Skip to main content
SpringerLink
Log in

A lightweight time series method for prediction of solar radiation

  • Original Paper
  • Published:
Energy Systems Aims and scope Submit manuscript

Abstract

Solar radiation (Rs) is vital and profoundly influences the environment. Accurate forecasting of Rs is crucial in renewable energy applications, despite its nonlinearity and dependency on loads. To overcome limitations in measurement tools, various methodologies are employed to estimate Rs using alternative environmental parameters. In our article, we present an innovative framework that explores the impact of feature selection (FS) on time series for accurate global Rs forecasting. This framework provides a holistic approach to recursive feature elimination (RFE) and its integration with various models such as random forest (RF), Decision Tree (DT), Logistic Regression (LR), Classification and Regression Tree (CART), Person (Per) and Gradient Boosting Models (GBM). The obtained results reveal that the CART, LR, and GBM models exhibit strong predictive accuracies of 0.894, 0.884, and 0.882, respectively. Notably, these three methods demonstrate a consistent standard deviation (std) of 0.033, indicating stability in their performance. Evaluating the normalized mean absolute error (nMAE) standard deviation (std), the models achieve values of 0.892 (0.029), 0.885 (0.034), and 0.885 (0.035) respectively. Additionally, the RFE algorithm showcases the significant impact of input lags as features and delivers good performance. Beyond accuracy, our findings hold practical implications for renewable energy planning, daily operation of solar power plants, and investment decision-making, contributing to the optimization and sustainability of solar energy systems.

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

Similar content being viewed by others

Data availability

Available on the request.

Abbreviations

AI:

Artificial intelligence, 4

ANFIS:

Adaptive neuro fuzzy inference system, 6

ANN:

Artificial neural network, 3

AR:

Autoregressive models, 7

ARIMA:

Auto regressive integrated moving average, 7

ARMA:

Hybrid autoregressive moving average, 5

BPNN:

Back propagation neural network, 4

CART:

Classification and Regression Tree, 1

DT:

Decision Tree, 1

EF:

Efficiency, 6

FS:

Feature selection, 1

GBM:

Gradient Boosting Model, 1

GBRT:

Gradient Boosting Regression Tree, 4

GEP:

Gene expression programming, 6

GHI:

Global horizontal irradiance, 5

GPR:

Gaussian process regression, 5

GRNN:

Generalized regression neural network, 6

k-NN:

K-nearest neighbors, 5

LightGBM:

Light gradient boosting, 5

LR:

Logistic regression, 1

LS:

Laplacian score, 5

LSTM:

Long short-term memory, 4

MABE:

Mean absolute bias error, 5

MAE:

Mean absolute error, 5

MAPE:

Mean absolute percentage error, 6

MBE:

Mean bias error, 5, 6

MCUVE:

Monte Carlo uninformative variable elimination, 5

ML:

Machine learning, 3

MLP:

MultiLayer perceptron, 3, 5

MLR:

Multiple linear regression, 5

MSE:

Mean squared error, 5

NCEP:

National Centers for Environmental Prediction, 10

nMAE:

Normalised mean absolute error, 1

nRMSE:

Negative RMSE, 5; normalized root mean square error, 6

NWP:

Numerical numerical weather prediction, 4

Per:

Person, 1

r:

Correlation coefficient, 5

R2 :

Coefficient of determination, 5

RBNN, 6:

Radial basis neural network

RBF:

Radial basis function, 6

RF:

Random Forest, 1, 5

RFE:

Recursive feature elimination, 1

RMSE:

Route mean square error, 5

rRMSE:

Relative RMSE, 5

Rs:

Solar radiation, 1

RT:

Regression Tree, 4

std:

Standard deviation, 1

SVM:

Support Vector Machine, 4

SVR:

Support Vector Regression, 4; Support-Vector Regression, 5

T-stat:

T statistic, 5

XGBoost:

EXtreme Gradient Boosting, 5

References

  1. Abdulrahim, M., Almaraashi, M.: Forecasting of short-term solar radiation based on a numerical weather prediction model over Saudi Arabia. Proceedings of the 6th International Conference on Informatics, Environment, Energy and Applications, pp. 16–19. https://doi.org/10.1145/3070617.3070624 (2017)

