Skip to main content

Advertisement

SpringerLink
Log in

A novel ensemble learning approach for hourly global solar radiation forecasting

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

Abstract

Precise solar radiation forecasting can provide great benefits and solutions for smart grid distribution and electricity management. However, its non-stationary behavior and randomness render its estimation very difficult. In this respect, a new hybrid learning approach is proposed for multi-hour global solar radiation forecasting, relying on Convolutional Neural Network (CNN), Nonparametric Gaussian Process Regression (GPR), Least Support Vector Machine (LS-SVM), and Extreme Learning Machine (ELM) as essence predictors. Then compressive sensing technique is applied to perform a hybridization scheme of the model’s output. Hourly global solar radiation data from two sites in Algeria with different climate conditions are used to evaluate the full potential of the integrated model, with stationarity checks with an advanced clear sky model (MecClear model). Different comparative simulations show the superiority of the proposed pipeline in forecasting hourly global solar radiation data for multi-hour ahead compared to the stand-alone model. Experimental results show that the proposed hybridization methodology can effectively improve the prediction accuracy and outperforms benchmarking models during all the forecasting horizons.

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

Similar content being viewed by others

Abbreviations

ANN:

Artificial neural network models

Bi-LSTM:

Bi-directional long short-term memory

CEEMDAN:

Complete ensemble empirical mode decomposition with adaptive noise

CNN:

Convolutional neural network

ELM:

Extreme learning machine

ESN:

Echo state network

ESSS:

Exponential smoothing state space

FNN:

Feedforward neural network

GA:

Genetic algorithm

GANs:

Generative Adversarial Networks

GH:

Extra-terrestrial solar radiation

GOA:

Grasshopper optimization algorithm

GPR:

Gaussian process regression

CSI:

Clear sky index

LS-SVM:

Least support vector machine

MABE:

Mean absolute bias error

MARS:

Multivariate adaptive regression spline

MMFF:

Multi-model forecasting framework

MOS:

Model output statistics

NMAE:

Normalized mean absolute error

nRMSE:

Normalized root mean square error

nRMSE:

Normalized root mean square error

OMP:

Orthogonal matching pursuit

PACF:

Partial autocorrelation factor

r:

Correlation coefficient

RF:

Random forest

RLMD:

Robust local mean decomposition

RMSE:

Root mean square error

SCA:

Sine cosine algorithm

St-OMP:

Stage-wise orthogonal matching pursuit

WPK:

Wavelet packet decomposition

WRF:

Numerical weather meso-scale model

\(\rho\) :

Lag value

\(\sigma_{s}\) :

Sparse solution

\(r_{s}\) :

Residual

References

  1. Ahmed R, Sreeram V, Mishra Y, Arif MD (2020) A review and evaluation of the state-of-the-art in PV solar power forecasting: techniques and optimization. Renew Sustain Energy Rev 124:109792. https://doi.org/10.1016/j.rser.2020.109792

    Article  Google Scholar 

  2. Bailek N, Bouchouicha K, Al-mostafa Z, El-shimy M (2018) A new empirical model for forecasting the diffuse solar radiation over Sahara in the Algerian Big South. Renew Energy 117:530–537. https://doi.org/10.1016/j.renene.2017.10.081

    Article  Google Scholar 

  3. Baraniuk RG (2007) Compressive sensing. IEEE Signal Process Mag. https://doi.org/10.1109/MSP.2007.4286571

    Article  MATH  Google Scholar 

  4. Benali L, Notton G, Fouilloy A, Voyant C, Dizene R (2019) Solar radiation forecasting using artificial neural network and random forest methods: application to normal beam, horizontal diffuse and global components. Renew Energy 132:871–884. https://doi.org/10.1016/j.renene.2018.08.044

    Article  Google Scholar 

  5. Benghanem M, Mellit A, Alamri SN (2009) ANN-based modelling and estimation of daily global solar radiation data: a case study. Energy Convers Manag 50:1644–1655

    Article  Google Scholar 

  6. Candes EJ, Wakin MB (2008) An introduction to compressive sampling: a sensing/sampling paradigm that goes against the common knowledge in data acquisition. IEEE Signal Process Mag 25:21–30. https://doi.org/10.1109/MSP.2007.914731

    Article  Google Scholar 

  7. Castangia M, Aliberti A, Bottaccioli L, Macii E, Patti E (2021) A compound of feature selection techniques to improve solar radiation forecasting. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2021.114979

    Article  Google Scholar 

  8. Deo RC, Şahin M, Adamowski JF, Mi J (2019) Universally deployable extreme learning machines integrated with remotely sensed MODIS satellite predictors over Australia to forecast global solar radiation: a new approach. Renew Sustain Energy Rev 104:235–261. https://doi.org/10.1016/j.rser.2019.01.009

    Article  Google Scholar 

  9. Diagne M, David M, Boland J, Schmutz N, Lauret P (2014) Post-processing of solar irradiance forecasts from WRF model at Reunion Island. Sol Energy 105:99–108. https://doi.org/10.1016/j.solener.2014.03.016

