Skip to main content
SpringerLink
Log in

A comprehensive review and analysis of solar forecasting techniques

  • Review Article
  • Published:
Frontiers in Energy Aims and scope Submit manuscript

Abstract

In the last two decades, renewable energy has been paid immeasurable attention to toward the attainment of electricity requirements for domestic, industrial, and agriculture sectors. Solar forecasting plays a vital role in smooth operation, scheduling, and balancing of electricity production by standalone PV plants as well as grid interconnected solar PV plants. Numerous models and techniques have been developed in short, mid and long-term solar forecasting. This paper analyzes some of the potential solar forecasting models based on various methodologies discussed in literature, by mainly focusing on investigating the influence of meteorological variables, time horizon, climatic zone, pre-processing techniques, air pollution, and sample size on the complexity and accuracy of the model. To make the paper reader-friendly, it presents all-important parameters and findings of the models revealed from different studies in a tabular mode having the year of publication, time resolution, input parameters, forecasted parameters, error metrics, and performance. The literature studied showed that ANN-based models outperform the others due to their nonlinear complex problemsolving capabilities. Their accuracy can be further improved by hybridization of the two models or by performing pre-processing on the input data. Besides, it also discusses the diverse key constituents that affect the accuracy of a model. It has been observed that the proper selection of training and testing period along with the correlated dependent variables also enhances the accuracy of the model.

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

Similar content being viewed by others

Abbreviations

ACF:

Autocorrelation function

ACO:

Ant colony optimization

AIC:

Akaike information criteria

ALHM:

Adaptive learning hybrid model

ALSTM:

Attention mechanism with multiple LSTM

ANFIS:

Adaptive neuro-fuzzy inference system

ANN:

Artificial neural network

APE:

Absolute percentage error

AR:

Auto regression

ARIMA:

Auto regressive integrated moving average

ARIMAX:

Auto-regressive integrated moving average model with exogenous variable

ARMA:

Auto regression and movie average

AVM:

Atmospheric motion vectors

BIC:

Bayesian information criteria

BR:

Bayesian regularization

BRT:

Boosted regression trees

BNI:

Beam normal irradiance

CART:

Classification and regression trees

CDER:

Renewable energies development centre

CDSWR:

Clear sky down welling short wave radiation

CGP:

Pola-Ribiere conjugate gradient

CNFRRM:

Cooperative network for renewable resources measurement

CNN:

Convolution neural network

CNQR:

Copula-based nonlinear quantile regression

CRPSS:

Continuous ranked probability skill score

CSRIO:

Commonwealth scientific and industrial research organization

DL:

Deep learning

DFT:

Discrete Fourier transform

DGSR:

Daily global solar radiation

DHI:

Direct horizontal irradiance

DHR:

Dynamic harmonic regression

DNI:

Direct normal irradiance

DNN:

Deep neural network

DSI:

Diffuse solar irradiance

DSR:

Daily solar radiation

DT:

Decision trees

ECMWF:

European centre for medium-range weather forecasts

EEMD:

Ensemble empirical mode decomposition

ELM:

Extreme learning machine

ELNN:

Elman neural network

EMD:

Empirical mode decomposition

ESR:

Extraterrestrial solar radiation

FF:

Firefly algorithm

FFBP:

Feed forward back propagation

FOA:

Fruit fly optimization algorithm

FS:

Forecast skill

GA:

Genetic algorithm

GABP:

Genetic algorithm back propagation neural network

GBDT:

Gradient boosting decision trees

GDX:

Gradient descent with adaptive learning rates and momentum

GFS:

Global forecast system

GHI:

Global horizontal irradiance

GMDHNN:

Group method of data handling neural network

GMDH:

Group method of data handling

GPI:

Global performance indicator

GRU:

Gate recurrent unit

GSI:

Global solar irradiance

GSR:

Global solar radiation

HGWO:

Differential evolution grey wolf optimize

HIS:

Hybrid intelligent system

HMM:

Hidden Markov model

ICP:

Interval coverage probability

IEA:

International energy agency

IMD:

Indian Meteorological Department

K-NN:

K-nearest neural network

KSI:

Kolonogorov-Smirnov integral

LLF:

Log-likelihood function

LASSO:

Least absolute shrinkage and selection operator

LR:

Linear regression

LM:

Levenberg-Marquardt

LMBP:

Levenberg Marquardt back propagation

LSTM:

Long short-term memory

LS-SVM:

Least square support vector machine

MABE:

Mean absolute biased error

MAD:

Mean absolute deviation

MAE:

Mean absolute error

MAID:

Mean absolute interval deviation

MAPE:

Mean absolute percentage error

MBD:

Mean bias deviation

MBE:

Mean bias error

MARS:

Multivariate adaptive regression splines

MFOA:

Modified fruit fly optimization

ML:

Machine learning

MLFFN:

Multilayer feed-forward neural network

MLP:

Multi-layer perceptron

MLR:

Multi linear regression

MNRE:

Ministry of New and Renewable Energy

MOS:

Model output statistics

MRE:

Mean relative error

MTM:

Markov transition method

NAR:

Nonlinear autoregressive

NCEP:

National Centers for Environmental Prediction

NCMRWF:

National Center for Medium Range Weather Forecasting

nE:

Normalized error

nMAE:

Normalized mean absolute error

NMSC:

National Meteorological Satellite Center

NNE:

Neural network ensemble

NNFOA:

Neural network modified fruit fly optimization

nRMSE:

Normalized root mean square error

NSE:

