Affiliations 

  • 1 Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University, Semnan, Iran
  • 2 School of Energy, Geoscience, Infrastructure and Society, Department of Civil Engineering, Heriot-Watt University, Putrajaya, Malaysia
  • 3 Intitute of Energy Infrastructure (IEI), Department of Civil Engineering, Universiti Tenaga Nasional, Selangor, Malaysia
  • 4 Department of Civil Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia
  • 5 Department of Biological and Agricultural Engineering, Zachry Department of Civil Engineering, Texas A&M University, College Station, Texas, United States of America
  • 6 Department of Environmental and Green Technology, Malaysia-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia (UTM), Kuala Lumpur, Malaysia
PLoS One, 2019;14(5):e0217634.
PMID: 31150467 DOI: 10.1371/journal.pone.0217634

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.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.