Skip to main content
SpringerLink
Log in

Estimation of solar radiation using neighboring stations through hybrid support vector regression boosted by Krill Herd algorithm

  • Original Paper
  • Published:
Arabian Journal of Geosciences Aims and scope Submit manuscript

Abstract

Solar radiation is a basic input in many fields of studies and models. However, the low density of solar network stations; the improper distribution of these stations; high cost of purchasing, maintaining, and calibrating solar radiation measurement instruments; and frequent errors in the available data are the most important deficiencies in this regard. Thus, researchers are seeking for new and practical methods to estimate solar radiation accurately. The present study aimed to estimate the solar radiation values based on a new hybrid support vector regression model. To this aim, the solar radiation values of all eight target synoptic stations during 1974–2014 were estimated by using Krill-Herd hybrid algorithm (SVR-KHA) method based on support vector regression and implementing neighboring station data. Results indicated that the testing performance of SVR-KHA has a more precision and lower error for all target stations, compared with classical SVR. In addition, the best results were obtained for SVR-KHA3 hybrid model (Isfahan station). Further, the RMSE, MAPE, and R2 values for this model were 1.98 MJ/m2/day, 7.4%, and 0.93, respectively. In accordance with the results, Krill-Herd algorithm method coupled with support vector regression had a high performance and capability for solar radiation estimation in Iran. In other words, the hybrid SVR-KHA model is more flexible and has less error in modeling the nonlinear and complex systems. Finally, the new method of using neighboring stations can be regarded as an appropriate method for estimating nonlinear phenomenon such as solar radiation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Abualigah LM, Khader AT, Hanandeh ES, Gandomi AH (2017) A novel hybridization strategy for krill herd algorithm applied to clustering techniques. Appl Soft Comput 60:423–435

    Article  Google Scholar 

  • Achour L, Bouharkat M, Assas O, Behar O (2017) Hybrid model for estimating monthly global solar radiation for the Southern of Algeria: (case study: Tamanrasset, Algeria). Energy 135:526–539

    Article  Google Scholar 

  • Ahmed F, Ulfat I (2004) Empirical models for the correlation of monthly average daily global solar radiation with hours of sunshine on a horizontal surface at Karachi, Pakistan. Turk J Phys 28(5):301–307

    Google Scholar 

  • Akpabio LE, Udo SO, Etuk SE (2004) Empirical correlations of global solar radiation with meteorological data for Onne, Nigeria. Turk J Phys 28(3):205–212

    Google Scholar 

  • Aladenola OO, Madramootoo CA (2014) Evaluation of solar radiation estimation methods for reference evapotranspiration estimation in Canada. Theor Appl Climatol 118(3):377–385

    Article  Google Scholar 

  • Al-Alawi SM, Al-Hinai HA (1998) An ANN-based approach for predicting global radiation in locations with no direct measurement instrumentation. Renew Energy 14(1-4):199–204

    Article  Google Scholar 

  • Alizamir M, Kim S, Kisi O, Zounemat-kermani M (2020) A comparative study of several machine learning based non-linear regression methods in estimating solar radiation: case studies of the USA and Turkey regions. Energy (in press) 197:117239

    Article  Google Scholar 

  • Alsina EF, Bortolini M, Gamberi M, Regattieri A (2016) Artificial neural network optimisation for monthly average daily global solar radiation prediction. Energy Convers Manag 120:320–329

    Article  Google Scholar 

  • Angstrom A (1924) Solar and terrestrial radiation. Report to the international commission for solar research on actinometric investigations of solar and atmospheric radiation. Q J R Meteorol Soc 50(210):121–126

    Article  Google Scholar 

  • Antonanzas J, Urraca R, Martinez-de-Pison FJ, Antonanzas-Torres F (2015) Solar irradiation mapping with exogenous data from support vector regression machines estimations. Energy Convers Manag 100:380–390

    Article  Google Scholar 

  • Ayodele TR, Ogunjuyigbe ASO, Amedu A, Munda JL (2019) Prediction of global solar irradiation using hybridized k-means and support vector regression algorithms. Renewable Energy Focus 29:78–93

    Article  Google Scholar 

  • Baziar A, Kavousi-Fard A (2015) Short term load forecasting using a hybrid model based on support vector regression. Int J Sci Technol Res 4(5):189–195

    Google Scholar 

  • Belaid S, Mellit A (2016) Prediction of daily and mean monthly global solar radiation using support vector machine in an arid climate. Energy Convers Manag 118:105–118

    Article  Google Scholar 

  • Benajes J, Martín J, García A, Villalta D, Warey A (2015) In-cylinder soot radiation heat transfer in direct-injection diesel engines. Energy Convers Manag 106:414–427

