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

Energy ◽  
2017 ◽  
Vol 123 ◽  
pp. 229-240 ◽  
Author(s):  
Furkan Baser ◽  
Haydar Demirhan
Keyword(s):  
Support Vector Machine ◽  
Solar Radiation ◽  
Global Solar Radiation ◽  
Fuzzy Regression ◽  
Support Vector

A support vector machine–firefly algorithm-based model for global solar radiation prediction

Solar Energy ◽  
2015 ◽  
Vol 115 ◽  
pp. 632-644 ◽  
Author(s):  
Lanre Olatomiwa ◽  
Saad Mekhilef ◽  
Shahaboddin Shamshirband ◽  
Kasra Mohammadi ◽  
Dalibor Petković ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Solar Radiation ◽  
Firefly Algorithm ◽  
Global Solar Radiation ◽  
Support Vector

A new hybrid support vector machine–wavelet transform approach for estimation of horizontal global solar radiation

2015 ◽  
Vol 92 ◽  
pp. 162-171 ◽  
Author(s):  
Kasra Mohammadi ◽  
Shahaboddin Shamshirband ◽  
Chong Wen Tong ◽  
Muhammad Arif ◽  
Dalibor Petković ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Wavelet Transform ◽  
Solar Radiation ◽  
Global Solar Radiation ◽  
Support Vector

A support vector machine approach to estimate global solar radiation with the influence of fog and haze

2018 ◽  
Vol 128 ◽  
pp. 155-162 ◽  
Author(s):  
Wanxiang Yao ◽  
Chunxiao Zhang ◽  
Haodong Hao ◽  
Xiao Wang ◽  
Xianli Li
Keyword(s):  
Support Vector Machine ◽  
Solar Radiation ◽  
Global Solar Radiation ◽  
Support Vector

scholarly journals Global Solar Radiation Forecasting using Artificial Neural Network and Support Vector Machine

2021 ◽  
Vol 2129 (1) ◽  
pp. 012079
Author(s):  
Emmanuel Philibus ◽  
Roselina Sallehuddin ◽  
Yusliza Yussof ◽  
Lizawati Mi Yusuf
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Solar Radiation ◽  
Land Surface ◽  
Statistical Tests ◽  
Global Solar Radiation ◽  
Support Vector ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Abstract Global solar radiation (GSoR) forecasting involves predicting future energy from the sun based on past and present data. Literature reveals that not all meteorological stations record solar radiation, some equipments are faulty, and are not available in every location due to high cost. Hence, the need to predict and forecast using predictors such as land surface temperature (LST). Satellite data when were used to complement ground-based stations have been yielding good results. Different artificial intelligence (AI) methods such as Support Vector Machine (SVM) and Artificial Neural Network (ANN) present different forecasting performances. Motivated by existing literature-related contradictions on the performance superiority of ANN and SVM in GSoR forecasting, the two techniques were compared based on several statistical tests. Experimental results show that ANN outperformed SVM by 2.9864% accuracy, making it superior in the forecast of GSoR.

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