Skip to main content

Advertisement

Springer Nature Link
Log in

Hybrid neurofuzzy investigation of short-term variability of wind resource in site suitability analysis: a case study in South Africa

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Energy generation from wind resources is now a mature technology with the ability to compete with traditional energy sources at utility scales in many countries, through the identification of suitable sites. However, beyond site suitability, predicting the wind resource variability of the potentially viable site presents overarching benefits in strategic and operational planning prior to site development. This study, therefore, combines geographical information systems multicriteria decision-making (GIS-MCDM) and hybrid neurofuzzy modeling tools for site suitability and resource variability forecast, respectively, in the Eastern Cape Province of South Africa. The GIS model uses two factors (climatological and environmental), and analytical hierarchical process was used for evaluating criteria degree of influence. Wind resource variability using diurnal satellite-based data for the candidate site was used on the models. Adaptive neurofuzzy inference system models hybrid with genetic algorithm (GA-ANFIS) and particle swarm optimization (PSO-ANFIS) were compared with standalone ANFIS and Levenberg–Marquardt backpropagation neural network (LMBP-ANN) using six statistical measures of error, accuracy, and variability. The GA-ANFIS and PSO-ANFIS accurately model the resource with PSO-ANFIS having lesser computational time compared to GA-ANFIS. However, LMBP-ANN is most robust and resistant in modeling the resource variability among the four models. Hence, wind resource variability investigation on a potentially viable site obtained from the GIS-MCDM model can complement on-site investigations prior to site development. Also, tuning ANFIS with evolutionary algorithms offers improved accuracy over standalone ANFIS model for wind resource forecast and further its robustness in predicting variability of the resource. From our findings, cross-boundary wind resource exploration between South Africa and Lesotho could foster regional interconnectivity.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 10
Fig. 11

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Abbreviations

AAPRE:

Average absolute percentage relative error

AHP:

Analytical hierarchical process

ANFIS:

Adaptive neurofuzzy inference system

ANN:

Artificial neural network

ARMA:

Autoregressive moving average

CT:

Computational time

FCM:

Fuzzy c-means

FIS:

Fuzzy inference system

GA:

Genetic algorithm

GIS:

Geographical information system

GMDH:

Group method of data handling

IBA:

Important bird areas

LDA:

Linear discriminant analysis

LMBP:

Levenberg–Marquardt backpropagation

MABAC:

Multi-attributive border approximation area comparison

MAD:

Mean absolute deviation

MAPE:

Mean absolute percentage error

MCDM:

Multicriteria decision-making

MLFFNN:

Multi-linear feedforward neural network

MSE:

Mean square error

NARX:

Nonlinear autoregressive with exogenous input

NASA:

National Aeronautic and Space Administration

NWP:

Numerical Weather Prediction

PCA:

Principal component analysis

PSO:

Particle swarm optimization

RCoV:

Robust coefficient of variation

rMBE:

Relative mean bias error

RMSE:

Root mean square error

SVR:

Support vector regression

TOPSIS:

Technique for order processing by similarity to ideal solutions

VAF:

Variance accounted for

VIKOR:

VIseKriterijumska Optimizacija I KompromisnoResenje

WLC:

Weighted linear combinations

References

  1. Tejeda J, Ferreira S (2014) Applying systems thinking to analyze wind energy sustainability. Proced Comput Sci 28:213–220. https://doi.org/10.1016/j.procs.2014.03.027

    Article  Google Scholar 

  2. Anoune K, Bouya M, Astito A, Abdellah AB (2018) Sizing methods and optimization techniques for PV-wind based hybrid renewable energy system: a review. Renew Sustain Energy Rev. 93:652–673. https://doi.org/10.1016/j.rser.2018.05.032

    Article  Google Scholar 

  3. SANEDI (2017) Appraisal of implementation of fossil fuel and renewable energy hybrid technologies in South Africa

  4. AWEA Environmental Benefits. Am Wind Energy Assoc 2020. https://www.awea.org/wind-101/benefits-of-wind/environmental-benefits (accessed August 17, 2020)

  5. Gibson L, Wilman EN, Laurance WF (2017) How green is ‘green’ energy? Trends Ecol Evol 32:922–935. https://doi.org/10.1016/j.tree.2017.09.007

    Article  Google Scholar 

  6. Baseer MA, Rehman S, Meyer JP, Alam M (2017) GIS-based site suitability analysis for wind farm development in Saudi Arabia. Energy 141:1166–1176. https://doi.org/10.1016/j.energy.2017.10.016

