Solar Irradiation Forecasting Technologies: A Review
Renewable energy has received a lot of attention in the previous two decades when it comes to meeting electrical needs in the home, industrial, and agricultural sectors. Solar forecasting is critical for the efficient operation, scheduling, and balancing of energy generation by standalone and grid-connected solar PV systems. A variety of models and methods have been developed in the literature to forecast solar irradiance. This paper provides an analysis of the techniques used in the literature to forecast solar irradiance. The main focus of the study is to investigate the influence of meteorological variables, time horizons, climatic zone, pre-processing technique, optimization & sample size on the complexity and accuracy of the model. Due to their nonlinear complicated problem solving skills, artificial neural network based models outperform other models in the literature. Hybridizing the two models or performing pre-processing on the input data can improve their accuracy even more. It also addresses the various main constituents that influence a model’s accuracy. The paper provides key findings based on studied literature to select the optimal model for a specific site. This paper also discusses the metrics used to measure the efficiency of forecasted model. It has been observed that the proper selection of training and testing period also enhance the accuracy of the model.
S. Sun, S. Wang, G. Zhang, and J. Zheng, “A decomposition-clustering-ensemble learning approach for solar radiation forecasting,” Sol.Energy, vol. 163, pp. 189–199, Mar. 2018.
A. S. Bahaj, “Means of enhancing and promoting the use of solar energy,” Renew. Energy, vol. 27, no. 1, pp. 9-105, Sep.2002.
D. I. Barnes, “Understanding pulverized coal, biomass and waste combustion - A brief overview,” Appl. Therm. Eng., vol. 74, pp. 89–95, Jan. 2015.
A. Setel, I. M. Gordan, and C. E. Gordan, “Use of geothermal energy to produce electricity and heating at average temperatures,” in IET Conference Publications, 2016, vol. 2016, no. CP711.
L. Alhmoud and B. Wang, “A review of the state-of-the-art in wind-energy reliability analysis,” Elsevier Ltd, Jan. 2018.
IRENA, Renewable capacity statistics 2019, International Renewable Energy Agency (IRENA).
S. Sobri, S. Koohi-Kamali, and N. A. Rahim, “Solar photovoltaic generation forecasting methods: A review,” Energy Conversion and Management, vol. 156. Elsevier Ltd, pp. 459–497, 15-Jan-2018.
S. Mohanty, P. K. Patra, S. S. Sahoo, and A. Mohanty, “Forecasting of solar energy with application for a growing economy like India: Survey and implication,” Renewable and Sustainable Energy Reviews, vol. 78. Elsevier Ltd, pp. 539–553, 2017.
International Energy Agency, “Snapshot of Global Photovoltaic Markets – 2018,” Rep. IEA PVPS T1-332018, pp. 1–16, 2018.
“Photovoltaic Power System Technology Collaboration Program Snapshot of Global PV Markets: Report IEA PVPS T1-35:2019,” 2019.
IRENA, Rrnewable capacity statistics 2010, International Renewable Energy Agency (IRENA), Abu Dhabi. 2019.
“Annual Report, Ministry of New and Renewable Energy, Government of India,” 12–13, 2017.
R. K. Singh, “India’s renewable energy capacity crosses 80GW-mark,” The Economic Times, 2016. [online]. Available: https://economictimes.indiatimes.com/industry/energy
G. Masson and M. Brunisholz, “2015 Snapshot of global photovoltaic markets,” Iea Pvps T1-292016, pp. 1–19, 2016.
S. A. Kalogirou, “Global Photovoltaic Markets,” McEvoy’s Handb. Photovoltaics, pp. 1231–1235, 2016.
J. G. da Silva Fonseca, T. Oozeki, T. Takashima, G. Koshimizu, Y. Uchida, and K. Ogimoto, “Use of support vector regression and numerically predicted cloudiness to forecast power output of a photovoltaic power plant in Kitakyushu, Japan,” Prog. Photovoltaics Res. Appl., vol. 20, no. 7, pp. 874– 882, Nov. 2012.
