Skip to main content
SpringerLink
Log in

Forecasting of solar radiation using different machine learning approaches

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Availability of data and material

Climatic data and hydrometric data were provided by the General Directorate of State Meteorological Affairs (DMİ) and General Directorate of State Hydralics Works (DSİ).

Code availability

Not applicable.

References

  1. Keller B, Costa AMS (2011) A Matlab GUI for calculating the solar radiation and shading of surfaces on the earth. Comput Appl Eng Educ 19:161–170. https://doi.org/10.1002/cae.20301

    Article  Google Scholar 

  2. Beer C, Reichstein M, Tomelleri E, Ciais P, Jung M, Carvalhais N et al (2010) Terrestrial gross carbon dioxide uptake: global distribution and covariation with climate. Science (80-) 329:834–838. https://doi.org/10.1126/science.1184984

    Article  Google Scholar 

  3. Islam MD, Kubo I, Ohadi M, Alili AA (2009) Measurement of solar energy radiation in Abu Dhabi. UAE Appl Energy 86:511–515. https://doi.org/10.1016/j.apenergy.2008.07.012

    Article  Google Scholar 

  4. Khatib T, Mohamed A, Sopian K (2012) A review of solar energy modeling techniques. Renew Sustain Energy Rev 16:2864–2869. https://doi.org/10.1016/j.rser.2012.01.064

    Article  Google Scholar 

  5. Meza F, Varas E (2000) Estimation of mean monthly solar global radiation as a function of temperature. Agric For Meteorol 100:231–241. https://doi.org/10.1016/S0168-1923(99)00090-8

    Article  Google Scholar 

  6. Kisi O (2014) Modeling solar radiation of Mediterranean region in Turkey by using fuzzy genetic approach. Energy 64:429–436. https://doi.org/10.1016/j.energy.2013.10.009

    Article  Google Scholar 

  7. Panwar NL, Kaushik SC, Kothari S (2011) Role of renewable energy sources in environmental protection: a review. Renew Sustain Energy Rev 15:1513–1524. https://doi.org/10.1016/j.rser.2010.11.037

    Article  Google Scholar 

  8. Park J-K, Das A, Park J-H (2015) A new approach to estimate the spatial distribution of solar radiation using topographic factor and sunshine duration in South Korea. Energy Convers Manag 101:30–39. https://doi.org/10.1016/j.enconman.2015.04.021

    Article  Google Scholar 

  9. Purohit I, Purohit P (2015) Inter-comparability of solar radiation databases in Indian context. Renew Sustain Energy Rev 50:735–747. https://doi.org/10.1016/j.rser.2015.05.020

    Article  Google Scholar 

  10. Wild M (2009) Global dimming and brightening: a review. J Geophys Res 114:D00D16. https://doi.org/10.1029/2008JD011470

    Article  Google Scholar 

  11. Wang L, Kisi O, Zounemat-Kermani M, Salazar GA, Zhu Z, Gong W (2016) Solar radiation prediction using different techniques: model evaluation and comparison. Renew Sustain Energy Rev 61:384–397. https://doi.org/10.1016/j.rser.2016.04.024

    Article  Google Scholar 

  12. Ndulue E, Onyekwelu I, Ogbu KN, Ogwo V (2019) Performance evaluation of solar radiation equations for estimating reference evapotranspiration (ETo) in a humid tropical environment. J Water L Dev 42:124–135. https://doi.org/10.2478/jwld-2019-0053

    Article  Google Scholar 

  13. Ododo JC, Sulaiman AT, Aidan J, Yuguda MM, Ogbu FA (1995) The importance of maximum air temperature in the parameterisation of solar radiation in Nigeria. Renew Energy 6:751–763. https://doi.org/10.1016/0960-1481(94)00097-P

    Article  Google Scholar 

  14. Bandyopadhyay A, Bhadra A, Raghuwanshi NS, Singh R (2008) Estimation of monthly solar radiation from measured air temperature extremes. Agric For Meteorol 148:1707–1718. https://doi.org/10.1016/j.agrformet.2008.06.002

    Article  Google Scholar 

  15. Ododo JC (1997) Prediction of solar radiation using only maximum temperature and relative humidity: south-east and north-east Nigeria. Energy Convers Manag 38:1807–1814

    Article  Google Scholar 

  16. Rehman S, Mohandes M (2008) Artificial neural network estimation of global solar radiation using air temperature and relative humidity. Energy Policy 36:571–576. https://doi.org/10.1016/j.enpol.2007.09.033