    Article  Google Scholar 

  7. Malczewski J (2004) GIS-based land-use suitability analysis: a critical overview. Prog Plann 62:3–65. https://doi.org/10.1016/j.progress.2003.09.002

    Article  Google Scholar 

  8. Mirhosseini M, Sharifi F, Sedaghat A (2011) Assessing the wind energy potential location in province of Semnan in Iran. Renew Sustain Energy Rev 15:449–459

    Article  Google Scholar 

  9. Schardinger I, Botzenhart F, Biberacher M, Hamacher T, Blaschke T (2012) Integrating spatial models into regional energy system optimisation: focusing on biomass. Int J Energy Sect Manag 6:5–32. https://doi.org/10.1108/17506221211216517

    Article  Google Scholar 

  10. Grassi S, Junghans S, Raubal M (2013) Estimating mean annual energy production of clustered wind turbines with GIS. Proc. 2013 Int. Conf. Appl. Energy, Pretoria. South Africa

  11. Noorollahi Y, Yousefi H, Mohammadi M (2016) Multi-criteria decision support system for wind farm site selection using GIS. Sustain Energy Technol Assess 13:38–50. https://doi.org/10.1016/j.seta.2015.11.007

    Article  Google Scholar 

  12. Villacreses G, Gaona G, Martínez-Gómez J, Jijón DJ (2017) Wind farms suitability location using geographical information system (GIS), based on multi-criteria decision making (MCDM) methods: the case of continental Ecuador. Renew Energy 109:275–286. https://doi.org/10.1016/j.renene.2017.03.041

    Article  Google Scholar 

  13. Suganthi L, Iniyan S, Samuel AA (2015) Applications of fuzzy logic in renewable energy systems - a review. Renew Sustain Energy Rev 48:585–607. https://doi.org/10.1016/j.rser.2015.04.037

    Article  Google Scholar 

  14. Al Garni HZ, Awasthi A (2017) Solar PV power plant site selection using a GIS-AHP based approach with application in Saudi Arabia. Appl Energy 206:1225–1240. https://doi.org/10.1016/j.apenergy.2017.10.024

    Article  Google Scholar 

  15. Sánchez-Lozano JM, García-Cascales MS, Lamata MT (2015) Evaluation of suitable locations for the installation of solar thermoelectric power plants. Comput Ind Eng 87:343–355. https://doi.org/10.1016/j.cie.2015.05.028

    Article  Google Scholar 

  16. Stewart TJ (2005) Trends in Multicriteria Decision Making

  17. Adedeji PA, Akinlabi SA, Madushele N, Olatunji OO (2020) Neuro-fuzzy resource forecast in site suitability assessment for wind and solar energy: a mini review. J Clean Prod 269:1–29

    Article  Google Scholar 

  18. Ayodele TR, Ogunjuyigbe ASO, Odigie O, Munda JL (2018) A multi-criteria GIS based model for wind farm site selection using interval type-2 fuzzy analytic hierarchy process: the case study of Nigeria. Appl Energy 228:1853–1869. https://doi.org/10.1016/j.apenergy.2018.07.051

    Article  Google Scholar 

  19. Jangid J, Bera AK, Joseph M, Singh V, Singh TP, Pradhan BK et al (2016) Potential zones identification for harvesting wind energy resources in desert region of India–a multi criteria evaluation approach using remote sensing and GIS. Renew Sustain Energy Rev 65:1–10. https://doi.org/10.1016/j.rser.2016.06.078

    Article  Google Scholar 

  20. Adedeji PA, Akinlabi SA, Olatunji OO (2020) Hybrid neurofuzzy wind power forecast and wind turbine location for embedded generation. Int J Energy Res. https://doi.org/10.1002/er.5620

    Article  Google Scholar 

  21. Gigović L, Pamučar D, Božanić D, Ljubojević S (2017) Application of the GIS-DANP-MABAC multi-criteria model for selecting the location of wind farms: a case study of Vojvodina. Serbia Renew Energy 103:501–521. https://doi.org/10.1016/j.renene.2016.11.057

    Article  Google Scholar 

  22. Latinopoulos D, Kechagia K (2015) A GIS-based multi-criteria evaluation for wind farm site selection. a regional scale application in Greece. Renew Energy 78:550–560. https://doi.org/10.1016/j.renene.2015.01.041