  2. Acikgoz, H.: A novel approach based on integration of convolutional neural networks and deep feature selection for short-term solar radiation forecasting. Appl. Energy 305, 117912 (2022). https://doi.org/10.1016/j.apenergy.2021.117912

    Article  Google Scholar 

  3. Ağbulut, Ü., Gürel, A.E., Biçen, Y.: Prediction of daily global solar radiation using different machine learning algorithms: Evaluation and comparison. Renew. Sustain. Energy Rev. 135, 110114 (2021). https://doi.org/10.1016/j.rser.2020.110114

    Article  Google Scholar 

  4. Aggarwal, S.K., Saini, L.M.: Solar energy prediction using linear and non-linear regularization models: a study on AMS (American Meteorological Society) 2013–14 solar energy prediction contest. Energy 78, 247–256 (2014). https://doi.org/10.1016/j.energy.2014.10.012

    Article  Google Scholar 

  5. Al Shalabi, L., Shaaban, Z.: Normalization as a preprocessing engine for data mining and the approach of preference matrix. In: 2006 International Conference on Dependability of Computer Systems, pp. 207–214. https://doi.org/10.1109/DEPCOS-RELCOMEX.2006.38 (2006)

  6. Almaraashi, M.: Investigating the impact of feature selection on the prediction of solar radiation in different locations in Saudi Arabia. Appl. Soft Comput. 66, 250–263 (2018). https://doi.org/10.1016/j.asoc.2018.02.029

    Article  Google Scholar 

  7. Alobaidi, M.H., Marpu, P.R., Ouarda, T.B.M.J., Ghedira, H.: Mapping of the solar irradiance in the UAE using advanced artificial neural network ensemble. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 7(8), 3668–3680 (2014). https://doi.org/10.1109/JSTARS.2014.2331255

    Article  ADS  Google Scholar 

  8. Alsina, E.F., Bortolini, M., Gamberi, M., Regattieri, A.: Artificial neural network optimisation for monthly average daily global solar radiation prediction. Energy Convers. Manag. 120, 320–329 (2016). https://doi.org/10.1016/j.enconman.2016.04.101

    Article  Google Scholar 

  9. Amiri, B., Dizène, R., Dahmani, K.: Most relevant input parameters selection for 10-min global solar irradiation estimation on arbitrary inclined plane using neural networks. Int. J. Sustain. Energy 39(8), 779–803 (2020). https://doi.org/10.1080/14786451.2020.1758104

    Article  Google Scholar 

  10. Aybar-Ruiz, A., Jiménez-Fernández, S., Cornejo-Bueno, L., Casanova-Mateo, C., Sanz-Justo, J., Salvador-González, P., Salcedo-Sanz, S.: A novel grouping genetic algorithm-extreme learning machine approach for global solar radiation prediction from numerical weather models inputs. Sol. Energy 132, 129–142 (2016). https://doi.org/10.1016/j.solener.2016.03.015

    Article  ADS  Google Scholar 

  11. Bhardwaj, S., Sharma, V., Srivastava, S., Sastry, O.S., Bandyopadhyay, B., Chandel, S.S., Gupta, J.R.P.: Estimation of solar radiation using a combination of hidden Markov model and generalized fuzzy model. Sol. Energy 93, 43–54 (2013). https://doi.org/10.1016/j.solener.2013.03.020

    Article  ADS  Google Scholar 

  12. Biazar, S.M., Rahmani, V., Isazadeh, M., Kisi, O., Dinpashoh, Y.: New input selection procedure for machine learning methods in estimating daily global solar radiation. Arab. J. Geosci. 13(12), 431 (2020). https://doi.org/10.1007/s12517-020-05437-0

    Article  CAS  Google Scholar 

  13. Bounoua, Z., OuazzaniChahidi, L., Mechaqrane, A.: Estimation of daily global solar radiation using empirical and machine-learning methods: a case study of five Moroccan locations. Sustain. Mater. Technol. 28, e00261 (2021). https://doi.org/10.1016/j.susmat.2021.e00261