    Article  Google Scholar 

  10. Dong Z, Yang D, Reindl T, Walsh WM (2015) A novel hybrid approach based on self-organizing maps, support vector regression and particle swarm optimization to forecast solar irradiance. Energy 82:570–577. https://doi.org/10.1016/j.energy.2015.01.066

    Article  Google Scholar 

  11. Donoho DL (2006) Compressed sensing. IEEE Trans Inf Theory 52:1289–1306. https://doi.org/10.1109/TIT.2006.871582

    Article  MathSciNet  MATH  Google Scholar 

  12. Duchaud J-L, Voyant C, Fouilloy A, Notton G, Nivet M-L (2020) Trade-Off between precision and resolution of a solar power forecasting algorithm for micro-grid optimal control. Energies 13:3565. https://doi.org/10.3390/en13143565

    Article  Google Scholar 

  13. Fan J, Wu L, Ma X, Zhou H, Zhang F (2020) Hybrid support vector machines with heuristic algorithms for prediction of daily diffuse solar radiation in air-polluted regions. Renew Energy 145:2034–2045. https://doi.org/10.1016/j.renene.2019.07.104

    Article  Google Scholar 

  14. Gao B, Huang X, Shi J, Tai Y, Zhang J (2020) Hourly forecasting of solar irradiance based on CEEMDAN and multi-strategy CNN-LSTM neural networks. Renew Energy 162:1665–1683. https://doi.org/10.1016/j.renene.2020.09.141

    Article  Google Scholar 

  15. Gensler, A., Henze, J., Sick, B., Raabe, N., 2017. Deep Learning for solar power forecasting - An approach using AutoEncoder and LSTM Neural Networks, in: 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc., pp. 2858–2865. https://doi.org/10.1109/SMC.2016.7844673

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

    Article  Google Scholar 

  17. Guermoui M, Gairaa K, Boland J, Arrif T (2021) A novel hybrid model for solar radiation forecasting using support vector machine and bee colony optimization algorithm: review and case study. J Sol Energy Eng Trans ASME. https://doi.org/10.1115/1.4047852

    Article  Google Scholar 

  18. Guermoui M, Gairaa K, Rabehi A, Djafer D, Benkaciali S (2018) Estimation of the daily global solar radiation based on the Gaussian process regression methodology in the Saharan climate. Eur Phys J Plus 133:1–17. https://doi.org/10.1140/epjp/i2018-12029-7

    Article  Google Scholar 

  19. Guermoui M, Melaab D, Mekhalfi ML (2016) Sparse coding joint decision rule for ear print recognition. Opt Eng 55:093105. https://doi.org/10.1117/1.oe.55.9.093105

    Article  Google Scholar 

  20. Guermoui M, Melgani F, Danilo C (2018) Multi-step ahead forecasting of daily global and direct solar radiation: a review and case study of Ghardaia region. J Clean Prod 201:716–734. https://doi.org/10.1016/j.jclepro.2018.08.006

    Article  Google Scholar 

  21. 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 

  22. Guermoui M, Rabehi A, Gairaa K, Benkaciali S (2018) Support vector regression methodology for estimating global solar radiation in Algeria. Eur Phys J Plus 133:1–9. https://doi.org/10.1140/epjp/i2018-11845-y

    Article  Google Scholar 

  23. Hocaoglu FO, Serttas F (2017) A novel hybrid (Mycielski-Markov) model for hourly solar radiation forecasting. Renew Energy 108:635–643. https://doi.org/10.1016/j.renene.2016.08.058

    Article  Google Scholar 

  24. Huang J, Korolkiewicz M, Agrawal M, Boland J (2013) Forecasting solar radiation on an hourly time scale using a Coupled AutoRegressive and Dynamical System (CARDS) model. Sol Energy 87:136–149. https://doi.org/10.1016/j.solener.2012.10.012

    Article  Google Scholar 

  25. Huang X, Li Q, Tai Y, Chen Z, Zhang J, Shi J, Gao B, Liu W (2021) Hybrid deep neural model for hourly solar irradiance forecasting. Renew Energy. https://doi.org/10.1016/j.renene.2021.02.161

    Article  Google Scholar 

  26. Jovanovic, R., Pomares, L.M., Mohieldeen, Y.E., Perez-Astudillo, D., Bachour, D., 2017. An evolutionary method for creating ensembles with adaptive size neural networks for predicting hourly solar irradiance, in: Proceedings of the International Joint Conference on Neural Networks. Institute of Electrical and Electronics Engineers Inc., pp. 1962–1967. https://doi.org/10.1109/IJCNN.2017.7966091

  27. Lefèvre M, Oumbe A, Blanc P, Espinar B, Gschwind B, Qu Z, Wald L, Homscheidt MS, Hoyer-Klick C, Arola A, Lefèvre M, Oumbe A, Blanc P, Espinar B, Gschwind B, Qu Z, Wald L, Schroedter-Homscheidt M, Hoyer-Klick C, Arola A, Benedetti A, Kaiser JW, Morcrette J-J (2013) Atmospheric measurement techniques. Eur Geosci Union 6:2403–2418. https://doi.org/10.5194/amt-6-2403