    Article  Google Scholar 

  • Bristow KL, Campbell GS (1984) On the relationship between incoming solar radiation and daily maximum and minimum temperature. Agric For Meteorol 31(2):159–166

    Article  Google Scholar 

  • Burari FW, Sambo AS (2001) Model for the prediction of global solar radiation for Bauchi using meteorological data. Nigeria J Renew Energy 91:30–33

    Google Scholar 

  • Chen JL, Li GS, Xiao BB, Wen ZF, Lv MQ, Chen CD, Jiang Y, Wang XX, Wu SJ (2015) Assessing the transferability of support vector machine model for estimation of global solar radiation from air temperature. Energy Convers Manag 89:318–329

    Article  Google Scholar 

  • Chen X, Xia XL, Liu H, Li Y, Liu B (2016) Heat transfer analysis of a volumetric solar receiver by coupling the solar radiation transport and internal heat transfer. Energy Convers Manag 114:20–27

    Article  Google Scholar 

  • Cheng K, Lu Z (2018) Adaptive sparse polynomial chaos expansions for global sensitivity analysis based on support vector regression. Comput Struct 194:86–96

    Article  Google Scholar 

  • Cheng HY, Yu CC, Lin SJ (2014) Bi-model short-term solar irradiance prediction using support vector regressors. Energy 70:121–127

    Article  Google Scholar 

  • Demirhan H, Menteş T, Atilla M (2013) Statistical comparison of global solar radiation estimation models over Turkey. Energy Convers Manag 68:141–148

    Article  Google Scholar 

  • Deo RC, Kisi O, Singh VP (2017) Drought forecasting in eastern Australia using multivariate adaptive regression spline, least square support vector machine and M5Tree model. Atmos Res 184:149–175

    Article  Google Scholar 

  • Deo RC, Şahin M (2017) Forecasting long-term global solar radiation with an ANN algorithm coupled with satellite-derived (MODIS) land surface temperature (LST) for regional locations in Queensland. Renew Sust Energ Rev 72:828–848

    Article  Google Scholar 

  • Dubayah R (1992) Estimating net solar radiation using Landsat Thematic Mapper and digital elevation data. Water Resour Res 28(9):2469–2484

    Article  Google Scholar 

  • El Mghouchi Y, Chham E, Krikiz MS, Ajzoul T, El Bouardi A (2016) On the prediction of the daily global solar radiation intensity on south-facing plane surfaces inclined at varying angles. Energy Convers Manag 120:397–411

    Article  Google Scholar 

  • Feng Y, Wang GG, Deb S, Lu M, Zhao XJ (2017) Solving 0–1 knapsack problem by a novel binary monarch butterfly optimization. Neural Comput & Applic 28(7):1619–1634

    Article  Google Scholar 

  • Gandomi AH, Alavi AH (2012) Krill herd: a new bio-inspired optimization algorithm. Commun Nonlinear Sci Numer Simul 17(12):4831–4845

    Article  Google Scholar 

  • Gandomi AH, Yang XS, Talatahari S, Alavi AH (2013) Metaheuristic algorithms in modeling and optimization. In Metaheuristic applications in structures and infrastructures 1-24.

  • Gairaa K, Khellaf A, Messlem Y, Chellali F (2016) Estimation of the daily global solar radiation based on Box–Jenkins and ANN models: a combined approach. Renew Sust Energ Rev 57:238–249

    Article  Google Scholar 

  • Gizaw MS, Gan TY (2016) Regional flood frequency analysis using support vector regression under historical and future climate. J Hydrol 538:387–398

    Article  Google Scholar 

  • Guo L, Wang GG, Gandomi AH, Alavi AH, Duan H (2014) A new improved krill herd algorithm for global numerical optimization. Neurocomputing 138:392–402

    Article  Google Scholar 

  • Hay JE (1979) Calculation of monthly mean solar radiation for horizontal and inclined surfaces. Sol Energy 23(4):301–307

    Article  Google Scholar 

  • Hontoria L, Aguilera J, Zufiria P (2002) Generation of hourly irradiation synthetic series using the neural network multilayer perceptron. Sol Energy 72(5):441–446

    Article  Google Scholar 

  • Hosseini SM, Mahjouri N (2016) Integrating support vector regression and a geomorphologic artificial neural network for daily rainfall-runoff modeling. Appl Soft Comput 38:329–345

    Article  Google Scholar 

  • Ibrahim IA, Khatib T (2017) A novel hybrid model for hourly global solar radiation prediction using random forests technique and firefly algorithm. Energy Convers Manag 138:413–425

    Article  Google Scholar 

  • Jahani B, Dinpashoh Y, Nafchi AR (2017) Evaluation and development of empirical models for estimating daily solar radiation. Renew Sust Energ Rev 73:878–891

