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Daily Global Solar Radiation Prediction with Hybrid LSTM-SVM:The Case of Nusaybin.

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Title: Daily Global Solar Radiation Prediction with Hybrid LSTM-SVM:The Case of Nusaybin.
Authors: Tanyıldızı Ağır, Tuba1 (AUTHOR) tuba.tanyildiziagir@batman.edu.tr
Source: Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. ). Mar2026, Vol. 51 Issue 6, p7311-7324. 14p.
Subject Terms: *Machine learning, *Global radiation, *Data analysis, *Model validation, *Energy management
Geographic Terms: Mardin (Turkey)
Abstract: Solar radiation forecasting is important for energy management, energy stability and installation of solar energy systems. In this study, hybrid LSTM-SVM was developed to estimate the daily solar radiation of Nusaybin district of Mardin province. To compare the hybrid LSTM-SVM, support vector machines (SVM), long short-term memory (LSTM), decision tree and K-nearest neighbor were used. The performance of the models was evaluated with the help of coefficient of determination ( R 2 ), mean square error (MSE), root-mean-square error (RMSE), normalized root-mean-square error (NRMSE), mean absolute percentage error (MAPE) and Taylor diagram. The regression curve of the models was drawn and their solar radiation prediction performances were compared. The correlation matrix determined the correlation between data. R 2 , MSE, RMSE, NRMSE and MAPE of the hybrid LSTM-SVM were 0.962, 2.0191e-04, 0.0142, 0.0384 and 42.7962, respectively. The proposed hybrid LSTM-SVM had higher performance than other compared models. Taylor diagram proved that the prediction accuracy of the hybrid LSTM-SVM model is good. A correct correlation of 0.8315 between solar radiation and sunshine duration was determined by the correlation matrix. The meteorological data that most affected solar radiation was sunshine duration. As a result, the hybrid LSTM-SVM can be used as an alternative method for solar radiation estimation. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 193141673
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PubType: Academic Journal
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  Data: Daily Global Solar Radiation Prediction with Hybrid LSTM-SVM:The Case of Nusaybin.
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  Data: *<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Global+radiation%22">Global radiation</searchLink><br />*<searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink><br />*<searchLink fieldCode="DE" term="%22Model+validation%22">Model validation</searchLink><br />*<searchLink fieldCode="DE" term="%22Energy+management%22">Energy management</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Mardin+%28Turkey%29%22">Mardin (Turkey)</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Solar radiation forecasting is important for energy management, energy stability and installation of solar energy systems. In this study, hybrid LSTM-SVM was developed to estimate the daily solar radiation of Nusaybin district of Mardin province. To compare the hybrid LSTM-SVM, support vector machines (SVM), long short-term memory (LSTM), decision tree and K-nearest neighbor were used. The performance of the models was evaluated with the help of coefficient of determination ( R 2 ), mean square error (MSE), root-mean-square error (RMSE), normalized root-mean-square error (NRMSE), mean absolute percentage error (MAPE) and Taylor diagram. The regression curve of the models was drawn and their solar radiation prediction performances were compared. The correlation matrix determined the correlation between data. R 2 , MSE, RMSE, NRMSE and MAPE of the hybrid LSTM-SVM were 0.962, 2.0191e-04, 0.0142, 0.0384 and 42.7962, respectively. The proposed hybrid LSTM-SVM had higher performance than other compared models. Taylor diagram proved that the prediction accuracy of the hybrid LSTM-SVM model is good. A correct correlation of 0.8315 between solar radiation and sunshine duration was determined by the correlation matrix. The meteorological data that most affected solar radiation was sunshine duration. As a result, the hybrid LSTM-SVM can be used as an alternative method for solar radiation estimation. [ABSTRACT FROM AUTHOR]
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        Value: 10.1007/s13369-025-10322-7
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      – Code: eng
        Text: English
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        PageCount: 14
        StartPage: 7311
    Subjects:
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Global radiation
        Type: general
      – SubjectFull: Data analysis
        Type: general
      – SubjectFull: Model validation
        Type: general
      – SubjectFull: Energy management
        Type: general
      – SubjectFull: Mardin (Turkey)
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      – TitleFull: Daily Global Solar Radiation Prediction with Hybrid LSTM-SVM:The Case of Nusaybin.
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              M: 03
              Text: Mar2026
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              Y: 2026
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