Forecasting of solar radiation using different machine learning approaches


Neural Computing and Applications, vol.35, no.1, pp.887-906, 2023 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 35 Issue: 1
  • Publication Date: 2023
  • Doi Number: 10.1007/s00521-022-07841-x
  • Journal Name: Neural Computing and Applications
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Applied Science & Technology Source, Biotechnology Research Abstracts, Compendex, Computer & Applied Sciences, Index Islamicus, INSPEC, zbMATH
  • Page Numbers: pp.887-906
  • Keywords: Solar radiation, Long short-term memory, Gaussian process regression, Support vector machine regression, Extreme learning machines and K-nearest neighbors, Turkey, MEASURED AIR-TEMPERATURE, SUPPORT VECTOR MACHINE, DATA MINING MODELS, REFERENCE EVAPOTRANSPIRATION, SUNSHINE DURATION, GLOBAL RADIATION, EMPIRICAL-MODELS, PREDICTION, REGRESSION, KERNEL
  • Kayseri University Affiliated: No


© 2022, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.In this study, monthly solar radiation (SR) estimation was performed using five different machine learning-based approaches. The models used are support vector machine regression (SVMR), long short-term memory (LSTM), Gaussian process regression (GPR), extreme learning machines (ELM) and K-nearest neighbors (KNN). Modeling of these approaches was carried out in two stages. In the first stage, VIF analysis was carried out to develop the model. Thus, the input parameters that decrease the performance of the model are removed. In the second stage, remaining input parameters such as meteorological data, station location data and spatial and temporal information were used in the forecasting modeling according to the correlation SR. In this study, the data set is divided into two parts as test and training. 30% was used in the testing phase, and 70% of the data was used in the training phase. When comparing models, the following error statistics were used: Nash–Sutcliffe efficiency coefficient (NSE), mean absolute error (MAE), mean absolute relative error (MARE), root-mean-square error (RMSE) and coefficient of determination (R2). In addition, Taylor diagrams, violin plots, box error, spider plot and Kruskal–Wallis (KW) and ANOVA test were utilized to determine robustness of model's forecast. As a result of the study, the KW test and ANOVA test results showed that the data of many models were from the same population with observations, and it has proved that LSTM and GPR algorithms are applicable, valid and an alternative for SR forecasting in Turkey, which has arid and semi-arid climatic regions.