    Article  Google Scholar 

  14. Bouzgou, H., Gueymard, C.A.: Minimum redundancy–maximum relevance with extreme learning machines for global solar radiation forecasting: toward an optimized dimensionality reduction for solar time series. Sol. Energy 158, 595–609 (2017). https://doi.org/10.1016/j.solener.2017.10.035

    Article  ADS  Google Scholar 

  15. Bristow, K.L., Campbell, G.S.: On the relationship between incoming solar radiation and daily maximum and minimum temperature. Agric. For. Meteorol. 31(2), 159–166 (1984). https://doi.org/10.1016/0168-1923(84)90017-0

    Article  ADS  Google Scholar 

  16. Budyko, M.I.: The effect of solar radiation variations on the climate of the Earth. Tellus 21(5), 611–619 (1969). https://doi.org/10.1111/j.2153-3490.1969.tb00466.x

    Article  ADS  Google Scholar 

  17. Cfsr. Retrieved June 14, 2022, from https://swat.tamu.edu/data/cfsr (n.d.)

  18. Chaibi, M., Benghoulam, E.M., Tarik, L., Berrada, M., Hmaidi, A.E.: An interpretable machine learning model for daily global solar radiation prediction. Energies 14(21), 7367 (2021). https://doi.org/10.3390/en14217367

    Article  Google Scholar 

  19. Chandola, D., Gupta, H., Tikkiwal, V.A., Bohra, M.K.: Multi-step ahead forecasting of global solar radiation for arid zones using deep learning. Procedia Comput. Sci. 167, 626–635 (2020). https://doi.org/10.1016/j.procs.2020.03.329

    Article  Google Scholar 

  20. Chen, J.-L., Liu, H.-B., Wu, W., Xie, D.-T.: Estimation of monthly solar radiation from measured temperatures using support vector machines—a case study. Renew. Energy 36(1), 413–420 (2011). https://doi.org/10.1016/j.renene.2010.06.024

    Article  Google Scholar 

  21. Dahmani, K., Notton, G., Voyant, C., Dizene, R., Nivet, M.L., Paoli, C., Tamas, W.: Multilayer perceptron approach for estimating 5-min and hourly horizontal global irradiation from exogenous meteorological data in locations without solar measurements. Renew. Energy 90, 267–282 (2016). https://doi.org/10.1016/j.renene.2016.01.013

    Article  Google Scholar 

  22. de Araujo, J.M.S.: Performance comparison of solar radiation forecasting between WRF and LSTM in Gifu, Japan. Environ. Res. Commun. 2(4), 045002 (2020). https://doi.org/10.1088/2515-7620/ab7366

    Article  Google Scholar 

  23. De Felice, M., Petitta, M., Ruti, P.M.: Short-term predictability of photovoltaic production over Italy. Renew. Energy 80, 197–204 (2015). https://doi.org/10.1016/j.renene.2015.02.010

    Article  Google Scholar 

  24. de Freitas Viscondi, G., Alves-Souza, S.N.: Solar irradiance prediction with machine learning algorithms: a Brazilian case study on photovoltaic electricity generation. Energies 14(18), 5657 (2021). https://doi.org/10.3390/en14185657

    Article  Google Scholar 

  25. Demirhan, H.: The problem of multicollinearity in horizontal solar radiation estimation models and a new model for Turkey. Energy Convers. Manag. 84, 334–345 (2014). https://doi.org/10.1016/j.enconman.2014.04.035

    Article  Google Scholar 

  26. Fan, J., Wang, X., Wu, L., Zhou, H., Zhang, F., Yu, X., Lu, X., Xiang, Y.: Comparison of Support Vector Machine and Extreme Gradient Boosting for predicting daily global solar radiation using temperature and precipitation in humid subtropical climates: a case study in China. Energy Convers. Manag. 164, 102–111 (2018). https://doi.org/10.1016/j.enconman.2018.02.087