V. Z. Antonopoulos, D. M. Papamichail, V. G. Aschonitis, and A. V. Antonopoulos, “Solar radiation estimation methods using ANN and empirical models,” Comput. Electron. Agric., vol. 160, pp. 160– 167, May 2019.
A. Khosravi, R. O. Nunes, M. E. H. Assad, and L. Machado, “Comparison of artificial intelligence methods in estimation of daily global solar radiation,” J. Clean. Prod., vol. 194, pp. 342–358, Sep. 2018.
N. Premalatha and A. Valan Arasu, “Prediction of solar radiation for solar systems by using ANN models with different back propagation algorithms,” J. Appl. Res. Technol., vol. 14, no. 3, pp. 206–214, Jun. 2016.
M. A. Behrang, E. Assareh, A. Ghanbarzadeh, and A. R. Noghrehabadi, “The potential of different artificial neural network (ANN) techniques in daily global solar radiation modeling based on meteorological data,” Sol. Energy, vol. 84, no. 8, pp. 1468–1480, Aug. 2010.
A. Fouilloy et al., “Solar irradiation prediction with machine learning: Forecasting models selection method depending on weather variability,” Energy, vol. 165, pp. 620–629, Dec. 2018.
L. Mazorra-Aguiar and F. Díaz, “Solar Radiation Forecasting with Statistical Models,” 2018, pp. 171–200.
Y. Liu, S. Shimada, J. Yoshino, T. Kobayashi, Y. Miwa, and K. Furuta, “Ensemble forecasting of solar irradiance by applying a mesoscale meteorological model,” Sol. Energy, vol. 136, pp. 597–605, Oct. 2016.
A. Ghanbarzadeh, A. R. Noghrehabadi, E. Assareh, and M. A. Behrang, “Solar radiation forecasting based on meteorological data using artificial neural networks,” in IEEE International Conference on Industrial Informatics (INDIN), 2009, pp. 227–231.
M. Bou-Rabee, S. A. Sulaiman, M. S. Saleh, and S. Marafi, “Using artificial neural networks to estimate solar radiation in Kuwait,” Renewable and Sustainable Energy Reviews, vol. 72. Elsevier Ltd, pp. 434– 438, 2017.
Y. Yu, J. Cao, and J. Zhu, “An LSTM Short-Term Solar Irradiance Forecasting under Complicated Weather Conditions,” IEEE Access, vol. 7, pp. 145651–145666, 2019.
N. Kumar, U. K. Sinha, S. P. Sharma, and Y. K. Nayak, “Prediction of daily global solar radiation using Neural Networks with improved gain factors and RBF Networks,” Int. J. Renew. Energy Res., vol. 7, no. 3, pp. 1235–1244, 2017.
G. Notton, C. Voyant, A. Fouilloy, J. L. Duchaud, and M. L. Nivet, “Some applications of ANN to solar radiation estimation and forecasting for energy applications,” Appl. Sci., vol. 9, no. 1, pp. 1–21, 2019.
F. Rodríguez, A. Fleetwood, A. Galarza, and L. Fontán, “Predicting solar energy generation through artificial neural networks using weather forecasts for microgrid control,” Renew. Energy, vol. 126, pp. 855–864, Oct. 2018.
B. Jahani and B. Mohammadi, “A comparison between the application of empirical and ANN methods for estimation of daily global solar radiation in Iran,” Theor. Appl. Climatol., vol. 137, no. 1–2, pp. 1257–1269, Jul. 2019.
B. Sivaneasan, C. Y. Yu, and K. P. Goh, “Solar Forecasting using ANN with Fuzzy Logic Pre- processing,” in Energy Procedia, 2017, vol. 143, pp. 727–732.
C. R. Chen and U. T. Kartini, “k-nearest neighbor neural network models for very short-term global solar irradiance forecasting based on meteorological data,” Energies, vol. 10, no. 2, 2017.
