Integrated support vector regression and an improved particle swarm optimization-based model for solar radiation prediction

Hamidreza Ghazvinian, Sayed-Farhad Mousavi, Hojat Karami, Saeed Farzin, Mohammad Ehteram, Md Shabbir Hossain, Chow Ming Fai, Huzaifa Bin Hashim, Vijay P Singh, Faizah Che Ros, Ali Najah Ahmed, Haitham Abdulmohsin Afan, Sai Hin Lai and Ahmed El-Shafie

PLOS ONE, 2019, vol. 14, issue 5, 1-24

Abstract: Solar energy is a major type of renewable energy, and its estimation is important for decision-makers. This study introduces a new prediction model for solar radiation based on support vector regression (SVR) and the improved particle swarm optimization (IPSO) algorithm. The new version of algorithm attempts to enhance the global search ability for the PSO. In practice, the SVR method has a few parameters that should be determined through a trial-and-error procedure while developing the prediction model. This procedure usually leads to non-optimal choices for these parameters and, hence, poor prediction accuracy. Therefore, there is a need to integrate the SVR model with an optimization algorithm to achieve optimal choices for these parameters. Thus, the IPSO algorithm, as an optimizer is integrated with SVR to obtain optimal values for the SVR parameters. To examine the proposed model, two solar radiation stations, Adana, Antakya and Konya, in Turkey, are considered for this study. In addition, different models have been tested for this prediction, namely, the M5 tree model (M5T), genetic programming (GP), SVR integrated with four different optimization algorithms SVR-PSO, SVR-IPSO, Genetic Algorithm (SVR-GA), FireFly Algorithm (SVR-FFA) and the multivariate adaptive regression (MARS) model. The sensitivity analysis is performed to achieve the highest accuracy level of the prediction by choosing different input parameters. Several performance measuring indices have been considered to examine the efficiency of all the prediction methods. The results show that SVR-IPSO outperformed M5T and MARS.

Date: 2019
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0217634

DOI: 10.1371/journal.pone.0217634

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