    Article  Google Scholar 

  27. Ghimire, S., Deo, R.C., Casillas-Pérez, D., Salcedo-Sanz, S.: Boosting solar radiation predictions with global climate models, observational predictors and hybrid deep-machine learning algorithms. Appl. Energy 316, 119063 (2022). https://doi.org/10.1016/j.apenergy.2022.119063

    Article  Google Scholar 

  28. Ghimire, S., Deo, R.C., Raj, N., Mi, J.: Deep solar radiation forecasting with convolutional neural network and long short-term memory network algorithms. Appl. Energy 253, 113541 (2019). https://doi.org/10.1016/j.apenergy.2019.113541

    Article  Google Scholar 

  29. Goliatt, L., Yaseen, Z.M.: Development of a hybrid computational intelligent model for daily global solar radiation prediction. Expert Syst. Appl. 212, 118295 (2023). https://doi.org/10.1016/j.eswa.2022.118295

    Article  Google Scholar 

  30. Guermoui, M., Gairaa, K., Rabehi, A., Djafer, D., Benkaciali, S.: Estimation of the daily global solar radiation based on the Gaussian process regression methodology in the Saharan climate. Eur. Phys. J. Plus 133(6), 211 (2018). https://doi.org/10.1140/epjp/i2018-12029-7

    Article  Google Scholar 

  31. Gueymard, C.A.: Direct solar transmittance and irradiance predictions with broadband models. Part I: detailed theoretical performance assessment. Sol. Energy 74(5), 355–379 (2003). https://doi.org/10.1016/S0038-092X(03)00195-6

    Article  ADS  Google Scholar 

  32. Guezzaz, A., Asimi, A., Asimi, Y., Azrour, M., Benkirane, S.: A distributed intrusion detection approach based on machine leaning techniques for a cloud security. In: Gherabi, N., Kacprzyk, J. (eds.) Intelligent Systems in Big Data, Semantic Web and Machine Learning, vol. 1344, pp. 85–94. Springer International Publishing, Berlin (2021). https://doi.org/10.1007/978-3-030-72588-4_6

    Chapter  Google Scholar 

  33. Guezzaz, A., Asimi, A., Mourade, A., Tbatou, Z., Asimi, Y.: A multilayer perceptron classifier for monitoring network traffic. In: Farhaoui, Y. (ed.) Big Data and Networks Technologies, vol. 81, pp. 262–270. Springer International Publishing, Berlin (2020). https://doi.org/10.1007/978-3-030-23672-4_19

    Chapter  Google Scholar 

  34. Guezzaz, A., Azrour, M., Benkirane, S., Mohy-Eddine, M., Attou, H., Douiba, M.: A lightweight hybrid intrusion detection framework using machine learning for edge-based IIoT security. Int Arab J. Inf. Technol. (2022). https://doi.org/10.34028/iajit/19/5/14

    Article  Google Scholar 

  35. Guezzaz, A., Benkirane, S., Azrour, M., Khurram, S.: A reliable network intrusion detection approach using Decision Tree with enhanced data quality. Secur. Commun. Netw. 2021, 1–8 (2021). https://doi.org/10.1155/2021/1230593

    Article  Google Scholar 

  36. Halabi, L.M., Mekhilef, S., Hossain, M.: Performance evaluation of hybrid adaptive neuro-fuzzy inference system models for predicting monthly global solar radiation. Appl. Energy 213, 247–261 (2018). https://doi.org/10.1016/j.apenergy.2018.01.035

    Article  ADS  Google Scholar 

  37. Hassan, M.A., Khalil, A., Kaseb, S., Kassem, M.A.: Potential of four different machine-learning algorithms in modeling daily global solar radiation. Renew. Energy 111, 52–62 (2017). https://doi.org/10.1016/j.renene.2017.03.083

    Article  Google Scholar 

  38. He, C., Liu, J., Xu, F., Zhang, T., Chen, S., Sun, Z., Zheng, W., Wang, R., He, L., Feng, H., Yu, Q., He, J.: Improving solar radiation estimation in China based on regional optimal combination of meteorological factors with machine learning methods. Energy Convers. Manag. 220, 113111 (2020). https://doi.org/10.1016/j.enconman.2020.113111

