A New Approach for Prediction of Solar Radiation with Using Ensemble Learning Algorithm

  • Kivanc BasaranEmail author
  • Akın Özçift
  • Deniz Kılınç
Research Article - Electrical Engineering


This article investigates the competence of ensemble learning techniques in solar irradiance prediction. It was seen from the literature survey, an ensemble tree model, random forests is studied more frequently as ensemble models. However, ensemble of support vector regression (SVR) and artificial neural networks (ANN) is also possible. So, this study is the first detailed evaluation of ensemble models in solar irradiance estimation domain. Boosting and bagging ensembles of SVR, ANN and decision tree (DT), are developed to estimate solar irradiance in hourly basis in five cities in Turkey. First frequently used base models (SVR, ANN, and DT) are created and tested with the use of 5 years meteorological data. Then boosting and bagging ensembles of the base models are developed and tested with the same data. The base models are compared with their ensemble counterparts in terms of average coefficient of determination (R2) and root mean squared error (RMSE). The comparative results show that boosting and bagging ensemble models improve SVR, ANN, and DT in terms of RMSE between 4.6 and 14.6% in average. The results show empirically that ensemble models improve prediction accuracies of various base regression models and it can be applied to other machine learning models used in solar irradiance prediction.


Solar irradiance Prediction Machine learning Ensemble methods 



Turkish state meteorological service


Sunshine duration fraction


Modified sunshine duration fraction


Adaptive neuro-fuzzy inference system


Autoregressive moving average


Artificial neural network


Multi-layer perceptron


Support vector machine


Support vector regression




Iternative Dichotomizer


K-nearest neighbors


Gradient boosting tree


Random forests


Root mean square error


Coefficient of determination


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Copyright information

© King Fahd University of Petroleum & Minerals 2019

Authors and Affiliations

  1. 1.Department of Energy Systems EngineeringManisa Celal Bayar UniversityManisaTurkey
  2. 2.Department of Software EngineeringManisa Celal Bayar UniversityManisaTurkey

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