Z. Li, S. Rahman, R. Vega, and B. Dong, “A Hierarchical Approach Using Machine Learning Methods in Solar Photovoltaic Energy Production Forecasting,” Energies, vol. 9, no. 1, p. 55, Jan. 2016.
M. Vakili, S. R. Sabbagh-Yazdi, S. Khosrojerdi, and K. Kalhor, “Evaluating the effect of particulate matter pollution on estimation of daily global solar radiation using artificial neural network modeling based on meteorological data,” J. Clean. Prod., vol. 141, pp. 1275–1285, Jan. 2017.
R S. Alam, S. C. Kaushik, and S. N. Garg, “Assessment of diffuse solar energy under general sky condition using artificial neural network,” Appl. Energy, vol. 86, no. 4, pp. 554–564, 2009.
P. M. Reilly, “Probability and Statistics for Engineers and Scientists,” Can. J. Stat., vol. 6, no. 2, pp. 283-284, 1978.
CSEE J. Power Energy Syst., vol. 1, no. 4, pp. 38–46, Jan. 2016.
G.U.Yule, “On the Time –Correlation Problem,witj Especial Reference to the Variate-Difference Correlation Method,” J.R.Stat.Soc., vol. 84, no. 4, p. 497, Jul. 1921.
“Economteric Modeler App Overview-MATLAB & Simulink-MathWorks India,”[Online]. Available: http://in.mathworks.com/help/econ/econometric-modeler-overview.html.
S. Atique,S. Noureen,V. Roy,V. Subburaj,S. Bayne,and J. MacFie, “Forecasting of total daily solar energy generation using ARIMA:A case study,”in 2019 IEEE 9th Annual computing and Communication Workshop and Conference, CCWC 2019, 2019, pp. 114–119.
M. Alsharif, M. Younes, and J. Kim, “Time Series ARIMA Model for Prediction of Daily and Monthly Average Global Solar Radiation: The Case Study of Seoul, South Korea,” Symmetry (Basel)., vol. 11, no. 2, p. 240, Feb. 2019.
J. R. Trapero, N. Kourentzes, and A. Martin, “Short-term solar irradiation forecasting based on dynamic harmonic regression,” Energy, vol. 84, pp. 289–295, May 2015.
P. F. Jiménez-Pérez and L. Mora-López, “Modeling and forecasting hourly global solar radiation using clustering and classification techniques,” Sol. Energy, vol. 135, pp. 682–691, Oct. 2016.
J. Shi, W. J. Lee, Y. Liu, Y. Yang, and P. Wang, “Forecasting power output of photovoltaic systems based on weather classification and support vector machines,” in IEEE Transactions on Industry Applications, 2012, vol. 48, no. 3, pp. 1064–1069.
H. S. Jang, K. Y. Bae, H. S. Park, and D. K. Sung, “Solar Power Prediction Based on Satellite Images and Support Vector Machine,” IEEE Trans. Sustain. Energy, vol. 7, no. 3, pp. 1255–1263, Jul. 2016.
V. Eniola, T. Suriwong, C. Sirisamphanwong, and K. Ungchittrakool, “Hour-ahead forecasting of photovoltaic power output based on Hidden Markov Model and Genetic Algorithm,” Int. J. Renew. Energy Res., vol. 9, no. 2, pp. 933–943, 2019.
S. Bhardwaj et al., “Estimation of solar radiation using a combination of Hidden Markov Model and generalized Fuzzy model,” Sol. Energy, vol. 93, pp. 43–54, Jul. 2013.
A. Wibun and P. Chaiwiwatworakul, “An Estimation of Thailand’s Hourly Solar Radiation Using Markov Transition Matrix Method,” Appl. Mech. Mater., vol. 839, pp. 29–33, Jun. 2016.
S. Li, H. Ma, and W. Li, “Typical solar radiation year construction using k-means clustering and discrete-time Markov chain,” Appl. Energy, vol. 205, pp. 720–731, Nov. 2017.