    Article  Google Scholar 

  23. Gothwal S, Saha R (2015) Plant location selection of a manufacturing industry using analytic hierarchy process approach. Int J Serv Oper Manag 22:235–255. https://doi.org/10.1504/IJSOM.2015.071531

    Article  Google Scholar 

  24. Pohekar SD, Ramachandran M (2004) Application of multi-criteria decision making to sustainable energy planning - a review. Renew Sustain Energy Rev 8:365–381. https://doi.org/10.1016/j.rser.2003.12.007

    Article  Google Scholar 

  25. Kumar A, Sah B, Singh AR, Deng Y, He X, Kumar P (2017) A review of multi criteria decision making ( MCDM ) towards sustainable renewable energy development. Renew Sustain Energy Rev 69:596–609. https://doi.org/10.1016/j.rser.2016.11.191

    Article  Google Scholar 

  26. Perez R (2018) Wind field and solar radiation characterization and forecasting: a numerical approach for complex terrain. Springer International Publishing AG, Switzerland

    Book  Google Scholar 

  27. Giebel G, Kariniotakis G (2017) Wind power forecasting-a review of the state of the art. Elsevier Ltd, Amsterdam. https://doi.org/10.1016/B978-0-08-100504-0.00003-2

    Book  Google Scholar 

  28. Skittides C, Früh W (2014) Wind forecasting using principal component analysis. Renew Energy 69:365–374. https://doi.org/10.1016/j.renene.2014.03.068

    Article  Google Scholar 

  29. Ahmed A, Khalid M (2019) A review on the selected applications of forecasting models in renewable power systems. Renew Sustain Energy Rev 100:9–21. https://doi.org/10.1016/j.rser.2018.09.046

    Article  Google Scholar 

  30. Ramón J, Dopico R (2011) Encyclopedia of Artificial Intelligence. Hershey, New York, USA: Information Science Reference, IGI Global; https://doi.org/10.4018/978-1-59904-849-9

  31. Khosravi A, Machado L, Nunes RO (2018) Time-series prediction of wind speed using machine learning algorithms: a case study osorio wind farm. Braz Appl Energy 224:550–566. https://doi.org/10.1016/j.apenergy.2018.05.043

    Article  Google Scholar 

  32. Nousi P, Tefas A (2017) Deep learning algorithms for discriminant autoencoding. Neurocomputing 266:325–335. https://doi.org/10.1016/j.neucom.2017.05.042

    Article  Google Scholar 

  33. Torrecilla JL, Romo J (2018) Data learning from big data. Stat Probab Lett 136:15–19. https://doi.org/10.1016/j.spl.2018.02.038

    Article  MathSciNet  MATH  Google Scholar 

  34. Komura D, Ishikawa S (2018) Machine learning methods for histopathological image analysis. Comput Struct Biotechnol J 16:34–42. https://doi.org/10.1016/j.csbj.2018.01.001

    Article  Google Scholar 

  35. Adedeji PA, Masebinu SO, Akinlabi SA, Madushele N (2019) adaptive neuro-fuzzy inference system (ANFIS) modelling in energy system and water resources. Optim Using Evol Algorithms Metaheuristics. https://doi.org/10.1201/9780429293030-7

    Article  Google Scholar 

  36. Balasubramanian K (2020) Improved adaptive neuro-fuzzy inference system based on modified glowworm swarm and differential evolution optimization algorithm for medical diagnosis. Neural Comput Appl. https://doi.org/10.1007/s00521-020-05507-0

    Article  Google Scholar 

  37. Moayedi H, Rezaei A (2020) The feasibility of PSO–ANFIS in estimating bearing capacity of strip foundations rested on cohesionless slope. Neural Comput Appl. https://doi.org/10.1007/s00521-020-05231-9

    Article  Google Scholar 

  38. Hasanipanah M, Bakhshandeh H, Hossein A (2018) Svm PSOAÁ. feasibility of PSO–ANFIS model to estimate rock fragmentation produced by mine blasting. Neural Comput Appl 30:1015–1024. https://doi.org/10.1007/s00521-016-2746-1

    Article  Google Scholar 

  39. Pérez EC, Algredo-badillo I, Hugo V, Rodríguez G (2012) Performance analysis of ANFIS in short term wind speed prediction. Int J Comput Sci Inf 9:94–102

