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Energies 2019, 12(8), 1416; https://doi.org/10.3390/en12081416

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

1
Department of Civil Engineering, Graduate University of Advanced Technology, Kerman 76318-18356, Iran
2
Department of Civil Engineering, Pardisan University, Freidoonkenar 74715-47516, Iran
3
Department of Railroad Construction and Safety Engineering, Dongyang University, Yeongju 36040, Korea
4
Department of Civil Engineering, Tabari University of Babol, Babol 47139-75689, Iran
5
Department of Physical Education, Shahid Bahonar University, Kerman 76169-13439, Iran
6
Department of Land, Water and Environment Research, Korea Institute of Civil Engineering and Building Technology, Goyang 10223, Korea
7
Department of Civil Engineering, Monash University, 23 College Walk, Clayton, VIC 3800, Australia
*
Authors to whom correspondence should be addressed.
Received: 25 February 2019 / Revised: 28 March 2019 / Accepted: 4 April 2019 / Published: 12 April 2019
(This article belongs to the Special Issue Modelling and Simulation of Smart Energy Management Systems)
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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. View Full-Text
Keywords: solar radiation forecasting; multivariate adaptive regression splines; crow search algorithm; complete ensemble empirical mode decomposition with adaptive noise; gene expression programing solar radiation forecasting; multivariate adaptive regression splines; crow search algorithm; complete ensemble empirical mode decomposition with adaptive noise; gene expression programing
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Rezaie-Balf, M.; Maleki, N.; Kim, S.; Ashrafian, A.; Babaie-Miri, F.; Kim, N.W.; Chung, I.-M.; Alaghmand, S. Forecasting Daily Solar Radiation Using CEEMDAN Decomposition-Based MARS Model Trained by Crow Search Algorithm. Energies 2019, 12, 1416.

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