Nash-Sutcliffe efficiency

NWP:

Numerical weather prediction

OCCUR:

Optimized cross-validated clustering

PACF:

Partial autocorrelation function

PCA:

Principal component analysis

PEV:

Potential economic value

PINAW:

Prediction interval normalized average width

PSO:

Particle swarm optimization

PV:

Photo voltaic

RB:

Batch training with bias and weight learning rules

RBF:

Radial basis function

RDI:

Ramp detection index

RF:

Random forest

RM:

Ramp magnitude

RS:

Random subspace

RSM:

Response surface method

RVFL:

Random vector functional link

SARIMA:

Seasonal auto regressive integrated moving average

SCADA:

Supervisory control and data acquisition

SCG:

Scaled conjugate gradient

SP:

Smart persistence

SRSCAD:

Square root smoothly clipped absolute deviation

SVM:

Support vector machine

TIC:

Theil inequality coefficient

TMLM:

Time-varying multiple linear model

TSRY:

Typical solar radiation year

TMY:

Typical meteorological year

WD:

Wavelet decomposition

WGPR:

Weighted Gaussian process regression

WGPR-CFA:

Weighted Gaussian process regression — cascade forecasting architecture

WGPR-PFA:

Weighted Gaussian process regression — parallel forecasting architecture

WI:

Wilmot’s index

WMIM:

Wrapper mutual information methodology

WRF:

Weather research and forecasting

WT:

Wavelet transform

References

  1. Sun S, Wang S, Zhang G, et al. A decomposition-clustering-ensemble learning approach for solar radiation forecasting. Solar Energy, 2018, 163: 189–199

    Article  Google Scholar 

  2. Bahaj A S. Means of enhancing and promoting the use of solar energy. Renewable Energy, 2002, 27(1): 97–105

    Article  Google Scholar 

  3. Barnes D I. Understanding pulverised coal, biomass and waste combustion—a brief overview. Applied Thermal Engineering, 2015, 74: 89–95

    Article  Google Scholar 

  4. Setel A, Gordan I M, Gordan C E. Use of geothermal energy to produce electricity and heating at average temperatures. In: Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion, Belgrade, Serbia, 2016

  5. Alhmoud L, Wang B. A review of the state-of-the-art in wind-energy reliability analysis. Renewable & Sustainable Energy Reviews, 2018, 81: 1643–1651

    Article  Google Scholar 

  6. Sobri S, Koohi-Kamali S, Rahim N A. Solar photovoltaic generation forecasting methods: a review. Energy Conversion and Management, 2018, 156: 459–497

    Article  Google Scholar 

  7. International Energy Agency. Snapshot of global photovoltaic markets. Technical Report IEA PVPS T1-332018, 2018

  8. Fan J, Wu L, Zhang F, et al. Empirical and machine learning models for predicting daily global solar radiation from sunshine duration: a review and case study in China. Renewable & Sustainable Energy Reviews, 2019, 100: 186–212

    Article  Google Scholar 

  9. Mohanty S, Patra P K, Sahoo S S, Mohanty A. Forecasting of solar energy with application for a growing economy like India: survey and implication. Renewable & Sustainable Energy Reviews, 2017, 78: 539–553

    Article  Google Scholar 

  10. International Energy Agency. Snapshot of global PV markets. Photovoltaic Power Systems Technology Collaboration Program Report IEA PVPS T1-35, 2019

  11. International Renewable Energy Agency (IRENA). Renewable capacity statistics 2019. Technical Report, Abu Dhabi, 2019

  12. Graph G. Annual report by Ministry of New and Renewable Energy. 2017, available at the website of mnre.gov.in

  13. Singh R K. India’s renewable energy capacity crosses 80 GW-mark. 2019-07-16, available at website of The Economic Times.

  14. Masson G, Brunisholz M. 2015 snapshot of global photovoltaic markets. IEA PVPS T1-292016, 2016

  15. Kalogirou S A. Global photovoltaic markets. In: McEvoy’s Handbook of Photovoltaics. Academic Press, 2016: 1231–1235

  16. Nwokolo S C, Ogbulezie J C. A quantitative review and classification of empirical models for predicting global solar radiation in West Africa. Beni-Suef University Journal of Basic and Applied Sciences, 2018, 7(4): 367–396

    Article  Google Scholar 

  17. Wang K, Qi X, Liu H. A comparison of day-ahead photovoltaic power forecasting models based on deep learning neural network. Applied Energy, 2019, 251: 113315

    Article  Google Scholar 

  18. da Silva Fonseca J G Jr, Oozeki T, Takashima T, et al. Use of support vector regression and numerically predicted cloudiness to forecast power output of a photovoltaic power plant in Kitakyushu, Japan. Progress in Photovoltaics: Research and Applications, 2012, 20(7): 874–882

    Article  Google Scholar 

  19. Antonopoulos V Z, Papamichail D M, Aschonitis V G, et al. Solar radiation estimation methods using ANN and empirical models. Computers and Electronics in Agriculture, 2019, 160: 160–167

    Article  Google Scholar 

  20. Manjili Y S, Vega R, Jamshidi M M. Data-analytic-based adaptive solar energy forecasting framework. IEEE Systems Journal, 2018, 12(1): 285–296

    Article  Google Scholar 

  21. Antonanzas J, Osorio N, Escobar R, et al. Review of photovoltaic power forecasting. Solar Energy, 2016, 136: 78–111

    Article  Google Scholar 

  22. Kumar A. KUSUM scheme for solar uptake by farmers: a fineprint. 2019-03-22, available at website of ET EnergyWorld

  23. Gigoni L, Betti A, Crisostomi E, et al. Day-ahead hourly forecasting of power generation from photovoltaic plants. IEEE Transaction on Sustainable Energy, 2018, 9(2): 831–842

