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

Mohammad Rezaie-Balf, Niloofar Maleki, Sungwon Kim, Ali Ashrafian, Fatemeh Babaie-Miri, Nam Won Kim, Il-Moon Chung and Sina Alaghmand
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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

Energies, 2019, vol. 12, issue 8, 1-23

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.

Keywords: solar radiation forecasting; multivariate adaptive regression splines; crow search algorithm; complete ensemble empirical mode decomposition with adaptive noise; gene expression programing (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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