Solar Irradiation Forecasting Technologies: A Review
DOI:
https://doi.org/10.13052/spee1048-4236.391413Keywords:
Forecasting techniques, hybrid models, neural network, solar forecasting, error metrics, SVM, Markov chain, numerical weather prediction.Abstract
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, optimiza-
tion & 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.
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References
S. Sun, S. Wang, G. Zhang, and J. Zheng, “A decomposition-
clustering-ensemble learning approach for solar radiation forecast-
ing,” 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.
A. Gupta et al.
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 Mar-
kets – 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,”
IRENA, Rrnewable capacity statistics 2010, International Renewable
Energy Agency (IRENA), Abu Dhabi. 2019.
“Annual Report, Ministry of New and Renewable Energy, Govern-
ment of India,” 12–13, 2017.
R. K. Singh, “India’s renewable energy capacity crosses 80GW-mark,”
The Economic Times, 2016. [online]. Available: https://economictime
s.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 pho-
tovoltaic power plant in Kitakyushu, Japan,” Prog. Photovoltaics Res.
Appl., vol. 20, no. 7, pp. 874– 882, Nov. 2012.
Solar Irradiation Forecasting Technologies: A Review 343
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, “Com-
parison 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.
M. A. Behrang, E. Assareh, A. Ghanbarzadeh, and A. R. Noghre-
habadi, “The potential of different artificial neural network (ANN)
techniques in daily global solar radiation modeling based on mete-
orological data,” Sol. Energy, vol. 84, no. 8, pp. 1468–1480, Aug.
A. Fouilloy et al., “Solar irradiation prediction with machine learn-
ing: Forecasting models selection method depending on weather
variability,” Energy, vol. 165, pp. 620–629, Dec. 2018.
L. Mazorra-Aguiar and F. D ́ıaz, “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.
A. Gupta et al.
G. Notton, C. Voyant, A. Fouilloy, J. L. Duchaud, and M. L. Nivet,
“Some applications of ANN to solar radiation estimation and fore-
casting for energy applications,” Appl. Sci., vol. 9, no. 1, pp. 1–21,
F. Rodr ́ıguez, A. Fleetwood, A. Galarza, and L. Font ́an, “Predict-
ing 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–
, 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, “Eval-
uating 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 dif-
fuse 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 Refer-
ence 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/h
elp/econ/econometric-modeler-overview.html.
Solar Irradiation Forecasting Technologies: A Review 345
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 irra-
diation forecasting based on dynamic harmonic regression,” Energy,
vol. 84, pp. 289–295, May 2015.
P. F. Jim ́enez-P ́erez and L. Mora-L ́opez, “Modeling and forecast-
ing 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 Hid-
den 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.
A. Gupta et al.
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 Evapo-
transpiration,” J. Irrig. Drain. Div., vol. 108, no. 3, pp. 225–230,
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. D ́ıaz, and M. David, “Combin-
ing solar irradiance measurements, satellite-derived data and a numer-
ical 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,” Renew-
able and Sustainable Energy Reviews, vol. 56. Elsevier Ltd, pp. 428–
, 01-Apr-2016.
N. Dong, J. F. Chang, A. G. Wu, and Z. K. Gao, “A novel convo-
lutional 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 irra-
diance 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 radia-
tion 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.
Solar Irradiation Forecasting Technologies: A Review 347
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–
, 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 spa-
tiotemporal 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 Statis-
tical Feature Parameters,” Energies, vol. 5, no. 5, pp. 1355–1370, May
D. Liu and K. Sun, “Random forest solar power forecast based on
classification optimization,” Energy, vol. 187, Nov. 2019.
A. Gupta et al.
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–
, 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 irradi-
ance,” 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.
Solar Irradiation Forecasting Technologies: A Review 349
D. Yang, “A Guideline to solar forecasting research practice: Repro-
ducible, 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,
Mills. A., and R. Wiser. (2010), “Implications of Wide-Area Geo-
graphic 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 bench-
marking 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, “Unsu-
pervised 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
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