    Article  Google Scholar 

  17. Kisi O, Alizamir M, Trajkovic S, Shiri J, Kim S (2020) Solar radiation estimation in Mediterranean climate by weather variables using a novel Bayesian model averaging and machine learning methods. Neural Process Lett 52:2297–2318. https://doi.org/10.1007/s11063-020-10350-4

    Article  Google Scholar 

  18. Alsafadi M, Filik ÜB (2020) Hourly global solar radiation estimation based on machine learning methods in Eskişehir. Eskişehir Tech Univ J Sci Technol A Appl Sci Eng 21:294–313. https://doi.org/10.18038/estubtda.650497

    Article  Google Scholar 

  19. Kumar R, Aggarwal RK, Sharma JD (2015) Comparison of regression and artificial neural network models for estimation of global solar radiations. Renew Sustain Energy Rev 52:1294–1299. https://doi.org/10.1016/j.rser.2015.08.021

    Article  Google Scholar 

  20. Chabane F, Arif A, Benramache S (2020) Prediction of the solar radiation map on algeria by latitude and longitude coordinates. Tec Ital J Eng Sci 64:213–215. https://doi.org/10.18280/ti-ijes.642-413

    Article  Google Scholar 

  21. Rahimikhoob A, Behbahani SMR, Banihabib ME (2013) Comparative study of statistical and artificial neural network’s methodologies for deriving global solar radiation from NOAA satellite images. Int J Climatol 33:480–486. https://doi.org/10.1002/joc.3441

    Article  Google Scholar 

  22. Polo J, Antonanzas-Torres F, Vindel JM, Ramirez L (2014) Sensitivity of satellite-based methods for deriving solar radiation to different choice of aerosol input and models. Renew Energy 68:785–792. https://doi.org/10.1016/j.renene.2014.03.022

    Article  Google Scholar 

  23. Ahmad MJ, Tiwari GN (2011) Solar radiation models—a review. Int J Energy Res 35:271–290. https://doi.org/10.1002/er.1690

    Article  Google Scholar 

  24. Sonmete MH, Ertekin C, Menges HO, Hacıseferoğullari H, Evrendilek F (2011) Assessing monthly average solar radiation models: a comparative case study in Turkey. Environ Monit Assess 175:251–277. https://doi.org/10.1007/s10661-010-1510-8

    Article  Google Scholar 

  25. Citakoglu H (2015) Comparison of artificial intelligence techniques via empirical equations for prediction of solar radiation. Comput Electron Agric 118:28–37. https://doi.org/10.1016/j.compag.2015.08.020

    Article  Google Scholar 

  26. Yacef R, Mellit A, Belaid S, Şen Z (2014) New combined models for estimating daily global solar radiation from measured air temperature in semi-arid climates: application in Ghardaïa, Algeria. Energy Convers Manag 79:606–615. https://doi.org/10.1016/j.enconman.2013.12.057

    Article  Google Scholar 

  27. Citakoglu H, Babayigit B, Haktanir NA (2020) Solar radiation prediction using multi-gene genetic programming approach. Theor Appl Climatol 142:885–897. https://doi.org/10.1007/s00704-020-03356-4

    Article  Google Scholar 

  28. Ozoegwu CG (2019) Artificial neural network forecast of monthly mean daily global solar radiation of selected locations based on time series and month number. J Clean Prod 216:1–13. https://doi.org/10.1016/j.jclepro.2019.01.096

    Article  Google Scholar 

  29. Citakoglu H, Demir V (2021) Forecasting solar radiation using deep learning: the case of Turkey. In: International World energy conference, pp 167–175

  30. Cano D, Monget JM, Albuisson M, Guillard H, Regas N, Wald L (1986) A method for the determination of the global solar radiation from meteorological satellite data. Sol Energy 37:31–39. https://doi.org/10.1016/0038-092X(86)90104-0

    Article  Google Scholar 

  31. Al-Mostafa ZA, Maghrabi AH, Al-Shehri SM (2014) Sunshine-based global radiation models: a review and case study. Energy Convers Manag 84:209–216. https://doi.org/10.1016/j.enconman.2014.04.021

    Article  Google Scholar 

  32. Badescu V, Dumitrescu A (2016) Simple solar radiation modelling for different cloud types and climatologies. Theor Appl Climatol 124:141–160. https://doi.org/10.1007/s00704-015-1400-7

