A fuzzy regression with support vector machine approach to the estimation of horizontal global solar radiation
Created by W.Langdon from
gp-bibliography.bib Revision:1.8051
- @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",
-
keywords = "genetic algorithms, genetic programming, Artificial
intelligence, Fuzzy regression, METEONORM, Solar
radiation model, Support vector machines",
-
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 uses 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.",
- }
Genetic Programming entries for
Furkan Baser
Haydar Demirhan
Citations