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Application of machine learning for solar radiation modeling

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Abstract

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 (R2) 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.

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Abbreviations

ANFIS:

Adaptive Network-Based Fuzzy Inference System

BP:

Back-propagation

G oH :

Daily extraterrestrial radiation (W m−2)

H :

Daily global solar radiation (W m−2)

N :

Day length

R h :

Daily average relative humidity (%)

T max :

Daily maximum temperature (K)

T ave :

Daily average temperature (K)

P :

Daily total rainfall

EF:

Efficiency model

f(T ave):

Function based on the daily mean temperature

FIS:

Fuzzy Inference System

GPR:

Gaussian process regression

GSR:

Global solar radiation

QVNN:

Quaternion-valued neural network

GD:

Gradient descent

LSE:

Least-squares error

LM:

Levenberg–Marquardt

MLR:

Multiple linear regression

MAPE:

Mean absolute percentage error

MLP:

Multilayer perceptron

RBF:

Radial bias function

RMSE:

Root mean square error

Φ:

Site latitude

I sc :

Solar constant

Δ:

Solar declination

SC:

Subtractive clustering

ω s :

Sunset hour angle

n :

Sunshine hours

SVM:

Support vector machine

WT:

Wavelet transform

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Acknowledgments

The authors would like to thank the editor in chief and the anonymous referees for their valuable suggestions and useful comments that improved the paper content substantially. Also special thanks to the Meteorological Office Data Center in Khorasan Razavi, Iran, for providing data related to this study. This study was supported by Agricultural Sciences and Natural Resources University of Khuzestan, Iran. The authors are grateful for the support provided by this university.

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Authors and Affiliations

  1. Department of Agricultural Machinery and Mechanization, Faculty of Agricultural Engineering and Rural Development, Agricultural Sciences and Natural Resources University of Khuzestan, P.O. Box: 6341773637, Mollasani, Iran

    Morteza Taki

  2. Department of Biosystems Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran

    Abbas Rohani

  3. Faculty of Engineering, Energy Systems Engineering, Adana Alparslan Türkeş Science and Technology University, Adana, Turkey

    Hasan Yildizhan

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  1. Morteza Taki
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  2. Abbas Rohani
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  3. Hasan Yildizhan
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Contributions

Morteza Taki: conceptualization, methodology, software, investigation, writing—original draft. Abbas Rohani: software, resources, data curation. Hasan Yildizhan: review and editing.

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Correspondence to Morteza Taki.

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Appendix

Appendix

Extraterrestrial solar radiation (GoH)

In this study, daily total extraterrestrial radiation (GoH W m−2) was calculated based on Kalogirou (2014):

$$ {G}_{\mathrm{oH}}=1366.1\left[1+0.033\cos \left(\frac{360n}{365}\right)\right]\left(\cos \phi \cos \delta \cos \omega +\sin \phi \sin \delta \right) $$

where n is the day of the year, φ is the site latitude, δ is the solar angle, and ω is the hour angle.

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Taki, M., Rohani, A. & Yildizhan, H. Application of machine learning for solar radiation modeling. Theor Appl Climatol 143, 1599–1613 (2021). https://doi.org/10.1007/s00704-020-03484-x

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