    Article  Google Scholar 

  33. Samuel CN (2017) A comprehensive review of empirical models for estimating global solar radiation in Africa. Renew Sustain Energy Rev 78:955–995. https://doi.org/10.1016/j.rser.2017.04.101

    Article  Google Scholar 

  34. Fan J, Chen B, Wu L, Zhang F, Lu X, Xiang Y (2018) Evaluation and development of temperature-based empirical models for estimating daily global solar radiation in humid regions. Energy 144:903–914. https://doi.org/10.1016/j.energy.2017.12.091

    Article  Google Scholar 

  35. Fan J, Wu L, Zhang F, Cai H, Zeng W, Wang X et al (2019) Empirical and machine learning models for predicting daily global solar radiation from sunshine duration: a review and case study in China. Renew Sustain Energy Rev 100:186–212. https://doi.org/10.1016/j.rser.2018.10.018

    Article  Google Scholar 

  36. Sharafati A, Khosravi K, Khosravinia P, Ahmed K, Salman SA, Yaseen ZM et al (2019) The potential of novel data mining models for global solar radiation prediction. Int J Environ Sci Technol 16:7147–7164. https://doi.org/10.1007/s13762-019-02344-0

    Article  Google Scholar 

  37. Tao H, Ewees AA, Al-Sulttani AO, Beyaztas U, Hameed MM, Salih SQ et al (2021) Global solar radiation prediction over North Dakota using air temperature: development of novel hybrid intelligence model. Energy Rep 7:136–157. https://doi.org/10.1016/j.egyr.2020.11.033

    Article  Google Scholar 

  38. Guermoui M, Melgani F, Gairaa K, Mekhalfi ML (2020) A comprehensive review of hybrid models for solar radiation forecasting. J Clean Prod. https://doi.org/10.1016/j.jclepro.2020.120357

    Article  Google Scholar 

  39. Citakoglu H (2021) Comparison of multiple learning artificial intelligence models for estimation of long-term monthly temperatures in Turkey. Arab J Geosci 14:2131. https://doi.org/10.1007/s12517-021-08484-3

    Article  Google Scholar 

  40. Ly HB, Nguyen TA, Pham BT (2021) Estimation of soil cohesion using machine learning method: a random forest approach. Adv Civ Eng. https://doi.org/10.1155/2021/8873993

    Article  Google Scholar 

  41. Heddam S, Kisi O (2017) Extreme learning machines: a new approach for modeling dissolved oxygen (DO) concentration with and without water quality variables as predictors. Environ Sci Pollut Res 24:16702–16724. https://doi.org/10.1007/s11356-017-9283-z

    Article  Google Scholar 

  42. Naghibi SA, Moghaddam DD, Kalantar B, Pradhan B, Kisi O (2017) A comparative assessment of GIS-based data mining models and a novel ensemble model in groundwater well potential mapping. J Hydrol 548:471–483. https://doi.org/10.1016/j.jhydrol.2017.03.020

    Article  Google Scholar 

  43. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9:1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735

    Article  Google Scholar 

  44. Hrnjica B, Mehr AD (2020) Energy demand forecasting using deep learning. In: EAI/Springer ınnovations in communication and computing, pp 71–104. https://doi.org/10.1007/978

  45. Sattari MT, Apaydin H, Band SS, Mosavi A, Prasad R (2021) Comparative analysis of kernel-based versus ANN and deep learning methods in monthly reference evapotranspiration estimation. Hydrol Earth Syst Sci 25:603–618. https://doi.org/10.5194/hess-25-603-2021

    Article  Google Scholar 

  46. Rahimzad M, Moghaddam Nia A, Zolfonoon H, Soltani J, Danandeh Mehr A, Kwon HH (2021) Performance comparison of an LSTM-based deep learning model versus conventional machine learning algorithms for streamflow forecasting. Water Resour Manag 35:4167–4187. https://doi.org/10.1007/s11269-021-02937-w

    Article  Google Scholar 

  47. Liu M, Huang Y, Li Z, Tong B, Liu Z, Sun M et al (2020) The applicability of LSTM-KNN model for real-time flood forecasting in different climate zones in China. Water (Switzerland) 12:1–21. https://doi.org/10.3390/w12020440

    Article  Google Scholar 

  48. Pandey MK, Srivastava PK (2021) A probe into performance analysis of real-time forecasting of endemic ınfectious diseases using machine learning and deep learning algorithms, pp 241–65. https://doi.org/10.1007/978-981-16-0538-3_12

