Application of machine learning for solar radiation modeling
[ X ]
Tarih
2021
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer Wien
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Solar radiation is an important parameter that affects the atmosphere-earth thermal balance and many water and soil processes such as evapotranspiration and plant growth. The modeling of the daily and monthly solar radiation by Gaussian process regression (GPR) with K-fold cross-validation model has been discussed recently. This study evaluated different neural models such as artificial neural network (ANN), support vector machine (SVM), adaptive network-based fuzzy inference system (ANFIS), and multiple linear regression (MLR) for estimating the global solar radiation (daily and monthly) with K-fold cross-validation method. For the appropriate comparison of the models, the randomized complete block (RCB) design applied in the training and test phases. Also, different data sets were evaluated by K-fold cross-validation in each model. The results showed that radial basis function (RBF) model has the lowest error for estimating the monthly and daily solar radiation. In this study, the result of RBF was compared with the GPR models. The conclusion indicated that RBF methodology can predict solar radiation with higher accuracy relative to the GPR model. The results of yearly solar radiation estimation (2009-2014) showed that the RBF model can estimate solar radiation with the MAPE and RMSE of 5.1% and 0.29, respectively. Also, the coefficient of correlation (R-2) between actual and estimated values throughout the year is 98% and can be used by the engineers and other researchers for solar and thermal applications.
Açıklama
Anahtar Kelimeler
Global solar radiation, Modeling, Support vector machine, Radial bias function
Kaynak
Theoretical and Applied Climatology
WoS Q Değeri
Q3
Scopus Q Değeri
Q2
Cilt
143
Sayı
3-4