R. A. Verzijlbergh, P. W. Heijnen, S. R. de Roode, A. Los, and H. J. J. Jonker, “Improved model output statistics of numerical weather prediction based irradiance forecasts for solar power applications,” Sol. Energy, vol. 118, pp. 634–645, Aug. 2015.
H. Verbois, R. Huva, A. Rusydi, and W. Walsh, “Solar irradiance forecasting in the tropics using numerical weather prediction and statistical learning,” Sol. Energy, vol. 162, pp. 265–277, Mar. 2018.
K. Bakker, K. Whan, W. Knap, and M. Schmeits, “Comparison of statistical post-processing methods for probabilistic NWP forecasts of solar radiation,” Sol. Energy, vol. 191, pp. 138–150, Oct. 2019.
G. H. Hargreaves and Z. A. Samani, “Estimating Potential Evapotranspiration,” J. Irrig. Drain. Div., vol. 108, no. 3, pp. 225–230, 1982.
E. Quansah et al., “Empirical Models for Estimating Global Solar Radiation over the Ashanti Region of Ghana,” J. Sol. Energy, vol. 2014, pp. 1–6, Jan. 2014.
T. R. Ayodele and A. S. O. Ogunjuyigbe, “Performance assessment of empirical models for prediction of daily and monthly average global solar radiation: the case study of Ibadan, Nigeria,” Int. J. Ambient Energy, vol. 38, no. 8, pp. 803–813, Nov. 2017.
Y. Xie, “Values and limitations of statistical models,” Res. Soc. Stratif. Mobil., vol. 29, no. 3, pp. 343–349, 2011.
K. R. Kumar and M. S. Kalavathi, “Artificial intelligence based forecast models for predicting solar power generation,” in Materials Today: Proceedings, 2018, vol. 5, no. 1, pp. 796–802.
L. M. Aguiar, B. Pereira, P. Lauret, F. Díaz, and M. David, “Combining solar irradiance measurements, satellite-derived data and a numerical weather prediction model to improve intra-day solar forecasting,” Renew. Energy, vol. 97, pp. 599–610, Nov. 2016.
S. Shamshirband et al., “Estimating the diffuse solar radiation using a coupled support vector machine- wavelet transform model,” Renewable and Sustainable Energy Reviews, vol. 56. Elsevier Ltd, pp. 428–435, 01-Apr-2016.
N. Dong, J. F. Chang, A. G. Wu, and Z. K. Gao, “A novel convolutional neural network framework based solar irradiance prediction method,” Int. J. Electr. Power Energy Syst., vol. 114, Jan. 2020.
H. Bouzgou and C. A. Gueymard, “Fast short-term global solar irradiance forecasting with wrapper mutual information,” Renew. Energy, vol. 133, pp. 1055–1065, Apr. 2019.
S. Ghimire, R. C. Deo, N. J. Downs, and N. Raj, “Global solar radiation prediction by ANN integrated with European Centre for medium range weather forecast fields in solar rich cities of Queensland Australia,” J. Clean. Prod., vol. 216, pp. 288–310, Apr. 2019.
C. Huang, L. Wang, and L. L. Lai, “Data-Driven Short-Term Solar Irradiance Forecasting Based on Information of Neighboring Sites,” IEEE Trans. Ind. Electron., vol. 66, no. 12, pp. 9918–9927, Dec. 2019.
X. Huang, J. Shi, B. Gao, Y. Tai, Z. Chen, and J. Zhang, “Forecasting Hourly Solar Irradiance Using Hybrid Wavelet Transformation and Elman Model in Smart Grid,” IEEE Access, vol. 7, pp. 139909–139923, Sep. 2019.
Y. Liu et al., “Ensemble spatiotemporal forecasting of solar irradiation using variational Bayesian convolutional gate recurrent unit network,” Appl. Energy, vol. 253, Nov. 2019.
M. Q. Raza, M. Nadarajah, J. Li, K. Y. Lee, and H. B. Gooi, “An Ensemble Framework For Day-Ahead Forecast of PV Output in Smart Grids,” IEEE Trans. Ind. Informatics, 2018.