    Google Scholar 

  40. Mohandes M, Rehman S, Rahman SM (2011) Estimation of wind speed profile using adaptive neuro-fuzzy inference system (ANFIS). Appl Energy 88:4024–4032. https://doi.org/10.1016/j.apenergy.2011.04.015

    Article  Google Scholar 

  41. Hassanien AE, Gaber T, Tsai P, Pan J (2016) A hybrid krill-ANFIS model for wind speed forecasting. Int Conf Adv Intell Syst Inform 7(2017):365–372. https://doi.org/10.1007/978-3-319-48308-5

    Article  Google Scholar 

  42. Roshni T (2020) Neural network modeling for groundwater-level forecasting in coastal aquifers. Neural Comput Appl 32:12737–12754. https://doi.org/10.1007/s00521-020-04722-z

    Article  Google Scholar 

  43. Christodoulou CI, Pattichis CS (1999) Unsupervided pattern recognition for the classification of EMG signals. IEEE Trans Biomed Eng 46:169–178. https://doi.org/10.1109/10.740879

    Article  Google Scholar 

  44. Adedeji PA, Akinlabi S, Ajayi O, Madushele N (2019) Non-linear autoregressive neural network (NARNET) with SSA filtering for a university energy consumption forecast. Procedia Manuf 33:176–183. https://doi.org/10.1016/j.promfg.2019.04.022

    Article  Google Scholar 

  45. Islam S, Mohandes M, Rehman S (2017) Vertical extrapolation of wind speed using artificial neural network hybrid system. Neural Comput Appl 28:2351–2361. https://doi.org/10.1007/s00521-016-2373-x

    Article  Google Scholar 

  46. Jamil M, Zeeshan M (2019) A comparative analysis of ANN and chaotic approach-based wind speed prediction in India. Neural Comput Appl 31:6807–6819. https://doi.org/10.1007/s00521-018-3513-2

    Article  Google Scholar 

  47. Cevallos-Sierra J, Ramos-Martin J (2018) Spatial assessment of the potential of renewable energy: the case of Ecuador. Renew Sustain Energy Rev 81:1154–1165. https://doi.org/10.1016/j.rser.2017.08.015

    Article  Google Scholar 

  48. Höfer T, Sunak Y, Siddique H, Madlener R (2016) Wind farm siting using a spatial analytic hierarchy process approach: a case study of the städteregion aachen. Appl Energy 163:222–243. https://doi.org/10.1016/j.apenergy.2015.10.138

    Article  Google Scholar 

  49. Janke JR (2010) multicriteria GIS modeling of wind and solar farms in Colorado. Renew Energy 35:2228–2234. https://doi.org/10.1016/j.renene.2010.03.014

    Article  Google Scholar 

  50. Badger M, Astrup P, Hasager CB, Chang R, Zhu R (2014) Satellite based wind resource assessment over the South China Sea. Proc China Wind Power Conf (CWP 2014)

  51. Garmashov AV, Kubryakov AA, Shokurov MV, Stanichny SV, Toloknov YN, Korovushkin AI (2016) Comparing satellite and meteorological data on wind velocity over the Black Sea. Izv-Atmos Ocean Phys 52:309–316. https://doi.org/10.1134/S000143381603004X

    Article  Google Scholar 

  52. Furevik BR, Espedal HA, Hamre T, Hasager CB, Johannessen OM, Jørgensen BH et al (2003) Satellite-based wind maps as guidance for siting offshore wind farms. Wind Eng 27:327–338

    Article  Google Scholar 

  53. Department of Energy. 2015 State of Renewable Energy in South Africa. https://doi.org/10.1016/j.joule.2017.07.005

  54. Konstantinos I, Georgios T, Garyfalos A (2019) A decision support system methodology for selecting wind farm installation locations using AHP and TOPSIS: case study in Eastern Macedonia and Thrace region. Greece Energy Policy 132:232–246. https://doi.org/10.1016/j.enpol.2019.05.020

    Article  Google Scholar 

  55. Van Haaren R, Fthenakis V (2011) GIS-based wind farm site selection using spatial multi-criteria analysis (SMCA): evaluating the case for New York State. Renew Sustain Energy Rev 15:3332–3340. https://doi.org/10.1016/j.rser.2011.04.010