    Article  Google Scholar 

  39. Hedar, A.-R., Almaraashi, M., Abdel-Hakim, A.E., Abdulrahim, M.: Hybrid machine learning for solar radiation prediction in reduced feature spaces. Energies 14(23), 7970 (2021). https://doi.org/10.3390/en14237970

    Article  CAS  Google Scholar 

  40. Huang, J., Troccoli, A., Coppin, P.: An analytical comparison of four approaches to modelling the daily variability of solar irradiance using meteorological records. Renew. Energy 72, 195–202 (2014). https://doi.org/10.1016/j.renene.2014.07.015

    Article  Google Scholar 

  41. Huang, L., Kang, J., Wan, M., Fang, L., Zhang, C., Zeng, Z.: Solar radiation prediction using different machine learning algorithms and implications for extreme climate events. Front. Earth Sci. 9, 596860 (2021). https://doi.org/10.3389/feart.2021.596860

    Article  Google Scholar 

  42. Ibrahim, I.A., Khatib, T.: A novel hybrid model for hourly global solar radiation prediction using random forests technique and firefly algorithm. Energy Convers. Manag. 138, 413–425 (2017). https://doi.org/10.1016/j.enconman.2017.02.006

    Article  Google Scholar 

  43. Jadidi, A., Menezes, R., de Souza, N., de Castro Lima, A.: A hybrid GA–MLPNN model for one-hour-ahead forecasting of the global horizontal irradiance in Elizabeth City, North Carolina. Energies 11(10), 2641 (2018). https://doi.org/10.3390/en11102641

    Article  Google Scholar 

  44. Jiang, H., Dong, Y.: A nonlinear support vector machine model with hard penalty function based on glowworm swarm optimization for forecasting daily global solar radiation. Energy Convers. Manag. 126, 991–1002 (2016). https://doi.org/10.1016/j.enconman.2016.08.069

    Article  Google Scholar 

  45. Jiang, H., Dong, Y., Wang, J., Li, Y.: Intelligent optimization models based on hard-ridge penalty and RBF for forecasting global solar radiation. Energy Convers. Manag. 95, 42–58 (2015). https://doi.org/10.1016/j.enconman.2015.02.020

    Article  Google Scholar 

  46. Jung, Y.: Multiple predicting K-fold cross-validation for model selection. J. Nonparametr. Stat. 30(1), 197–215 (2018). https://doi.org/10.1080/10485252.2017.1404598

    Article  MathSciNet  Google Scholar 

  47. Krishnan, N., Kumar, K.R., Inda, C.S.: How solar radiation forecasting impacts the utilization of solar energy: a critical review. J. Clean. Prod. 388, 135860 (2023). https://doi.org/10.1016/j.jclepro.2023.135860

    Article  Google Scholar 

  48. Kuhn, M., Johnson, K.: Applied predictive modeling. Springer, Berlin (2013)

    Book  Google Scholar 

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

    Article  Google Scholar 

  50. Kumar, S., Kaur, T.: Efficient solar radiation estimation using cohesive artificial neural network technique with optimal synaptic weights. Proc. Inst. Mech. Eng. Part A J. Power Energy 234(6), 862–873 (2020). https://doi.org/10.1177/0957650919878318

    Article  Google Scholar 

  51. Lazzaroni, M., Ferrari, S., Piuri, V., Salman, A., Cristaldi, L., Faifer, M.: Models for solar radiation prediction based on different measurement sites. Measurement 63, 346–363 (2015). https://doi.org/10.1016/j.measurement.2014.11.037

    Article  Google Scholar 

  52. Li, M.-F., Fan, L., Liu, H.-B., Wu, W., Chen, J.-L.: Impact of time interval on the Ångström–Prescott coefficients and their interchangeability in estimating radiation. Renew. Energy 44, 431–438 (2012). https://doi.org/10.1016/j.renene.2012.01.107

    Article  CAS  Google Scholar 

  53. Linares-Rodríguez, A., Ruiz-Arias, J.A., Pozo-Vázquez, D., Tovar-Pescador, J.: Generation of synthetic daily global solar radiation data based on ERA-Interim reanalysis and artificial neural networks. Energy 36(8), 5356–5365 (2011). https://doi.org/10.1016/j.energy.2011.06.044