    Article  Google Scholar 

  • Jahani B, Mohammadi B (2018) A comparison between the application of empirical and ANN methods for estimation of daily global solar radiation in Iran. Theor Appl Climatol 137(1-2):1257–1269

    Article  Google Scholar 

  • Jiang Y (2009) Computation of monthly mean daily global solar radiation in China using artificial neural networks and comparison with other empirical models. Energy 34(9):1276–1283

    Article  Google Scholar 

  • Khosravi A, Koury RN, Machado L, Pabon JJ (2018) Prediction of hourly solar radiation in Abu Musa Island using machine learning algorithms. J Clean Prod 176:63–75

    Article  Google Scholar 

  • Kosmopoulos PG, Kazadzis S, Lagouvardos K, Kotroni V, Bais A (2015) Solar energy prediction and verification using operational model forecasts and ground-based solar measurements. Energy 93:1918–1930

    Article  Google Scholar 

  • Landeras G, López JJ, Kisi O, Shiri J (2012) Comparison of Gene Expression Programming with neuro-fuzzy and neural network computing techniques in estimating daily incoming solar radiation in the Basque Country (Northern Spain). Energy Convers Manag 62:1–3

    Article  Google Scholar 

  • Lam JC, Wan KK, Yang L (2008) Solar radiation modelling using ANNs for different climates in China. Energy Convers Manag 49(5):1080–1090

    Article  Google Scholar 

  • Li H, Ma W, Lian Y, Wang X, Zhao L (2011) Global solar radiation estimation with sunshine duration in Tibet, China. Renew Energy 36(11):3141–3145

    Article  Google Scholar 

  • Liu X, Mei X, Li Y, Porter JR, Wang Q, Zhang Y (2010) Choice of the Ångström–Prescott coefficients: are time-dependent ones better than fixed ones in modeling global solar irradiance? Energy Convers Manag 51(12):2565–2574

    Article  Google Scholar 

  • Liu Y, Zhou Y, Wang D, Wang Y, Li Y, Zhu Y (2017) Classification of solar radiation zones and general models for estimating the daily global solar radiation on horizontal surfaces in China. Energy Convers Manag 154:168–179

    Article  Google Scholar 

  • Martí P, Gasque M (2011) Improvement of temperature-based ANN models for solar radiation estimation through exogenous data assistance. Energy Convers Manag 52(2):990–1003

    Article  Google Scholar 

  • Medeiros FJ, Silva CM, Bezerra BG (2017) Calibration of Ångström-Prescott equation to estimate daily solar radiation on Rio Grande do Norte State, Brazil. Revista Brasileira de Meteorologia 32(3):409–416

    Article  Google Scholar 

  • Moazenzadeh R, Mohammadi B (2019) Assessment of bio-inspired metaheuristic optimisation algorithms for estimating soil temperature. Geoderma 353:152–171

    Article  Google Scholar 

  • Moazenzadeh R, Mohammadi B, Shamshirband S, Chau KW (2018) Coupling a firefly algorithm with support vector regression to predict evaporation in northern Iran. Eng Appl Comp Fluid Mech 12(1):584–597

    Google Scholar 

  • Mohammadi B (2019a) Letter to the editor “Estimation of sodium adsorption ratio indicator using data mining methods: a case study in Urmia Lake basin, Iran” by Mohammad Taghi Sattari, Arya Farkhondeh, and John Patrick Abraham. Environ Sci Pollut Res 26(10):10439–10440

    Article  Google Scholar 

  • Mohammadi B (2019b) Letter to the editor “Predicting total phosphorus levels as indicators for shallow lake management”. Ecol Indic 107:105664

    Article  Google Scholar 

  • Mohammadi B (2019c) “Prediction of effective climate change indicators using statistical downscaling approach and impact assessment on pearl millet (Pennisetum glaucum L.) yield through genetic algorithm in Punjab, Pakistan” by Asmat Ullah, Nasrin Salehnia, Sohrab Kolsoumi, Ashfaq Ahmad, Tasneem Khaliq. Ecol Indic 101:973–974

    Article  Google Scholar 

  • Mohammadi B (2019d) Letter to the editor “Design of an integrated climatic assessment indicator (ICAI) for wheat production: a case study in Jiangsu Province, China” by Xiangying Xu, Ping Gao, Xinkai Zhu, Wenshan Guo, Jinfeng Ding, Chunyan Li, Min Zhu, Xuanwei Wu. Ecol Indic 103:493

    Article  Google Scholar 

  • Olatomiwa L, Mekhilef S, Shamshirband S, Mohammadi K, Petković D, Sudheer C (2015) A support vector machine–firefly algorithm-based model for global solar radiation prediction. Sol Energy 115:632–644

    Article  Google Scholar 

  • Pai PF, Hong WC (2007) A recurrent support vector regression model in rainfall forecasting. Hydrol Process 21(6):819–827