    Article  Google Scholar 

  24. Stanwell. Negative prices: how they occur, what they mean? 2020-04-16, available at website of Stanwell

  25. Götz P, Henkel J, Lenck T, et al. Negative Electricity Prices: Causes and Effects. Agora Energiewende, 2014

  26. Yang D, Wu E, Kleissl J. Operational solar forecasting for the realtime market. International Journal of Forecasting, 2019, 35(4): 1499–1519

    Article  Google Scholar 

  27. Huld T. VMAPS: software tools and data for the estimation of solar radiation and photovoltaic module performance over large geographical areas. Solar Energy, 2017, 142: 171–181

    Article  Google Scholar 

  28. Khosravi A, Nunes R O, Assad M E H, et al. Comparison of artificial intelligence methods in estimation of daily global solar radiation. Journal of Cleaner Production, 2018, 194: 342–358

    Article  Google Scholar 

  29. Munkhammar J, Van der Meer D, Widén J. Probabilistic forecasting of high-resolution clear-sky index time-series using a Markov-chain mixture distribution model. Solar Energy, 2019, 184: 688–695

    Article  Google Scholar 

  30. Fan J, Chen B, Wu L, et al. Evaluation and development of temperature-based empirical models for estimating daily global solar radiation in humid regions. Energy, 2018, 144: 903–914

    Article  Google Scholar 

  31. Premalatha N, Valan Arasu A. Prediction of solar radiation for solar systems by using ANN models with different back propagation algorithms. Journal of Applied Research and Technology, 2016, 14(3): 206–214

    Article  Google Scholar 

  32. Behrang M A, Assareh E, Ghanbarzadeh A, et al. The potential of different artificial neural network (ANN) techniques in daily global solar radiation modeling based on meteorological data. Solar Energy, 2010, 84(8): 1468–1480

    Article  Google Scholar 

  33. Fouilloy A, Voyant C, Notton G, et al. Solar irradiation prediction with machine learning: forecasting models selection method depending on weather variability. Energy, 2018, 165: 620–629

    Article  Google Scholar 

  34. Mazorra-Aguiar L, Díaz F. Solar radiation forecasting with statistical models. In: Wind Field and Solar Radiation Characterization and Forecasting: A Numerical Approach for Complex Terrain, Springer Verlag, 2018: 171–200

  35. Dolara A, Leva S, Manzolini G. Comparison of different physical models for PV power output prediction. Solar Energy, 2015, 119: 83–99

    Article  Google Scholar 

  36. Liu Y, Shimada S, Yoshino J, et al. Ensemble forecasting of solar irradiance by applying a mesoscale meteorological model. Solar Energy, 2016, 136: 597–605

    Article  Google Scholar 

  37. Yadav H K, Pal Y, Tripathi M M. A novel GA-ANFIS hybrid model for short-term solar PV power forecasting in Indian electricity market. Journal of Information and Optimization Science, 2019, 40(2): 377–395

    Article  Google Scholar 

  38. Reilly P M. Probability and statistics for engineers and scientists. Canadian Journal of Statistics, 1978, 6(2): 283–284

    Article  Google Scholar 

  39. Wan C, Zhao J, Song Y, et al. Photovoltaic and solar power forecasting for smart grid energy management. CSEE Journal of Power Energy Systems, 2015, 1(4): 38–46

    Article  Google Scholar 

  40. Yule G U. On the time-correlation problem, with especial reference to the variate-difference correlation method. Journal of the Royal Statistical Society, 1921, 84(4): 497

    Article  Google Scholar 

  41. Wang Y, Wang C, Shi C, et al. Short-term cloud coverage prediction using the ARIMA time series model. Remote Sensing Letters, 2018, 9(3): 274–283

    Article  Google Scholar 

  42. MATLAB. Works India-Econometric Modeler App Overview. 2019-12-20, available at website of MathWorks India

  43. Sekulima E B, El Moursi M S, Al Hinai A, et al. Wind speed and solar irradiance forecasting techniques for enhanced renewable energy integration with the grid: a review. IET Renewable Power Generation, 2016, 10(7): 885–989

    Article  Google Scholar 

  44. Atique S, Noureen S, Roy V, et al. Forecasting of total daily solar energy generation using ARIMA: a case study. In: 9th Annual Computing and Communication Workshop Conference, Las Vegas, NV, USA, 2019, 114–119

  45. Alsharif M H, Younes M K, Kim J. Time series ARIMA model for prediction of daily and monthly average global solar radiation: the case study of Seoul, South Korea. Symmetry, 2019, 11(2): 240

    Article  Google Scholar 

  46. Shadab A, Said S, Ahmad S. Box-Jenkins multiplicative ARIMA modeling for prediction of solar radiation: a case study. International Journal of Energy Water Resources, 2019, 3: 0123456789

    Article  Google Scholar 

  47. Colak I, Yesilbudak M, Genc N, et al. Multi-period prediction of solar radiation using ARMA and ARIMA models. In: IEEE 14th International Conference Machine Learning and Applications, Miami, FL, USA, 2015, 1045–1049

  48. Doorga J, Rughooputh S, Boojhawon R. Modelling the global solar radiation climate of Mauritius using regression techniques. Renewable Energy, 2019, 131: 861–878