    Article  Google Scholar 

  56. Ayodele TR, Ogunjuyigbe ASO, Odigie O, Munda JL (2018) A multi-criteria GIS based model for wind farm site selection using interval type-2 fuzzy analytic hierarchy process: the case study of Nigeria. Appl Energy 228:1853–1869. https://doi.org/10.1016/j.apenergy.2018.07.051

    Article  Google Scholar 

  57. Gorsevski PV, Cathcart SC, Mirzaei G, Jamali MM, Ye X, Gomezdelcampo E (2013) A group-based spatial decision support system for wind farm site selection in Northwest Ohio. Energy Policy 55:374–385. https://doi.org/10.1016/j.enpol.2012.12.013

    Article  Google Scholar 

  58. Ali S, Taweekun J, Techato K, Waewsak J, Gyawali S (2019) GIS based site suitability assessment for wind and solar farms in Songkhla. Thail Renew Energy 132:1360–1372. https://doi.org/10.1016/j.renene.2018.09.035

    Article  Google Scholar 

  59. Maczulak A.( 2003) Renewable Energy Sources. Taylor & Francis Books Inc

  60. Johnson GL (2006) Wind Energy Systems. https://doi.org/10.1109/5.241490.

  61. Sadeghi M, Karimi M (2017) GIS-based solar and wind turbine site selection using multi-criteria analysis: case study Tehran Iran. Int Arch Photogramm Remote Sens Spat Inf Sci-ISPRS Arch 42:469–476. https://doi.org/10.5194/isprs-archives-XLII-4-W4-469-2017

    Article  Google Scholar 

  62. Sliz-Szkliniarz B, Vogt J (2011) GIS-based approach for the evaluation of wind energy potential: a case study for the Kujawsko-Pomorskie Voivodeship. Renew Sustain Energy Rev 15:1696–1707. https://doi.org/10.1016/j.rser.2010.11.045

    Article  Google Scholar 

  63. Roa-Escalante GDJ, Sánchez-Lozano JM, Faxas JG, García-Cascales MS, Urbina A (2015) The effects of photovoltaic electricity injection into microgrids: combination of geographical information systems, multicriteria decision methods and electronic control modeling. Energy Convers Manag 96:89–99. https://doi.org/10.1016/j.enconman.2015.02.060

    Article  Google Scholar 

  64. Aydin NY, Kentel E, Sebnem DH (2013) GIS-based site selection methodology for hybrid renewable energy systems: a case study from western Turkey. Energy Convers Manag 70:90–106. https://doi.org/10.1016/j.enconman.2013.02.004

    Article  Google Scholar 

  65. Department of Environmental Affairs (2015) EIA guideline for renewable energy projects

  66. Atici KB, Simsek AB, Ulucan A, Tosun MU (2015) A GIS-based multiple criteria decision analysis approach for wind power plant site selection. Util Policy 37:86–96. https://doi.org/10.1016/j.jup.2015.06.001

    Article  Google Scholar 

  67. Aydin NY, Kentel E, Duzgun S (2010) GIS-based environmental assessment of wind energy systems for spatial planning: a case study from Western Turkey. Renew Sustain Energy Rev 14:364–373. https://doi.org/10.1016/j.rser.2009.07.023

    Article  Google Scholar 

  68. Tamura J (2012) Wind Energy Conversion Systems https://doi.org/10.1007/978-1-4471-2201-2.

  69. Kumar Y, Ringenberg J, Depuru SS, Devabhaktuni VK, Lee JW, Nikolaidis E et al (2016) Wind energy: trends and enabling technologies. Renew Sustain Energy Rev 53:209–224. https://doi.org/10.1016/j.rser.2015.07.200

    Article  Google Scholar 

  70. Taylor MR, Peacock F (2018) The state of South Africa’s birds. South Africa

  71. Ralston Paton S, Smallie J, Pearson A, Ramalho R (2017) Wind energy’s impacts on birds in South Africa A preliminary review of the results of operational monitoring at the first wind farms of the Renewable Energy Independent Power Producer Procurement Programme in South Africa

  72. Saaty TL.(2012) Decision making for leaders: The analytic hierarchy process for decisions in a complex world. 3rd revise. Pittsburgh: RWS

  73. Uyan M (2013) GIS-based solar farms site selection using analytic hierarchy process (AHP) in Karapinar region Konya/Turkey. Renew Sustain Energy Rev 28:11–17. https://doi.org/10.1016/j.rser.2013.07.042

