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

被引:80
作者
Baser, Furkan [1 ]
Demirhan, Haydar [2 ]
机构
[1] Ankara Univ, Fac Sci Appl, Dept Insurance & Actuarial Sci, TR-06100 Ankara, Turkey
[2] RMIT Univ, Sch Sci, Math Sci, Melbourne, Vic 3001, Australia
关键词
Artificial intelligence; Fuzzy regression; Genetic programming; METEONORM; Solar radiation model; Support vector machines; ARTIFICIAL NEURAL-NETWORKS; ESTIMATION MODELS; CLUSTER VALIDITY; C-MEANS; TURKEY; PREDICTION; ALGORITHM; SURFACE;
D O I
10.1016/j.energy.2017.02.008
中图分类号
O414.1 [热力学];
学科分类号
摘要
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. (C) 2017 Elsevier Ltd. All rights reserved.
引用
收藏
页码:229 / 240
页数:12
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