    Article  Google Scholar 

  49. Trapero J R, Kourentzes N, Martin A. Short-term solar irradiation forecasting based on dynamic harmonic regression. Energy, 2015, 84: 289–295

    Article  Google Scholar 

  50. Suthar M, Singh G K, Saini R P. Effects of air pollution for estimating global solar radiation in India. International Journal of Sustainable Energy, 2017, 36(1): 20–27

    Article  Google Scholar 

  51. Bright J M, Smith C J, Taylor P G, et al. Stochastic generation of synthetic minutely irradiance time series derived from mean hourly weather observation data. Solar Energy, 2015, 115: 229–242

    Article  Google Scholar 

  52. Voyant C, Notton G, Kalogirou S, et al. Machine learning methods for solar radiation forecasting: a review. Renewable Energy, 2017, 105: 569–582

    Article  Google Scholar 

  53. Gurarie E. Introduction to stochastic processes: Markov chains. 2019-11-22, available at website of Department of Mathematics, University of Washington

  54. Eniola V, Suriwong T, Sirisamphanwong C, et al. Hour-ahead forecasting of photovoltaic power output based on hidden Markov model and genetic algorithm. International Journal of Renewable Energy Research, 2019, 9(2): 933–943

    Google Scholar 

  55. Bhardwaj S, Sharma V, Srivastava S, et al. Estimation of solar radiation using a combination of hidden Markov model and generalized fuzzy model. Solar Energy, 2013, 93: 43–54

    Article  Google Scholar 

  56. Wibun A, Chaiwiwatworakul P. An estimation of Thailand’s hourly solar radiation using Markov transition matrix method. Applied Mechanics and Materials, 2016, 839: 29–33

    Article  Google Scholar 

  57. Hocaoglu F O, Serttas F. A novel hybrid (Mycielski-Markov) model for hourly solar radiation forecasting. Renewable Energy, 2017, 108: 635–643

    Article  Google Scholar 

  58. Li S, Ma H, Li W. Typical solar radiation year construction using k-means clustering and discrete-time Markov chain. Applied Energy, 2017, 205: 720–731

    Article  Google Scholar 

  59. Mengaldo G, Wyszogrodzki A, Diamantakis M, et al. Current and emerging time-integration strategies in global numerical weather and climate prediction. Archives of Computational Methods in Engineering, 2019, 26(3): 663–684

    Article  MathSciNet  Google Scholar 

  60. Yadav A, Chandel S. Solar radiation prediction using artificial neural network techniques: a review. Renewable & Sustainable Energy Reviews, 2014, 33: 772–781

    Article  Google Scholar 

  61. Das S, Ashrit R, Iyengar G, et al. Skills of different mesoscale models over Indian region during monsoon season: forecast errors. Journal of Earth System Science, 2008, 117(5): 603–620

    Article  Google Scholar 

  62. Verzijlbergh R A, Heijnen P W, de Roode S R, et al. Improved model output statistics of numerical weather prediction based irradiance forecasts for solar power applications. Solar Energy, 2015, 118: 634–645

    Article  Google Scholar 

  63. Verbois H, Huva R, Rusydi A, et al. Solar irradiance forecasting in the tropics using numerical weather prediction and statistical learning. Solar Energy, 2018, 162: 265–277

    Article  Google Scholar 

  64. Bakker K, Whan K, Knap W, et al. Comparison of statistical postprocessing methods for probabilistic NWP forecasts of solar radiation. Solar Energy, 2019, 191: 138–150

    Article  Google Scholar 

  65. Hargreaves G H, Samani Z A. Estimating potential evapotranspiration. Journal of the Irrigation and Drainage Division, 1982, 108(3): 225–230

    Article  Google Scholar 

  66. Besharat F, Dehghan A A, Faghih A R. Empirical models for estimating global solar radiation: a review and case study. Renewable & Sustainable Energy Reviews, 2013, 21: 798–821

    Article  Google Scholar 

  67. Mahajan B Y, Namrata K. Performance evaluation of developed empirical models for predicting global solar radiation in western region of India. International Journal of Renewable Energy Research, 2019, 9(3): 1135–1143

    Google Scholar 

  68. Quansah E, Amekudzi L K, Preko K, et al. Empirical models for estimating global solar radiation over the Ashanti Region of Ghana. Journal of Solar Energy, 2014: 897970

  69. Ayodele T R, Ogunjuyigbe A S O. Performance assessment of empirical models for prediction of daily and monthly average global solar radiation: the case study of Ibadan, Nigeria. International Journal of Ambient Energy, 2017, 38(8): 803–813

    Article  Google Scholar 

  70. Bailek N, Bouchouicha K, Al-Mostafa Z, et al. A new empirical model for forecasting the diffuse solar radiation over Sahara in the Algerian Big South. Renewable Energy, 2018, 117: 530–537

    Article  Google Scholar 

  71. Fausett L V. Fundamentals of Neural Networks: Architectures, Algorithms, and Applications. Prentice-Hall, Inc., USA, 1994

    MATH  Google Scholar 

  72. Ghanbarzadeh A, Noghrehabadi A R, Assareh E, et al. Solar radiation forecasting based on meteorological data using artificial neural networks. In: 7th IEEE International Conference of Industrial Informatics, Cardiff, Wales, UK, 2009: 227–231

  73. Elsheikh A H, Sharshir S W, Abd Elaziz M, et al. Modeling of solar energy systems using artificial neural network: a comprehensive review. Solar Energy, 2019, 180: 622–639