L. Benali, G. Notton, A. Fouilloy, C. Voyant, and R. Dizene, “Solar radiation forecasting using artificial neural network and random forest methods: Application to normal beam, horizontal diffuse and global components,” Renew. Energy, vol. 132, pp. 871–884, Mar. 2019.
A. Heydari, D. Astiaso Garcia, F. Keynia, F. Bisegna, and L. De Santoli, “A novel composite neural network based method for wind and solar power forecasting in microgrids,” Appl. Energy, vol. 251, Oct. 2019.
A. Mellit and A. M. Pavan, “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, vol. 84, no. 5, pp. 807–821, May 2010.
S. X. Chen, H. B. Gooi, and M. Q. Wang, “Solar radiation forecast based on fuzzy logic and neural networks,” Renew. Energy, vol. 60, pp. 195–201, Dec. 2013.
H. Lan, C. Zhang, Y. Y. Hong, Y. He, and S. Wen, “Day-ahead spatiotemporal solar irradiation forecasting using frequency-based hybrid principal component analysis and neural network,” Appl. Energy, vol. 247, pp. 389–402, Aug. 2019.
F. Wang, Z. Mi, S. Su, and H. Zhao, “Short-Term Solar Irradiance Forecasting Model Based on Artificial Neural Network Using Statistical Feature Parameters,” Energies, vol. 5, no. 5, pp. 1355–1370, May 2012.
D. Liu and K. Sun, “Random forest solar power forecast based on classification optimization,” Energy, vol. 187, Nov. 2019.
W. Zhang, H. Dang, and R. Simoes, “A new solar power output prediction based on hybrid forecast engine and decomposition model,” ISA Trans., vol. 81, pp. 105–120, Oct. 2018.
Y. Dong and H. Jiang, “Global Solar Radiation Forecasting Using Square Root Regularization-Based Ensemble,” Math. Probl. Eng., vol. 2019, 2019.
James Mubiru “Using Artificial Neural Networks to predict direct solar irradiation,” Hindawai Publishing Corporation., Volume 2011, Article ID 142054, 6 pages.
A. T. Eseye, J. Zhang, and D. Zheng, “Short-term photovoltaic solar power forecasting using a hybrid wavelet-PSO-SVM model based on SCADA and Meteorlogical information,” Renew. Energy, vol. 118, pp. 357-367, Apr. 2018.
W. VanDeventer et al., “Short-term PV power forecasting using hybrid GASVM technique,” Renew. Energy, vol. 140, pp. 367–379, Sep. 2019.
A. Shadab, S. Said, and S. Ahmad, “Box-Jenkins multiplicative ARIMA modeling for prediction of solar radiation: a case study,” Int. J. Energy Water Resour., Sep. 2019.
J. Dong et al., “Novel stochastic methods to predict short-term solar radiation and photovoltaic power,” Rener. Energy, vol. 145, pp. 333–346, Jan. 2020.
Sanjari et al., “Probabilistic forecast of PV power generation based on higher order markov chain”, IEEE Access, vol. 32, pp. 2942–2952, July 2017.
S. Monjoly, M. Andre, R. Calif, and T. Soubdhan, “Hourly forecasting of global solar radiation based on multiscale decomposition methods: A hybrid approach,” Energy, vol. 119, pp. 228–298, 2017.
M. Paulescu and E. Paulscu, “Short term forecasting of solar irradiance,” Renew. Energy, vol. 143, pp. 985–994, Dec. 2019.
C. Voyant and G. Notton, “Solar irradiation now casting by stochastic persistence:A new parsimonious simple and efficient forecasting tool, Renew. Sustain Energy Rev., vol. 92, 343–352, Sep. 2018.
F. Jiang et al., “Artificial intelligence in healthcare: past, present and future, “Stroke vasa.Neural., vol. 2, no. 4, pp. 230–243, 2017.