    Article  Google Scholar 

  28. Malek S, Melgani F, Bazi Y (2018) One-dimensional convolutional neural networks for spectroscopic signal regression. J Chemom. https://doi.org/10.1002/cem.2977

    Article  Google Scholar 

  29. Mawloud G, Djamel M (2016) Weighted sparse representation for human ear recognition based on local descriptor. J Electron Imaging 25:013036. https://doi.org/10.1117/1.jei.25.1.013036

    Article  Google Scholar 

  30. Mellit A, Pavan AM (2010) A 24-h forecast of solar irradiance using artificial neural network: application for performance prediction of a grid-connected PV plant at Trieste Italy. Sol Energy 84:807–821. https://doi.org/10.1016/j.solener.2010.02.006

    Article  Google Scholar 

  31. Meng F, Zou Q, Zhang Z, Wang B, Ma H, Abdullah HM, Almalaq A, Mohamed MA (2021) An intelligent hybrid wavelet-adversarial deep model for accurate prediction of solar power generation. Energy Rep. https://doi.org/10.1016/j.egyr.2021.04.019

    Article  Google Scholar 

  32. Mirjalili SZ, Mirjalili S, Saremi S, Faris H, Aljarah I (2018) Grasshopper optimization algorithm for multi-objective optimization problems. Appl Intell 48:805–820. https://doi.org/10.1007/s10489-017-1019-8

    Article  Google Scholar 

  33. Ngoc-Lan Huynh A, Deo RC, Ali M, Abdulla S, Raj N (2021) Novel short-term solar radiation hybrid model: Long short-term memory network integrated with robust local mean decomposition. Appl Energy. https://doi.org/10.1016/j.apenergy.2021.117193

    Article  Google Scholar 

  34. Peel MC, Finlayson BL, McMahon TA (2007) Updated world map of the Köppen-Geiger climate classification. Hydrol Earth Syst Sci 11:1633–1644. https://doi.org/10.5194/hess-11-1633-2007

    Article  Google Scholar 

  35. Peng T, Zhang C, Zhou J, Nazir MS (2021) An integrated framework of Bi-directional long-short term memory (BiLSTM) based on sine cosine algorithm for hourly solar radiation forecasting. Energy 221:119887. https://doi.org/10.1016/j.energy.2021.119887

    Article  Google Scholar 

  36. Rabehi A, Guermoui M, Lalmi D (2020) Hybrid models for global solar radiation prediction: a case study. Int J Ambient Energy 41:31–40. https://doi.org/10.1080/01430750.2018.1443498

    Article  Google Scholar 

  37. Voyant C, Darras C, Muselli M, Paoli C, Nivet ML, Poggi P (2014) Bayesian rules and stochastic models for high accuracy prediction of solar radiation. Appl Energy 114:218–226. https://doi.org/10.1016/j.apenergy.2013.09.051

    Article  Google Scholar 

  38. Voyant C, Muselli M, Paoli C, Nivet ML (2013) Hybrid methodology for hourly global radiation forecasting in Mediterranean area. Renew Energy 53:1–11. https://doi.org/10.1016/j.renene.2012.10.049

    Article  Google Scholar 

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

    Article  Google Scholar 

  40. Wang H, Lei Z, Zhang X, Zhou B, Peng J (2019) A review of deep learning for renewable energy forecasting. Energy Convers Manag. https://doi.org/10.1016/j.enconman.2019.111799

    Article  Google Scholar 

  41. Yang D, Sharma V, Ye Z, Lim LI, Zhao L, Aryaputera AW (2015) Forecasting of global horizontal irradiance by exponential smoothing, using decompositions. Energy 81:111–119. https://doi.org/10.1016/j.energy.2014.11.082

    Article  Google Scholar 

Download references

Acknowledgements

We would like to acknowledge the German federal bureau for supplying instrumentations used in this work, as part of the enreMENA project.

Author information

Authors and Affiliations

  1. Unité de Recherche Appliquée en Energies Renouvelables, URAER, Centre de Développement des Energies Renouvelables, CDER, 47133, Ghardaïa, Algeria

    Mawloud Guermoui, Said Benkaciali & Kacem Gairaa

  2. Unité de Recherche en Energies Renouvelables en Milieu Saharien (URERMS), Centre de Développement des Energies Renouvelables (CDER), 01000, Adrar, Algeria

    Kada Bouchouicha

  3. Materials, Energy Systems Technology and Environment Laboratory, Ghardaia University, Ghardaia, Algeria

    Tayeb Boulmaiz

  4. Industrial AI Research Centre, UniSA STEM, University of South Australia, Mawson Lakes, 5095, Australia

    John W. Boland

Authors
  1. Mawloud Guermoui
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Said Benkaciali
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kacem Gairaa
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kada Bouchouicha
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Tayeb Boulmaiz
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. John W. Boland
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mawloud Guermoui.

Ethics declarations

Conflict of interest

No declaration of interest.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Guermoui, M., Benkaciali, S., Gairaa, K. et al. A novel ensemble learning approach for hourly global solar radiation forecasting. Neural Comput & Applic 34, 2983–3005 (2022). https://doi.org/10.1007/s00521-021-06421-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-021-06421-9

Keywords

Search

Navigation