    Article  Google Scholar 

  54. Long, H., Zhang, Z., Su, Y.: Analysis of daily solar power prediction with data-driven approaches. Appl. Energy 126, 29–37 (2014). https://doi.org/10.1016/j.apenergy.2014.03.084

    Article  ADS  Google Scholar 

  55. Lu, N., Qin, J., Yang, K., Sun, J.: A simple and efficient algorithm to estimate daily global solar radiation from geostationary satellite data. Energy 36(5), 3179–3188 (2011). https://doi.org/10.1016/j.energy.2011.03.007

    Article  Google Scholar 

  56. Marzo, A., Trigo-Gonzalez, M., Alonso-Montesinos, J., Martínez-Durbán, M., López, G., Ferrada, P., Fuentealba, E., Cortés, M., Batlles, F.J.: Daily global solar radiation estimation in desert areas using daily extreme temperatures and extraterrestrial radiation. Renew. Energy 113, 303–311 (2017). https://doi.org/10.1016/j.renene.2017.01.061

    Article  Google Scholar 

  57. Marzouq, M., Bounoua, Z., El Fadili, H., Mechaqrane, A., Zenkouar, K., Lakhliai, Z.: New daily global solar irradiation estimation model based on automatic selection of input parameters using evolutionary artificial neural networks. J. Clean. Prod. 209, 1105–1118 (2019). https://doi.org/10.1016/j.jclepro.2018.10.254

    Article  Google Scholar 

  58. Meenal, R., Michael, P.A., Pamela, D., Rajasekaran, E.: Weather prediction using random forest machine learning model. Indones. J. Electr. Eng. Comput Sci. 22(2), 1208 (2021). https://doi.org/10.11591/ijeecs.v22.i2.pp1208-1215

    Article  Google Scholar 

  59. Meenal, R., Selvakumar, A.I.: Assessment of SVM, empirical and ANN based solar radiation prediction models with most influencing input parameters. Renew. Energy 121, 324–343 (2018). https://doi.org/10.1016/j.renene.2017.12.005

    Article  Google Scholar 

  60. Mehdizadeh, S., Behmanesh, J., Khalili, K.: Comparison of artificial intelligence methods and empirical equations to estimate daily solar radiation. J. Atmos. Sol.-Terr. Phys. 146, 215–227 (2016). https://doi.org/10.1016/j.jastp.2016.06.006

    Article  ADS  Google Scholar 

  61. Mellit, A.: Artificial intelligence technique for modelling and forecasting of solar radiation data: a review. Int. J. Artif. Intell. Soft Comput. 1(1), 52 (2008). https://doi.org/10.1504/IJAISC.2008.021264

    Article  Google Scholar 

  62. Mohammadi, K., Shamshirband, S., Kamsin, A., Lai, P.C., Mansor, Z.: Identifying the most significant input parameters for predicting global solar radiation using an ANFIS selection procedure. Renew. Sustain. Energy Rev. 63, 423–434 (2016). https://doi.org/10.1016/j.rser.2016.05.065

    Article  Google Scholar 

  63. Olatomiwa, L., Mekhilef, S., Shamshirband, S., Mohammadi, K., Petković, D., Sudheer, C.: A support vector machine–firefly algorithm-based model for global solar radiation prediction. Sol. Energy 115, 632–644 (2015). https://doi.org/10.1016/j.solener.2015.03.015

    Article  ADS  Google Scholar 

  64. Pang, Z., Niu, F., O’Neill, Z.: Solar radiation prediction using recurrent neural network and artificial neural network: a case study with comparisons. Renew. Energy 156, 279–289 (2020). https://doi.org/10.1016/j.renene.2020.04.042

    Article  Google Scholar 

  65. Pedro, H.T.C., Coimbra, C.F.M.: Nearest-neighbor methodology for prediction of intra-hour global horizontal and direct normal irradiances. Renew. Energy 80, 770–782 (2015). https://doi.org/10.1016/j.renene.2015.02.061