H. Bowang, J. Ning Xiong, & C. Yinzhao, “The mid-term forecast method of solar radiation index,” Chinese Astron. Astrophys., vol. 39, no. 2, pp. 198–211, Apr. 2015.
D. Yang, “A Guideline to solar forecasting research practice: Reproducible, operational, probabilistic or physically based, ensemble and skills (ROPES),” Journal of Renewable and Sustainable Energy, vol. 11, no. 2. American Institute of physics Inc., 01-Mar-2019.
R. J. Hyndman and A. B. Koehler, “Another look at measure of forecast accuracy,” Int. J. Forecast., vol. 22, no. 4, pp. 679–688, Oct. 2006.
J. Zhang, B. M. Hodge, A. Florita, S. Lu, H. F. Hamann, and V. Banunarayanan, “Metrics for evaluating the accuracy of solar power forecasting,” in 3rd International Workshop on Integrations of Solar Power into Power Systems, 2013, vol. no. 17436, October, pp. 1–10.
C. J. Willmott and K. Matsuura, “Advantages of the mean absolute error (MAE) over the root mean Square error (RMSE) in assessing average model performance,” Clim.Res., vol. 30, no. 1, pp. 79–82, 2005.
Mills. A., and R. Wiser. (2010), “Implications of Wide-Area Geographic Diversity for Short-Term Variability of Solar Power”, Report on Environmental Energy Technologies Division, Ernest Orlando Lawrence Berkeley National Laboratory.
A. Florita, B. M. Hodge, and K. Orwig, “Identifying wind and solar ramping events,” in IEEE Green Technologies Conference, 2013, no. January, pp. 147–152.
Y. Chu, H. T. C. Pedro, M. Li, and C. F. M. Coimbra, “Real-time forecasting of solar irradiance ramps with smart image processing,” Sol. Energy. vol. 114, pp. 91–104, Apr. 2015.
P. Lauret, C. Voyant, T. Soubdhan, M. David, and P. Poggi, “A benchmarking of machine learning techniques for solar radiation forecasting in an insular context,” Sol. Energy, vol. 112, pp. 446–457.
C. J. Wilmot and K. Matsuura, “Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance,” Clim. Res., vol. 30, no. 1, pp. 79–82, 2005.
C. Feng, M. Cui, B. Hodge, S. Lu, H. Hamann, and J. Zhang, “Unsupervised Clustering-Based Short-Term Solar Forecasting,” IEEE Trans. Sustain. Energy, 2018.
N. Basurto, A. Arroyo, R. Vega, H. Quintian, J.L. Calvo-Rolle, and A. Herrero, “A Hybrid Intelligent System to forecast solar energy production,” Comput. Electr. Eng., vol. 78, pp. 373–387, Sep. 2019.
H. Zhou, Y. Zhang, L. Yang, Q. Liu, K. Yan, and Y. Du, “Short-Term photovoltaic power forecasting based on long short term memory neural network and attention mechanisum,” IEEE Access, vol. 7, pp. 78063–78074, 2019.
V. Kushwaha and N.M. Pindoriya , “A SARIMA-RVFL hybrid model assisted by wavelet decomposition for very short-term solar PV power generation forecast,” Renew. Energy, vol. 140, pp. 124–139, Sep. 2019.
Y. Liu, Y. Zhou, Y. Chen, D. Wang, Y. Wang, and Y. Zhu, “Comparison of support vector machine and copula-based non-linear quantile regression for estimating the daily diffuse solar radiation: A case study in China,” Renew. Energy, vol. 146, pp. 1101–1112, Feb. 2020.
Fonseca JGDS, Oozeki T, Takashima T, Koshimizo G, Uchida Y, Ogimoto K, Photovoltaic power production forecast with support vector regression: a study on the forecast horizon. In: 2011 37th IEEE photovoltaic specialists conference: 2011, pp. 2579–2583.
Montteitro C, et al., Short-term forecasting models for photovoltaic plants: analytical versus soft-computing techniques.2013. In: Mathematical problems in engineering. Vol. 9; 2013. Art. No. 767284.
