A comprehensive review and analysis of solar forecasting techniques

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 problem-solving 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.

Graphical abstract

Keywords

forecasting techniques / hybrid models / neural network / solar forecasting / error metric / support vector machine (SVM)

References

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

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

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

[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

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

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

[8] Fan J, Wu L, Zhang F, Cai H, Zeng W, Wang X, Zou H. 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

[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

[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

[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

[18] da Silva Fonseca J G Jr, Oozeki T, Takashima T, . 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

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

[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

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

[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, . Day-ahead hourly forecasting of power generation from photovoltaic plants. IEEE Transaction on Sustainable Energy, 2018, 9(2): 831–842

[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, . Negative Electricity Prices: Causes and Effects. Agora Energiewende, 2014.

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

[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

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

[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

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

[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

[32] Behrang M A, Assareh E, Ghanbarzadeh A, . 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

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

[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

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

[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

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

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

[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

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

[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, . 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

[44] Atique S, Noureen S, Roy V, . 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

[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

[47] Colak I, Yesilbudak M, Genc N, . 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

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

[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

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

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

[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, . 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

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

[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

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

[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

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

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

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

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

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

[64] Bakker K, Whan K, Knap W, . Comparison of statistical post-processing methods for probabilistic NWP forecasts of solar radiation. Solar Energy, 2019, 191: 138–150

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

[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

[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

[68] Quansah E, Amekudzi L K, Preko K, . Empirical models for estimating global solar radiation over the Ashanti Region of Ghana. Journal of Solar Energy, 2014, 2014: 1–6

[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

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

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

[72] Ghanbarzadeh A, Noghrehabadi A R, Assareh E, . 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, . Modeling of solar energy systems using artificial neural network: a comprehensive review. Solar Energy, 2019, 180: 622–639

[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

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

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

[77] Koca A, Oztop H F, Varol Y, . 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

[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

[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

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

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

[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

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

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

[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

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

[87] Vakili M, Sabbagh-Yazdi S R, Khosrojerdi S, . 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

[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

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

[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

[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

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

[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

[95] Ruiz-Gonzalez R, Gomez-Gil J, Gomez-Gil F J, . 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

[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

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

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

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

[100] Fan J, Wu L, Zhang F, . 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

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

[102] Gensler A, Henze J, Sick B, . 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, . Forecasting solar PV output using convolutional neural networks with a sliding window algorithm. Energies, 2020, 13(3): 723

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

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

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

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

[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

[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

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

[112] Benali L, Notton G, Fouilloy A, . 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

[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

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

[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

[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

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

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

[119] Aguiar L M, Pereira B, Lauret P, . 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

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

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

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

[123] Ghimire S, Deo R C, Downs N J,. 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

[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

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

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

[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

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

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

[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

[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

[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

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

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

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

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

[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

[138] Liu Y, Zhou Y, Chen Y, . 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

[139] Zhou H, Zhang Y, Yang L, . 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

[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

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

[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

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

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

[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

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

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

[148] AlKandari M, Ahmad I. Solar power generation forecasting using ensemble approach based on deep learning and statistical methods. Applied Computing and Informatics, 2016, (in press)

[149] Durrani S P, Balluff S, Wurzer L, . 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

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

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

[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, . 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

[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

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

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

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

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

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

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

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

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

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

[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

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

[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 doi:10.1016/j.chinastron.2015.04.010

[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

[170] Gil V, Gaertner M A, Gutierrez C, Impact of climate change on solar irradiation and variability over the Iberian Peninsula using regional climate models. International Journal of Climatology, 2018, 39(3): 1733–1747doi: 10.1002/joc.5916

RIGHTS & PERMISSIONS

2021 Higher Education Press