A fuzzy regression with support vector machine approach to the estimation of horizontal global solar radiation

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@Article{Baser:2017:Energy,
  author =       "Furkan Baser and Haydar Demirhan",
  title =        "A fuzzy regression with support vector machine
                 approach to the estimation of horizontal global solar
                 radiation",
  journal =      "Energy",
  volume =       "123",
  pages =        "229--240",
  year =         "2017",
  ISSN =         "0360-5442",
  DOI =          "doi:10.1016/j.energy.2017.02.008",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0360544217301822",
  abstract =     "Accurate estimation of the amount of horizontal global
                 solar radiation for a particular field is an important
                 input for decision processes in solar radiation
                 investments. In this article, we focus on the
                 estimation of yearly mean daily horizontal global solar
                 radiation by using an approach that utilizes fuzzy
                 regression functions with support vector machine
                 (FRF-SVM). This approach is not seriously affected by
                 outlier observations and does not suffer from the
                 over-fitting problem. To demonstrate the utility of the
                 FRF-SVM approach in the estimation of horizontal global
                 solar radiation, we conduct an empirical study over a
                 dataset collected in Turkey and applied the FRF-SVM
                 approach with several kernel functions. Then, we
                 compare the estimation accuracy of the FRF-SVM approach
                 to an adaptive neuro-fuzzy system and a coplot
                 supported-genetic programming approach. We observe that
                 the FRF-SVM approach with a Gaussian kernel function is
                 not affected by both outliers and over-fitting problem
                 and gives the most accurate estimates of horizontal
                 global solar radiation among the applied approaches.
                 Consequently, the use of hybrid fuzzy functions and
                 support vector machine approaches is found beneficial
                 in long-term forecasting of horizontal global solar
                 radiation over a region with complex climatic and
                 terrestrial characteristics.",
  keywords =     "genetic algorithms, genetic programming, Artificial
                 intelligence, Fuzzy regression, METEONORM, Solar
                 radiation model, Support vector machines",
}

Genetic Programming entries for Furkan Baser Haydar Demirhan

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