Mohammadi, K. Shamshirband, S. Tong, C. W., Arif, M., Petkovic, D., & Ch, S. A new hybrid support vector machine-wavelet transform approach for estimation of horizontal global solar radiation. Energy Conversion and Management, 92, 162–171. http://doi.org/10.1016/j.enconman.2014.12.050
Prasad, R., Ali, M., Kwan, P., & Khan, H. Designing a multistage multivariate empirical model decomposition coupled with ant colony optimization and random forecast model to forecast monthly solar radiation. Applied energy, 236, 778–792. http://doi.org/10.1016/j.apenergy.2018.12.034.
Diagne, M., David, M., Boland, J., Schmutz, N., & Lauret, P.(2014). Post-processing of solar irradiance forecasts from WRF model at Reunion Island. Solar Energy, 105, 99–108. https://doi.org/10.1016/j.solener.2014.03.016.
Hussain, S., & AlAlili, A. Online sequential Learning of Neural Network in Solar Radiation Modeling Using Hybrid Bayesian Hierarchical Approach. Journal of Solar Energy Engineering, 138(6), 061012. https://doi.org/10.1115/1.4034907.
Che, Y., Chen, L., Zheng, J., Yuan, L., & Xiao, F. A Novel Hybrid Model of WRF and Clearness Index-Based Kalman Filter for Day-Ahead Solar Radiation Forecasting. Applied Sciences, 9(19), 3967. http://doi.org/10.3390/app9193967.
Dong, Z., Yang, D., Reindl, T., & Walsh, W.M. A novel hybrid approach based on self-organizing maps, support vector regression and particle swarm optimization to forecast solar irradiance. Energy, 82, 570–577. http://doi.org/10.1016/j.energy.2015.01.066
Hady, M. F. A., Schwenker, F., & Palm, G. Semi-supervised learning for tree-structured ensemble of RBF networks with co-training. Neural Network, 23(4), 497–509. http://doi.org/10.1016/j.neunet.2009.09.001
Wu, Y., & Wang, J. A novel hybrid model based on artificial neural network for solar radiation prediction. Renewable Energy, 89, 268–284. https://doi.org/10.1016/j.renene.2015.11.070.
Alomari MH, Youuis Ola, Hayainesh SMA: A predictive mode for solar photovoltaic power using the levenberg-marquardt and Bayesian regularization algorithms and real time weather data. Int.J. Adv Comput Sci Appl 2018; 9. Art no. 1.
Reikard G. Predicting solar radiation at high resolution: a comparison of time series forecast. Sol Energy 2009; 83(3):3-12-9.
Ibrahim, I.A., & Khatib, T. A novel hybrid model for hourly global solar radiation prediction using random forests techniques and firefly algorithm. Energy Conversion and Management, 138, 413–425. http://doi.org/10.1016/j.enconman.2017.02.006
Halabi, L.M., Mekhilef, S., & Hossain, M. Performance evaluation of hybrid adaptive neuro-fuzzy inference system models for predicting monthly global solar radiation. Applied energy, 213, 247–261. http://doi.org/10.1016/j.apenergy.2018.01.035
Jovanovic, R., Pomares, L.M., Mohieldeen, Y.E., Perez-Astudillo, D., & Bachour, D. An evolutionary method for creating ensembles with adaptive size neural networks for predicting hourly solar irradiance. In 2017 International Joint Conference on Neural Networks(IJCNN) (pp. 1962–1967).IEEE. DOI: 10.1109/IJCNN.2017.796609
K. Benmouiza and A. Cheknane, “Forecasting hourly global solar radiation using hybrid k-means and nonlinear autoregressive neural network models,” Energy Convers. Manag., vol. 75, pp. 561–569, Nov. 2013.
K. Wang, X. Qi, and H. Liu, “A comparison of day-ahead photovoltaic power forecasting models based on deep learning neural network,” Appl. Energy, vol. 251, Oct. 201934