  49. Ser G, Bati CT (2019) Determining the best model with deep neural networks: Keras application on mushroom data. Yuz Yil Univ J Agric Sci 29:406–417. https://doi.org/10.29133/yyutbd.505086

    Article  Google Scholar 

  50. Vapnik V (1995) The nature of statistical learning theory. Springer, New York

    Book  MATH  Google Scholar 

  51. Smola A (1996) Regression estimation with support vector learning machines, Master’s thesis, Tech Univ Munchen. Master’s thesis, Tech Univ Munchen

  52. Smola AJ, Scholkopf B (1998) A tutorial on support vector regression. R Hollow Coll London, UK, NeuroCOLT Tech,Technical Rep Ser

  53. Smola AJ, Olkopf BSCH (2004) A tutorial on support vector regression. Kluwer Acad Publ Manuf Netherlands 14:199–222

    MathSciNet  Google Scholar 

  54. Eldakhly N, Aboul-Ela MM, Abdalla A (2018) A novel approach of weighted support vector machine with applied chance theory for forecasting air pollution phenomenon in Egypt. Int J Comput Intell Appl 17(1). https://doi.org/10.1142/S1469026818500013

  55. Khan MS, Coulibaly P (2006) Application of support vector machine in lake water level prediction. J Hydrol Eng 11:199–205. https://doi.org/10.1061/(asce)1084-0699(2006)11:3(199)

    Article  Google Scholar 

  56. Yin Z, Wen X, Feng Q, He Z, Zou S (2017) Integrating genetic algorithm and support vector machine for modeling daily reference evapotranspiration in a semi-arid mountain area. Hydrol Res. https://doi.org/10.2166/nh.2016.205

    Article  Google Scholar 

  57. Gunn S (1998) Support vector machines for classification and regression, Univ Southapt, Image Speech Intell Syst Res Group

  58. Rasmussen CE, Williams CKI (2006) Gaussian processes for machine learning. vol. 7. The MIT Press, Massachusetts Institute of Technology

  59. Kocijan J, Ažman K, Grancharova A (2007) The concept for Gaussian process model based system identification toolbox. In: Proceedings of the 2007 International Conference on Computer Computer Systems and Technologies—CompSysTech ’07, New York, New York, USA: ACM Press; p. 1. https://doi.org/10.1145/1330598.1330647

  60. Neal RM (1996) Bayesian learning for neural networks, vol 118. Springer, New York. https://doi.org/10.1007/978-1-4612-0745-0

    Book  MATH  Google Scholar 

  61. Wang J, Lu S, Wang S, Zhang Y (2021) A review on extreme learning machine Jian. Multimed Tools Appl. https://doi.org/10.1007/s11042-021-11007-71181

    Article  Google Scholar 

  62. Huang G, Member S, Zhou H, Ding X, Zhang R (2012) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern Part B (Cybern) 42:513–529

    Article  Google Scholar 

  63. Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: Theory and applications. Neurocomputing 70:489–501. https://doi.org/10.1016/j.neucom.2005.12.126

    Article  Google Scholar 

  64. Huang G, Chen L (2007) Convex incremental extreme learning machine. Neurocomputing 70:3056–3062. https://doi.org/10.1016/j.neucom.2007.02.009

    Article  Google Scholar 

  65. Yaseen ZM, Sulaiman SO, Deo RC, Chau KW (2019) An enhanced extreme learning machine model for river flow forecasting: State-of-the-art, practical applications in water resource engineering area and future research direction. J Hydrol 569:387–408. https://doi.org/10.1016/j.jhydrol.2018.11.069

    Article  Google Scholar 

  66. Fix E, Hodges JL (1951) Discriminatory analysis. Nonparametric discrimination: consistency properties. USAF School of Aviation Medicine, Randolph Field, Texas.. https://doi.org/10.2307/1403797

  67. Altman NS (1992) An Introduction to kernel and nearest-neighbor nonparametric regression. Am Stat 46:175–185. https://doi.org/10.1080/00031305.1992.10475879

    Article  MathSciNet  Google Scholar 

  68. Luo X, Li D, Yang Y, Zhang S (2019) Spatiotemporal traffic flow prediction with KNN and LSTM. J Adv Transp. https://doi.org/10.1155/2019/4145353