    Article  Google Scholar 

  74. Samara S, Natsheh E. Modeling the output power of heterogeneous photovoltaic panels based on artificial neural networks using low cost microcontrollers. Heliyon, 2018, 4(11): e00972

    Article  Google Scholar 

  75. Bou-Rabee M, Sulaiman S A, Saleh M S, et al. Using artificial neural networks to estimate solar radiation in Kuwait. Renewable & Sustainable Energy Reviews, 2017, 72: 434–438

    Article  Google Scholar 

  76. Sözen A, Arcakliolu E, Özalp M, et al. Forecasting based on neural network approach of solar potential in Turkey. Renewable Energy, 2005, 30(7): 1075–1090

    Article  Google Scholar 

  77. Koca A, Oztop H F, Varol Y, et al. Estimation of solar radiation using artificial neural networks with different input parameters for Mediterranean region of Anatolia in Turkey. Expert Systems with Applications, 2011, 38(7): 8756–8762

    Article  Google Scholar 

  78. Yu Y, Cao J, Zhu J. An LSTM short-term solar irradiance forecasting under complicated weather conditions. IEEE Access: Practical Innovations, Open Solutions, 2019, 7: 145651–145666

    Article  Google Scholar 

  79. Kumar N, Sinha U K, Sharma S P. Prediction of daily global solar radiation using neural networks with improved gain factors and RBF Networks. International Journal of Renewable Energy Research, 2017, 7(3): 1235–1244

    Google Scholar 

  80. Notton G, Voyant C, Fouilloy A, et al. Some applications of ANN to solar radiation estimation and forecasting for energy applications. Applied Sciences (Basel, Switzerland), 2019, 9(1): 209

    Google Scholar 

  81. Rodríguez F, Fleetwood A, Galarza A, et al. Predicting solar energy generation through artificial neural networks using weather forecasts for microgrid control. Renewable Energy, 2018, 126: 855–864

    Article  Google Scholar 

  82. Jahani B, Mohammadi B. A comparison between the application of empirical and ANN methods for estimation of daily global solar radiation in Iran. Theoretical and Applied Climatology, 2019, 137 (1–2): 1257–1269

    Article  Google Scholar 

  83. Sivaneasan B, Yu C Y, Goh K P. Solar forecasting using ANN with fuzzy logic pre-processing. Energy Procedia, 2017, 143: 727–732

    Article  Google Scholar 

  84. Cervone G, Clemente-Harding L, Alessandrini S, et al. Short-term photovoltaic power forecasting using artificial neural networks and an analog ensemble. Renewable Energy, 2017, 108: 274–286

    Article  Google Scholar 

  85. Chen C R, Kartini U T. K-nearest neighbor neural network models for very short-term global solar irradiance forecasting based on meteorological data. Energies, 2017, 10(2): 186

    Article  Google Scholar 

  86. Li Z, Rahman S M, Vega R, et al. A hierarchical approach using machine learning methods in solar photovoltaic energy production forecasting. Energies, 2016, 9(1): 55

    Article  Google Scholar 

  87. Vakili M, Sabbagh-Yazdi S R, Khosrojerdi S, et al. Evaluating the effect of particulate matter pollution on estimation of daily global solar radiation using artificial neural network modeling based on meteorological data. Journal of Cleaner Production, 2017, 141: 1275–1285

    Article  Google Scholar 

  88. Hossain R, Ooa A M T, Alia A B M S. Historical weather data supported hybrid renewable energy forecasting using artificial neural network (ANN). Energy Procedia, 2012, 14: 1035–1040

    Article  Google Scholar 

  89. İzgi E, Öztopal A, Yerli B, et al. Short-mid-term solar power prediction by using artificial neural networks. Solar Energy, 2012, 86(2): 725–733

    Article  Google Scholar 

  90. Alam S, Kaushik S C, Garg S N. Assessment of diffuse solar energy under general sky condition using artificial neural network. Applied Energy, 2009, 86(4): 554–564

    Article  Google Scholar 

  91. Awad M, Khanna R. Support vector machines for classification. In: Efficient Learning Machines, Apress, Berkeley, CA, 2015, 39–66

  92. Zendehboudi A, Baseer M A, Saidur R. Application of support vector machine models for forecasting solar and wind energy resources: a review. Journal of Cleaner Production, 2018, 199: 272–285

    Article  Google Scholar 

  93. Srivastava N, Hinton G, Krizhevsky A, et al. Dropout: a simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 2014, 15: 1929–1958

    MathSciNet  MATH  Google Scholar 

  94. Zhang W, Chen J. Relief feature selection and parameter optimization for support vector machine based on mixed kernel function. International Journal of Performability Engineering, 2018, 14(2): 280–289

    Google Scholar 

  95. Ruiz-Gonzalez R, Gomez-Gil J, Gomez-Gil F J, et al. An SVM-based classifier for estimating the state of various rotating components in agro-industrial machinery with a vibration signal acquired from a single point on the machine chassis. Sensors (Switzerland), 2014, 14(11): 20713–20735

    Article  Google Scholar 

  96. Jiménez-Pérez P F, Mora-López L. Modeling and forecasting hourly global solar radiation using clustering and classification techniques. Solar Energy, 2016, 135: 682–691

    Article  Google Scholar 

  97. Zeng J, Qiao W. Short-term solar power prediction using a support vector machine. Renewable Energy, 2013, 52: 118–127

    Article  Google Scholar 

  98. Shi J, Lee W J, Liu Y, et al. Forecasting power output of photovoltaic systems based on weather classification and support vector machines. IEEE Transactions on Industry Applications, 2012, 48(3): 1064–1069