    Article  Google Scholar 

  66. Persson, C., Bacher, P., Shiga, T., Madsen, H.: Multi-site solar power forecasting using gradient boosted regression trees. Sol. Energy 150, 423–436 (2017). https://doi.org/10.1016/j.solener.2017.04.066

    Article  ADS  Google Scholar 

  67. Piri, J., Shamshirband, S., Petković, D., Tong, C.W., ur Rehman, M.H.: Prediction of the solar radiation on the Earth using support vector regression technique. Infrared Phys. Technol. 68, 179–185 (2015). https://doi.org/10.1016/j.infrared.2014.12.006

    Article  ADS  Google Scholar 

  68. Quej, V.H., Almorox, J., Arnaldo, J.A., Saito, L.: ANFIS, SVM and ANN soft-computing techniques to estimate daily global solar radiation in a warm sub-humid environment. J. Atmos. Sol.-Terrest. Phys. 155, 62–70 (2017). https://doi.org/10.1016/j.jastp.2017.02.002

    Article  ADS  Google Scholar 

  69. Reza Parsaei, M., Mollashahi, H., Darvishan, A., Mir, M., Simoes, R.: A new prediction model of solar radiation based on the neuro-fuzzy model. Int. J. Ambient Energy 41(2), 189–197 (2020). https://doi.org/10.1080/01430750.2018.1456964

    Article  Google Scholar 

  70. Rohani, A., Taki, M., Abdollahpour, M.: A novel soft computing model (Gaussian process regression with K-fold cross validation) for daily and monthly solar radiation forecasting (Part: I). Renew. Energy 115, 411–422 (2018). https://doi.org/10.1016/j.renene.2017.08.061

    Article  Google Scholar 

  71. Salcedo-Sanz, S., Deo, R.C., Cornejo-Bueno, L., Camacho-Gómez, C., Ghimire, S.: An efficient neuro-evolutionary hybrid modelling mechanism for the estimation of daily global solar radiation in the Sunshine State of Australia. Appl. Energy 209, 79–94 (2018). https://doi.org/10.1016/j.apenergy.2017.10.076

    Article  ADS  Google Scholar 

  72. Schneider, P., Xhafa, F.: Anomaly detection. In: Anomaly Detection and Complex Event Processing over IoT Data Streams, pp. 49–66. Elsevier, Amsterdam (2022). https://doi.org/10.1016/B978-0-12-823818-9.00013-4

    Chapter  Google Scholar 

  73. Sun, H., Gui, D., Yan, B., Liu, Y., Liao, W., Zhu, Y., Lu, C., Zhao, N.: Assessing the potential of random forest method for estimating solar radiation using air pollution index. Energy Convers. Manag. 119, 121–129 (2016). https://doi.org/10.1016/j.enconman.2016.04.051

    Article  Google Scholar 

  74. Voyant, C., Muselli, M., Paoli, C., Nivet, M.-L.: Numerical weather prediction (NWP) and hybrid ARMA/ANN model to predict global radiation. Energy 39(1), 341–355 (2012). https://doi.org/10.1016/j.energy.2012.01.006

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  77. Wu, Y.-K., Chen, C.-R., Abdul Rahman, H.: A novel hybrid model for short-term forecasting in PV power generation. Int. J. Photoenergy 2014, 1–9 (2014). https://doi.org/10.1155/2014/569249

    Article  Google Scholar 

  78. Xu, H., Wang, M., Wang, B.: A difference standardization method for mutual transfer learning. In: Proceedings of the 39th International Conference on Machine Learning, 24683–24697. https://proceedings.mlr.press/v162/xu22j.html (2022)

  79. Xue, X.: Prediction of daily diffuse solar radiation using artificial neural networks. Int. J. Hydrog. Energy 42(47), 28214–28221 (2017). https://doi.org/10.1016/j.ijhydene.2017.09.150

    Article  CAS  Google Scholar 

  80. Yakubu, U.A., Saputra, M.P.A.: Time series model analysis using autocorrelation function (ACF) and partial autocorrelation function (PACF) for E-wallet transactions during a pandemic. Int. J. Glob. Oper. Res. 3(3), 3 (2022). https://doi.org/10.47194/ijgor.v3i3.168