    Article  Google Scholar 

  69. Kramer O (2013) Dimensionality reduction with unsupervised nearest neighbors, vol 51. Springer, Berlin. https://doi.org/10.1007/978-3-642-38652-7

    Book  MATH  Google Scholar 

  70. Voyant C, Notton G, Kalogirou S, Nivet M-L, Paoli C, Motte F et al (2017) Machine learning methods for solar radiation forecasting: a review. Renew Energy 105:569–582. https://doi.org/10.1016/j.renene.2016.12.095

    Article  Google Scholar 

  71. Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part I—a discussion of principles. J Hydrol 10:282–290. https://doi.org/10.1016/0022-1694(70)90255-6

    Article  Google Scholar 

  72. Taylor KE (2001) Summarizing multiple aspects of model performance in a single diagram. J Geophys Res Atmos 106:7183–7192. https://doi.org/10.1029/2000JD900719

    Article  Google Scholar 

  73. Legouhy A (2021) al_goodplot—boxblot & violin plot. MATLAB Cent Mathworks. https://www.mathworks.com/matlabcentral/fileexchange/91790-al_goodplot-boxblot-violin-plot.

  74. Uncuoğlu E, Latifoğlu L, Özer AT (2021) Modelling of lateral effective stress using the particle swarm optimization with machine learning models. Arab J Geosci 14:2441. https://doi.org/10.1007/s12517-021-08686-9

    Article  Google Scholar 

  75. Moses (2022) Spider Plot. GitHub. https://github.com/NewGuy012/spider_plot/releases/tag/17.2. Accessed 4 Feb 2022

  76. Yaseen ZM, Jaafar O, Deo RC, Kisi O, Adamowski J, Quilty J et al (2016) Stream-flow forecasting using extreme learning machines: A case study in a semi-arid region in Iraq. J Hydrol 542:603–614. https://doi.org/10.1016/j.jhydrol.2016.09.035

    Article  Google Scholar 

  77. Başakın EE, Ekmekcioğlu Ö, Çıtakoğlu H, Özger M (2022) A new insight to the wind speed forecasting: robust multi-stage ensemble soft computing approach based on pre-processing uncertainty assessment. Neural Comput Appl 34:783–812. https://doi.org/10.1007/s00521-021-06424-6

    Article  Google Scholar 

  78. Uncuoglu E, Citakoglu H, Latifoglu L, Bayram S, Laman M, Ilkentapar M, Onera AA (2022) Comparison of neural network, Gaussian regression, support vector machine, long short-term memory, multi-gene genetic programming, and M5 Trees methods for solving civil engineering problems. Appl Soft Comput 129:109623

    Article  Google Scholar 

  79. Görkemli B, Citakoglu H, Haktanir T, Karaboga D (2022) A new method based on artificial bee colony programming for the regional standardized intensity–duration-frequency relationship. Arab J Geosci. https://doi.org/10.1007/s12517-021-09377-1

    Article  Google Scholar 

  80. Citakoglu H, Demir V (2022) Developing numerical equality to regional intensity duration–frequency curves using evolutionary algorithms and multi-gene genetic programming. Acta Geophys. https://doi.org/10.1007/s11600-022-00883-8

    Article  Google Scholar 

  81. Kisi O, Demir V, Kim S (2017) Estimation of long-term monthly temperatures by three different adaptive neuro-fuzzy approaches using geographical inputs. J Irrig Drain Eng. https://doi.org/10.1061/(ASCE)IR.1943-4774.0001242

    Article  Google Scholar 

Download references

Funding

No funding or support has been received for research from funding institutions.

Author information

Authors and Affiliations

  1. Civil Engineering Department, Faculty of Engineering and Natural Sciences, KTO Karatay University, Konya, Turkey

    Vahdettin Demir

  2. Civil Engineering Department, Faculty of Engineering, Erciyes University, Kayseri, Turkey

    Hatice Citakoglu

Authors
  1. Vahdettin Demir
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hatice Citakoglu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vahdettin Demir.

Ethics declarations

Conflicts of interest

The authors declare no conflicts of interest.

Ethics approval

The author paid attention to the ethical rules in the study. There is no violation of ethics.

Consent for publication: If this study is accepted, it can be published in the Neural Computing and Applications.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Demir, V., Citakoglu, H. Forecasting of solar radiation using different machine learning approaches. Neural Comput & Applic 35, 887–906 (2023). https://doi.org/10.1007/s00521-022-07841-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-022-07841-x

Keywords

Search

Navigation