Single- and combined-source typical metrological year solar energy data modelling

Afzal A., Buradi A., Alwetaishi M., Ağbulut Ü., Kim B., Kim H., ...More

Journal of Thermal Analysis and Calorimetry, vol.148, no.22, pp.12501-12523, 2023 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 148 Issue: 22
  • Publication Date: 2023
  • Doi Number: 10.1007/s10973-023-12604-4
  • Journal Name: Journal of Thermal Analysis and Calorimetry
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Chemical Abstracts Core, Chimica, Communication Abstracts, Compendex, Index Islamicus, INSPEC, Metadex, Civil Engineering Abstracts
  • Page Numbers: pp.12501-12523
  • Keywords: Energy data integration, Machine learning algorithms, Renewable energy prediction, Solar energy modelling, Solar parameter forecasting, Typical meteorological year
  • Yıldız Technical University Affiliated: No


Prediction of solar energy data is very crucial for the effective utilization of freely available renewable energy abundantly in nature. Solar energy data are widely available which must be carefully prepared and arranged for modelling. In this work, typical meteorological year (TMY) data made available by the Korea institute of energy research (KIER) and the National renewable energy laboratory (NREL) are used for modelling in different phases. TMY data at single-point location and multiple locations from KIER are initially used for training of machine learning (ML) algorithms. Later, the TMY data from NREL and KIER are combined and then modelled using radius nearest neighbour (RNN), decision tree regressor (DTR), random forest regressor (RFR), and X-gradient boosting (XGB) algorithms. The solar energy parameters modelled in this work are dew point temperature (DPT), dry bulb temperature (DBT), relative humidity (RH), surface pressure (SP), windspeed (WS), and solar insolation of horizontal plane (IHP). Quantitative analysis of the algorithms is also performed in each stage of the work. The modelling indicates that the DBT, DPT, RH, and SP are able to be predicted with a minimum accuracy of over 90% in each stage. The WS and IHP data when modelled from a single-source TMY data provide superior accuracy than when they are combined. RFR and XGB have outperformed overall as they provide good accuracy for WS and IHP data as well. RNN and DTR achieved 100% accuracy in training, while RFR and XGB showed slightly lower training accuracy due to their avoidance of overfitting. There are errors in testing for RNN/DTR. Using RNN/DTR, the training errors are 0% in all cases, while in some cases like DTP the error by RFR/XGB up to 3%, whereas RNN/DTR testing errors go up to 5% and in case of RFR/XGB they are up to 7.5%. For RH modelling RFR/XGB, training errors are max 6%. RNN/DTR testing errors go up to 11%, while for RFR/XGB up to 7.5% which indicates their robustness. It is observed that many solar parameters, when combined with different source data, can be predicted easily with good accuracy, while WS and IHP become a little bit challenging to model.