    Article  Google Scholar 

  99. Jang H S, Bae K Y, Park H S, et al. Solar power prediction based on satellite images and support vector machine. IEEE Transaction on Sustainable Energy, 2016, 7(3): 1255–1263

    Article  Google Scholar 

  100. Fan J, Wu L, Zhang F, et al. Evaluating the effect of air pollution on global and diffuse solar radiation prediction using support vector machine modeling based on sunshine duration and air temperature. Renewable & Sustainable Energy Reviews, 2018, 94: 732–747

    Article  Google Scholar 

  101. Ma M, Zhao L, Deng S, et al. Estimation of horizontal direct solar radiation considering air quality index in China. Energy Procedia, 2019, 158: 424–430

    Article  Google Scholar 

  102. Gensler A, Henze J, Sick B, et al. Deep learning for solar power forecasting—an approach using AutoEncoder and LSTM neural networks. In: 2016 IEEE International Conference on Systems Man and Cybernetics, Budapest, 2016, 2858–2865

  103. Suresh V, Janik P, Rezmer J, et al. Forecasting solar PV output using convolutional neural networks with a sliding window algorithm. Energies, 2020, 13(3): 723

    Article  Google Scholar 

  104. Aslam M, Lee J M, Kim H S, et al. Deep learning models for long-term solar radiation forecasting considering microgrid installation: a comparative study. Energies, 2019, 13(1): 147

    Article  Google Scholar 

  105. Qing X, Niu Y. Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM. Energy, 2018, 148: 461–468

    Article  Google Scholar 

  106. Chandola D, Gupta H, Tikkiwal V A, et al. Multi-step ahead forecasting of global solar radiation for arid zones using deep learning. Procedia Computer Science, 2020, 167: 626–635

    Article  Google Scholar 

  107. Wang Y, Liao W, Chang Y. Gated recurrent unit network-based short-term photovoltaic forecasting. Energies, 2018, 11(8): 2163

    Article  Google Scholar 

  108. Sodsong N, Yu K M, Ouyang W. Short-term solar PV forecasting using gated recurrent unit with a cascade model. In: 1st IEEE International Conference on Artificial Intelligence in Information and Communication, Okinawa, Japan, 2019, 292–297

  109. Xie Y. Values and limitations of statistical models. Research in Social Stratification and Mobility, 2011, 29(3): 343–349

    Article  Google Scholar 

  110. Kumar K R, Kalavathi M S. Artificial intelligence based forecast models for predicting solar power generation. Materials Today: Proceedings, 2018, 5(1): 796–802

    Google Scholar 

  111. Raza M Q, Nadarajah M, Li J, et al. An ensemble framework for day-ahead forecast of PV output power in smart grids. IEEE Transactions on Industrial Informatics, 2019, 15(8): 4624–4634

    Article  Google Scholar 

  112. Benali L, Notton G, Fouilloy A, et al. Solar radiation forecasting using artificial neural network and random forest methods: application to normal beam, horizontal diffuse and global components. Renewable Energy, 2019, 132: 871–884

    Article  Google Scholar 

  113. Elminir H K, Areed F F, Elsayed T S. Estimation of solar radiation components incident on Helwan site using neural networks. Solar Energy, 2005, 79(3): 270–279

    Article  Google Scholar 

  114. Heydari A, Garcia D A, Keynia F, et al. A novel composite neural network based method for wind and solar power forecasting in microgrids. Applied Energy, 2019, 251: 113353

    Article  Google Scholar 

  115. Mellit A, Pavan A M A. 24-h forecast of solar irradiance using artificial neural network: application for performance prediction of a grid-connected PV plant at Trieste, Italy. Solar Energy, 2010, 84 (5): 807–821

    Article  Google Scholar 

  116. Chen S X, Gooi H B, Wang M Q. Solar radiation forecast based on fuzzy logic and neural networks. Renewable Energy, 2013, 60: 195–201

    Article  Google Scholar 

  117. Lan H, Zhang C, Hong Y Y, et al. Day-ahead spatiotemporal solar irradiation forecasting using frequency-based hybrid principal component analysis and neural network. Applied Energy, 2019, 247: 389–402

    Article  Google Scholar 

  118. Cornejo-Bueno L, Casanova-Mateo C, Sanz-Justo J, et al. Machine learning regressors for solar radiation estimation from satellite data. Solar Energy, 2019, 183: 768–775

    Article  Google Scholar 

  119. Aguiar L M, Pereira B, Lauret P, et al. Combining solar irradiance measurements, satellite-derived data and a numerical weather prediction model to improve intra-day solar forecasting. Renewable Energy, 2016, 97: 599–610

    Article  Google Scholar 

  120. Shamshirband S, Mohammadi K, Khorasanizadeh H, et al. Estimating the diffuse solar radiation using a coupled support vector machine-wavelet transform model. Renewable & Sustainable Energy Reviews, 2016, 56: 428–435

    Article  Google Scholar 

  121. Dong N, Chang J F, Wu A G, et al. A novel convolutional neural network framework based solar irradiance prediction method. International Journal of Electrical Power & Energy Systems, 2020, 114: 105411

    Article  Google Scholar 

  122. Bouzgou H, Gueymard C A. Fast short-term global solar irradiance forecasting with wrapper mutual information. Renewable Energy, 2019, 133: 1055–1065