    Article  Google Scholar 

  81. Yang, H.-T., Huang, C.-M., Huang, Y.-C., Pai, Y.-S.: A weather-based hybrid method for 1-day ahead hourly forecasting of PV power output. IEEE Trans. Sustain. Energy 5(3), 917–926 (2014). https://doi.org/10.1109/TSTE.2014.2313600

    Article  ADS  Google Scholar 

  82. Yang, K., Huang, G.W., Tamai, N.: A hybrid model for estimating global solar radiation. Sol. Energy 70(1), 13–22 (2001). https://doi.org/10.1016/S0038-092X(00)00121-3

    Article  ADS  Google Scholar 

  83. Yıldırım, H.B., Çelik, Ö., Teke, A., Barutçu, B.: Estimating daily Global solar radiation with graphical user interface in Eastern Mediterranean region of Turkey. Renew. Sustain. Energy Rev. 82, 1528–1537 (2018). https://doi.org/10.1016/j.rser.2017.06.030

    Article  Google Scholar 

  84. Zamo, M., Mestre, O., Arbogast, P., Pannekoucke, O.: A benchmark of statistical regression methods for short-term forecasting of photovoltaic electricity production, part I: deterministic forecast of hourly production. Sol. Energy 105, 792–803 (2014). https://doi.org/10.1016/j.solener.2013.12.006

    Article  ADS  Google Scholar 

  85. Zang, H., Liu, L., Sun, L., Cheng, L., Wei, Z., Sun, G.: Short-term global horizontal irradiance forecasting based on a hybrid CNN-LSTM model with spatiotemporal correlations. Renew. Energy 160, 26–41 (2020). https://doi.org/10.1016/j.renene.2020.05.150

    Article  Google Scholar 

  86. Zeng, Z., Wang, Z., Gui, K., Yan, X., Gao, M., Luo, M., Geng, H., Liao, T., Li, X., An, J., Liu, H., He, C., Ning, G., Yang, Y.: Daily global solar radiation in China estimated from high-density meteorological observations: a Random Forest model framework. Earth Space Sci. (2020). https://doi.org/10.1029/2019EA001058

    Article  Google Scholar 

  87. Zhou, Y., Liu, Y., Wang, D., Liu, X., Wang, Y.: A review on global solar radiation prediction with machine learning models in a comprehensive perspective. Energy Convers. Manag. 235, 113960 (2021). https://doi.org/10.1016/j.enconman.2021.113960

    Article  Google Scholar 

Download references

Funding

Our work has not been funded and without financially supporting. We did this research work as professors of computer science at university.

Author information

Authors and Affiliations

  1. Faculty of Science, Science and Technology Research Structure, Chouaïb Doukkali University, El Jadida, Morocco

    Hasna Hissou & Abderrahim Beni-Hssane

  2. Technology Higher School Essaouira, Cadi Ayyad University, Marrakesh, Morocco

    Said Benkirane & Azidine Guezzaz

  3. IDMS Team, Faculty of Sciences and Techniques, Moulay Ismail University of Meknès, Meknes, Morocco

    Mourade Azrour

Authors
  1. Hasna Hissou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Said Benkirane
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Azidine Guezzaz
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mourade Azrour
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Abderrahim Beni-Hssane
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Hasna Hissou is the main author that manages the contribution and gives the detailed description of the research work. Said Benkirane writes the abstract, introduction and analyzes the related works section. Azidine Guezzaz evaluate the results obtained from implementation and drawing the figures. Abderrahim Beni-Hssane and Mourade Azrour participate in implementation of the model, prepare the final manuscript and corrects the English language.

Corresponding author

Correspondence to Azidine Guezzaz.

Ethics declarations

Conflict of interest

We declare that we have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.

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 (e.g. a society or other partner) 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

Hissou, H., Benkirane, S., Guezzaz, A. et al. A lightweight time series method for prediction of solar radiation. Energy Syst (2024). https://doi.org/10.1007/s12667-024-00657-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12667-024-00657-9

Keywords

Search

Navigation