran)

  • Fatemeh Babaie-Miri

    (Department of Physical Education, Shahid Bahonar University, Kerman 76169-13439, Iran)

  • Nam Won Kim

    (Department of Land, Water and Environment Research, Korea Institute of Civil Engineering and Building Technology, Goyang 10223, Korea)

  • Il-Moon Chung

    (Department of Land, Water and Environment Research, Korea Institute of Civil Engineering and Building Technology, Goyang 10223, Korea)

  • Sina Alaghmand

    (Department of Civil Engineering, Monash University, 23 College Walk, Clayton, VIC 3800, Australia)

Abstract

The precise forecasting of daily solar radiation (DSR) is receiving prominent attention among thriving solar energy studies. In this study, three standalone models, including gene expression programing (GEP), multivariate adaptive regression splines (MARS), and self-adaptive MARS (SaMARS), were evaluated to forecast DSR. A SaMARS model was classified as MARS model when using the crow search algorithm (CSA). In addition, to overcome the limitations of the standalone models, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) was employed to enhance the accuracy of DSR forecasting. Therefore, three hybrid models including CEEMDAN-GEP, CEEMDAN-MARS, and CEEMDAN-SaMARS were proposed to forecast DSR in Busan and Incheon stations in South Korea. The performance of proposed models were evaluated and affirmed that the accuracy of the CEEMDAN-SaMARS model (NSE = 0.878–0.883) outperformed CEEMDAN-MARS (NSE = 0.819–0.818), CEEMDAN-GEP (NSE = 0.873–0.789), SaMARS (NSE = 0.846–0.769), MARS (NSE = 0.819–0.758), and GEP (NSE = 0.814–0.755) models at both stations. Therefore, it can be concluded that the optimized CEEMDAN-SaMARS model significantly enhanced the accuracy of DSR forecasting compared to that of standalone models.

Suggested Citation

  • Mohammad Rezaie-Balf & Niloofar Maleki & Sungwon Kim & Ali Ashrafian & Fatemeh Babaie-Miri & Nam Won Kim & Il-Moon Chung & Sina Alaghmand, 2019. "Forecasting Daily Solar Radiation Using CEEMDAN Decomposition-Based MARS Model Trained by Crow Search Algorithm," Energies, MDPI, vol. 12(8), pages 1-23, April.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:8:p:1416-:d:222304
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    References listed on IDEAS

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    3. Lu, Yunbo & Wang, Lunche & Zhu, Canming & Zou, Ling & Zhang, Ming & Feng, Lan & Cao, Qian, 2023. "Predicting surface solar radiation using a hybrid radiative Transfer–Machine learning model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
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    6. Peng, Tian & Zhang, Chu & Zhou, Jianzhong & Nazir, Muhammad Shahzad, 2021. "An integrated framework of Bi-directional long-short term memory (BiLSTM) based on sine cosine algorithm for hourly solar radiation forecasting," Energy, Elsevier, vol. 221(C).

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