    Article  Google Scholar 

  123. Ghimire S, Deo R C, Downs N J, et al. Global solar radiation prediction by ANN integrated with European Centre for medium range weather forecast fields in solar rich cities of Queensland Australia. Journal of Cleaner Production, 2019, 216: 288–310

    Article  Google Scholar 

  124. Huang C, Wang L, Lai L L. Data-driven short-term solar irradiance forecasting based on information of neighboring sites. IEEE Transactions on Industrial Electronics, 2019, 66(12): 9918–9927

    Article  Google Scholar 

  125. Huang X, Shi J, Gao B, et al. Forecasting hourly solar irradiance using hybrid wavelet transformation and Elman model in smart grid. IEEE Access: Practical Innovations, Open Solutions, 2019, 7: 139909–139923

    Article  Google Scholar 

  126. Liu Y, Qin H, Zhang Z, et al. Ensemble spatiotemporal forecasting of solar irradiation using variational Bayesian convolutional gate recurrent unit network. Applied Energy, 2019, 253: 113596

    Article  Google Scholar 

  127. Guermoui M, Melgani F, Danilo C. Multi-step ahead forecasting of daily global and direct solar radiation: a review and case study of Ghardaia region. Journal of Cleaner Production, 2018, 201: 716–734

    Article  Google Scholar 

  128. Wang F, Mi Z, Su S, et al. Short-term solar irradiance forecasting model based on artificial neural network using statistical feature parameters. Energies, 2012, 5(5): 1355–1370

    Article  Google Scholar 

  129. Liu D, Sun K. Random forest solar power forecast based on classification optimization. Energy, 2019, 187: 115940

    Article  Google Scholar 

  130. Zhang W, Dang H, Simoes R. A new solar power output prediction based on hybrid forecast engine and decomposition model. ISA Transactions, 2018, 81: 105–120

    Article  Google Scholar 

  131. Caldas M, Alonso-Suárez R. Very short-term solar irradiance forecast using all-sky imaging and real-time irradiance measurements. Renewable Energy, 2019, 143: 1643–1658

    Article  Google Scholar 

  132. Eseye A T, Zhang J, Zheng D. Short-term photovoltaic solar power forecasting using a hybrid Wavelet-PSO-SVM model based on SCADA and meteorological information. Renewable Energy, 2018, 118: 357–367

    Article  Google Scholar 

  133. VanDeventer W, Jamei E, Thirunavukkarasu G S, et al. Short-term PV power forecasting using hybrid GASVM technique. Renewable Energy, 2019, 140: 367–379

    Article  Google Scholar 

  134. Dong Y, Jiang H. Global solar radiation forecasting using square root regularization-based ensemble. Mathematical Problems in Engineering, 2019: 9620945

  135. Abuella M, Chowdhury B. Forecasting of solar power ramp events: a post-processing approach. Renewable Energy, 2019, 133: 1380–1392

    Article  Google Scholar 

  136. Dong J, Olama M M, Kuruganti T, et al. Novel stochastic methods to predict short-term solar radiation and photovoltaic power. Renewable Energy, 2020, 145: 333–346

    Article  Google Scholar 

  137. Kushwaha V, Pindoriya N M. A SARIMA-RVFL hybrid model assisted by wavelet decomposition for very short-term solar PV power generation forecast. Renewable Energy, 2019, 140: 124–139

    Article  Google Scholar 

  138. Liu Y, Zhou Y, Chen Y, et al. Comparison of support vector machine and copula-based nonlinear quantile regression for estimating the daily diffuse solar radiation: a case study in China. Renewable Energy, 2020, 146: 1101–1112

    Article  Google Scholar 

  139. Zhou H, Zhang Y, Yang L, et al. Short-term photovoltaic power forecasting based on long short term memory neural network and attention mechanism. IEEE Access: Practical Innovations, Open Solutions, 2019, 7: 78063–78074

    Article  Google Scholar 

  140. Srivastava R, Tiwari A N, Giri V K. Solar radiation forecasting using MARS, CART, M5, and random forest model: a case study for India. Heliyon, 2019, 5(10): e02692

    Article  Google Scholar 

  141. Basurto N, Arroyo A, Vega R, et al. A hybrid intelligent system to forecast solar energy production. Computers & Electrical Engineering, 2019, 78: 373–387

    Article  Google Scholar 

  142. Liu L, Zhan M, Bai Y. A recursive ensemble model for forecasting the power output of photovoltaic systems. Solar Energy, 2019, 189: 291–298

    Article  Google Scholar 

  143. Feng C, Cui M, Hodge B M, et al. Unsupervised clustering-based short-term solar forecasting. IEEE Transaction on Sustainable Energy, 2019, 10(4): 2174–2185

    Article  Google Scholar 

  144. Monjoly S, André M, Calif R, et al. Hourly forecasting of global solar radiation based on multiscale decomposition methods: a hybrid approach. Energy, 2017, 119: 288–298

    Article  Google Scholar 

  145. Benmouiza K, Cheknane A. Forecasting hourly global solar radiation using hybrid k-means and nonlinear autoregressive neural network models. Energy Conversion and Management, 2013, 75: 561–569

    Article  Google Scholar 

  146. Ghimire S, Deo R C, Raj N, et al. Deep solar radiation forecasting with convolutional neural network and long short-term memory network algorithms. Applied Energy, 2019, 253: 113541

    Article  Google Scholar 

  147. Jiang H, Lu N, Qin J, et al. A deep learning algorithm to estimate hourly global solar radiation from geostationary satellite data. Renewable & Sustainable Energy Reviews, 2019, 114: 109327