    Article  Google Scholar 

  • Pinker RT, Frouin R, Li Z (1995) A review of satellite methods to derive surface shortwave irradiance. Remote Sens Environ 51(1):108–124

    Article  Google Scholar 

  • Prescott JA (1940) Evaporation from a water surface in relation to solar radiation. Trans R Soc S Aust 64(1):114–118

    Google Scholar 

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

    Article  Google Scholar 

  • Quej VH, Almorox J, Arnaldo JA, Saito L (2017) ANFIS, SVM and ANN soft-computing techniques to estimate daily global solar radiation in a warm sub-humid environment. J Atmos Sol Terr Phys 155:62–70

    Article  Google Scholar 

  • Ramli MA, Twaha S, Al-Turki YA (2015) Investigating the performance of support vector machine and artificial neural networks in predicting solar radiation on a tilted surface: Saudi Arabia case study. Energy Convers Manag 105:442–452

    Article  Google Scholar 

  • Renno C, Petito F, Gatto A (2015) Artificial neural network models for predicting the solar radiation as input of a concentrating photovoltaic system. Energy Convers Manag 106:999–1012

    Article  Google Scholar 

  • Samadianfard S, Ghorbani MA, Mohammadi B (2018) Forecasting soil temperature at multiple-depth with a hybrid artificial neural network model coupled-hybrid firefly optimizer algorithm. Inform Process Agric 5(4):465–476

    Google Scholar 

  • Salcedo-Sanz S, Casanova-Mateo C, Pastor-Sánchez A, Sánchez-Girón M (2014) Daily global solar radiation prediction based on a hybrid Coral Reefs Optimization–Extreme Learning Machine approach. Sol Energy 105:91–98

    Article  Google Scholar 

  • Shamshirband S, Mohammadi K, Tong CW, Zamani M, Motamedi S, Ch S (2016) A hybrid SVM-FFA method for prediction of monthly mean global solar radiation. Theor Appl Climatol 125(1-2):53–65

    Article  Google Scholar 

  • Sharifi SS, Rezaverdinejad V, Nourani V (2016) Estimation of daily global solar radiation using wavelet regression, ANN, GEP and empirical models: a comparative study of selected temperature-based approaches. J Atmos Sol Terr Phys 149:131–145

    Article  Google Scholar 

  • Tymvios FS, Jacovides CP, Michaelides SC, Scouteli C (2005) Comparative study of Ångström’s and artificial neural networks’ methodologies in estimating global solar radiation. Sol Energy 78(6):752–762

    Article  Google Scholar 

  • Vaheddoost B, Guan Y, Mohammadi B (2020) Application of hybrid ANN-whale optimization model in evaluation of the field capacity and the permanent wilting point of the soils. Environ Sci Pollut Res 27:1–11. https://doi.org/10.1007/s11356-020-07868-4

    Article  Google Scholar 

  • Vapnik VN, Chervonenkis AJ (1974) Theory of pattern recognition. Nauka, Moscow

    Google Scholar 

  • Wang G, Gandomi AH, Alavi AH, Hao GS (2014a) Hybrid krill herd algorithm with differential evolution for global numerical optimization. Neural Comput & Applic 25(2):297–308

    Article  Google Scholar 

  • Wang G, Guo L, Wang H, Duan H, Liu L, Li J (2014b) Incorporating mutation scheme into krill herd algorithm for global numerical optimization. Neural Comput & Applic 24(3-4):853–871

    Article  Google Scholar 

  • Yao W, Zhang C, Wang X, Zhang Z, Li X, Di H (2018b) A new correlation between global solar radiation and the quality of sunshine duration in China. Energy Convers Manag 164:579–587

    Article  Google Scholar 

  • Yao W, Zhang C, Hao H, Wang X, Li X (2018a) A support vector machine approach to estimate global solar radiation with the influence of fog and haze. Renew Energy 128:155–162

    Article  Google Scholar 

  • Yu X, Zhang X, Qin H (2018) A data-driven model based on Fourier transform and support vector regression for monthly reservoir inflow forecasting. J Hydro Environ Res 18:12–24

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Hydrology and Water Resources, Hohai University, Nanjing, 210098, China

    Babak Mohammadi

  2. Irrigation and Reclamation Engineering Department, University College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran

    Zahra Aghashariatmadari

Authors
  1. Babak Mohammadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zahra Aghashariatmadari
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zahra Aghashariatmadari.

Additional information

Responsible Editor: Zhihua Zhang

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mohammadi, B., Aghashariatmadari, Z. Estimation of solar radiation using neighboring stations through hybrid support vector regression boosted by Krill Herd algorithm. Arab J Geosci 13, 363 (2020). https://doi.org/10.1007/s12517-020-05355-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12517-020-05355-1

Keywords

Search

Navigation