    Article  Google Scholar 

  148. AlKandari M, Ahmad I. Solar power generation forecasting using ensemble approach based on deep learning and statistical methods. Applied Computing and Informatics, 2016, online, doi: https://doi.org/10.1016/j.aci.2019.11.002

  149. Durrani S P, Balluff S, Wurzer L, et al. Photovoltaic yield prediction using an irradiance forecast model based on multiple neural networks. Journal of Modern Power Systems and Clean Energy, 2018, 6(2): 255–267

    Article  Google Scholar 

  150. Zhang J, Hodge B M, Florita A, et al. Metrics for evaluating the accuracy of solar power forecasting. In: 3rd International Workshop on Integration of Solar Power into Power Systems, London, UK, 2013, 17436

  151. Willmott C J, Matsuura K. Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Research, 2005, 30(1): 79–82

    Article  Google Scholar 

  152. Lauret P, Voyant C, Soubdhan T, et al. A benchmarking of machine learning techniques for solar radiation forecasting in an insular context. Solar Energy, 2015, 112: 446–457

    Article  Google Scholar 

  153. Coimbra C F M, Kleissl J, Marquez R. Overview of solar-forecasting methods and a metric for accuracy evaluation. In: Kleissl J, ed. Solar Energy Forecasting and Resource Assessment. Academic Press, 2013: 171–194

  154. Crabtree G, Misewich J, Ambrosio R, et al. Integrating renewable electricity on the grid. In: AIP Conference Proceedings, 2011, 1401: 387–405

  155. Hyndman R J, Koehler A B. Another look at measures of forecast accuracy. International Journal of Forecasting, 2006, 22(4): 679–688

    Article  Google Scholar 

  156. Pereira R M, Silva Santos C, Rocha A. Solar irradiance modelling using an offline coupling procedure for the weather research and forecasting (WRF) model. Solar Energy, 2019, 188: 339–352

    Article  Google Scholar 

  157. Zhang J, Zhang Y, Yu C S. Rényi entropy uncertainty relation for successive projective measurements. Quantum Information Processing, 2015, 14(6): 2239–2253

    Article  MathSciNet  MATH  Google Scholar 

  158. Florita A, Hodge B M, Orwig K. Identifying wind and solar ramping events. In: IEEE Green Technologies Conference (Green-Tech), Denver, CO, USA, 2013, 147–152

  159. Chu Y, Pedro H T C, Li M, et al. Real-time forecasting of solar irradiance ramps with smart image processing. Solar Energy, 2015, 114: 91–104

    Article  Google Scholar 

  160. Russo M, Leotta G, Pugliatti P M, et al. Genetic programming for photovoltaic plant output forecasting. Solar Energy, 2014, 105: 264–273

    Article  Google Scholar 

  161. Despotovic M, Nedic V, Despotovic D, et al. Review and statistical analysis of different global solar radiation sunshine models. Renewable & Sustainable Energy Reviews, 2015, 52: 1869–1880

    Article  Google Scholar 

  162. Alessandrini S, Delle Monache L, Sperati S, et al. An analog ensemble for short-term probabilistic solar power forecast. Applied Energy, 2015, 157: 95–110

    Article  Google Scholar 

  163. Rana M, Koprinska I, Agelidis V G. 2D-interval forecasts for solar power production. Solar Energy, 2015, 122: 191–203

    Article  Google Scholar 

  164. Almeida M P, Perpiñán O, Narvarte L. PV power forecast using a nonparametric PV model. Solar Energy, 2015, 115: 354–368

    Article  Google Scholar 

  165. Paulescu M, Paulescu E. Short-term forecasting of solar irradiance. Renewable Energy, 2019, 143: 985–994

    Article  Google Scholar 

  166. Voyant C, Notton G. Solar irradiation nowcasting by stochastic persistence: a new parsimonious, simple and efficient forecasting tool. Renewable & Sustainable Energy Reviews, 2018, 92: 343–352

    Article  Google Scholar 

  167. Jiang F, Jiang Y, Zhi H, et al. Artificial intelligence in healthcare: past, present and future. Stroke and Vascular Neurology, 2017, 2 (4): 230–243

    Article  Google Scholar 

  168. Wang H B, Xiong J N, Zhao C Y. The mid-term forecast method of solar radiation index. Chinese Astronomy and Astrophysics, 2015, 39(2): 198–211

    Article  Google Scholar 

  169. Yang D. A guideline to solar forecasting research practice: reproducible, operational, probabilistic or physically-based, ensemble, and skill (ROPES). Journal of Renewable and Sustainable Energy, 2019, 11(2): 022701

    Article  Google Scholar 

  170. Gil V, Gaertner M A, Gutierrez C, et al. Impact of climate change on solar irradiation and variability over the Iberian Peninsula using regional climate models. International Journal of Climatology, 2018, 39(3): 1733–1747

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of ECE, Deenbandhu Chhotu Ram University of Science and Technology, Murthal, Sonepat, 131039, India

    Pardeep Singla & Manoj Duhan

  2. Department of Printing Technology (Electrical Engineering), Guru Jambheshwar University of Science and Technology, Hisar, 125001, India

    Sumit Saroha

Authors
  1. Pardeep Singla
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Manoj Duhan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sumit Saroha
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pardeep Singla.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Singla, P., Duhan, M. & Saroha, S. A comprehensive review and analysis of solar forecasting techniques. Front. Energy 16, 187–223 (2022). https://doi.org/10.1007/s11708-021-0722-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11708-021-0722-7

Keywords

Search

Navigation