    Article  Google Scholar 

  74. Yang J, Lee H (1997) An AHP decision model for facility location selection. Facil 15:241–254

    Article  Google Scholar 

  75. Goepel KD (2018) Implementation of an online software tool for the analytic hierarchy process (AHP-OS). Int J Anal Hierarchy Process 10:469–487

    Google Scholar 

  76. Vafaeipour M, Rahbari O, Rosen MA, Fazelpour F, Ansarirad P (2014) Application of sliding window technique for prediction of wind velocity time series. Int J Energy Environ Eng 5:1–7. https://doi.org/10.1007/s40095-014-0105-5

    Article  Google Scholar 

  77. He Z, Chen Y, Shang Z, Li C, Li L, Xu M (2019) A novel wind speed forecasting model based on moving window and multi-objective particle swarm optimization algorithm. Appl Math Model 76:717–740. https://doi.org/10.1016/j.apm.2019.07.001

    Article  MathSciNet  MATH  Google Scholar 

  78. Nurunnahar S, Talukdar DB, Rasel RI, Sultana N (2017) A short term wind speed forcasting using SVR and BP-ANN: A comparative analysis. 20th Int Conf Comput Inf Technol ICCIT 2018; 2018 1–6. https://doi.org/10.1109/ICCITECHN.2017.8281802

  79. Yu H, Wilamowski B (2011) Levenberg–Marquardt Training. Intell Syst. https://doi.org/10.1201/b10604-15

    Article  Google Scholar 

  80. Sze P, Lu Y (2020) “SPOCU”: scaled polynomial constant unit activation function. Neural Comput Appl. https://doi.org/10.1007/s00521-020-05182-1

    Article  Google Scholar 

  81. Therdthai N, Zhou W (2001) Artificial neural network modelling of the electrical conductivity property of recombined milk. J Food Eng 50:107–111. https://doi.org/10.1016/S0260-8774(00)00202-8

    Article  Google Scholar 

  82. Sahay T, Aggarwal A, Bansal A, Chandra M (2015) SVM and ANN: A comparative evaluation. Proc 2015 1st Int Conf Next Gener Comput Technol NGCT 2016: 960–4. https://doi.org/10.1109/NGCT.2015.7375263

  83. Langer S (2021) Approximating smooth functions by deep neural networks with sigmoid activation function. J Multivar Anal 182:104696. https://doi.org/10.1016/j.jmva.2020.104696

    Article  MathSciNet  MATH  Google Scholar 

  84. Luo D, Hosseini HG, Stewart JR (2004) Application of ANN with extracted parameters from an electronic nose in cigarette brand identification. Sens Actuators, B Chem 99:253–257. https://doi.org/10.1016/j.snb.2003.11.022

    Article  Google Scholar 

  85. Basatini FM, Chinipardaz R (2014) Softmax model as generalization upon logistic discrimination suffers from overfitting. J Data Sci 12:563–574

    Article  Google Scholar 

  86. Jang JR (1993) ANFIS: Adaptive network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23:665–685

    Article  Google Scholar 

  87. Sreedhara BM, Rao M, Mandal S (2018) Application of an evolutionary technique (PSO–SVM) and ANFIS in clear-water scour depth prediction around bridge piers. Neural Comput Appl. https://doi.org/10.1007/s00521-018-3570-6

    Article  Google Scholar 

  88. Pousinho HMI, Mendes VMF, Catalão JPS (2011) A hybrid pso-anfis approach for short-term wind power prediction in Portugal. Energy Convers Manag 52:397–402. https://doi.org/10.1016/j.enconman.2010.07.015

    Article  Google Scholar 

  89. Suparta W, Alhasa KM (2016) Adaptive neuro-fuzzy inference system model. Tropospheric Delays Using ANFIS, 5–19. https://doi.org/10.1007/978-3-319-28437-8

  90. Küçükdeniz T, Baray A, Ecerkale K (2012) Integrated use of fuzzy c-means and convex programming for capacitated multi-facility location problem. Expert Syst with Appl 39:4306–4314. https://doi.org/10.1016/j.eswa.2011.09.102

    Article  Google Scholar 

  91. Adedeji PA, Akinlabi S, Madushele N, Olatunji OO (2020) Wind turbine power output very short-term forecast: a comparative study of data clustering techniques in a PSO-ANFIS model. J Clean Prod 254:1–29. https://doi.org/10.1016/j.jclepro.2020.120135

    Article  Google Scholar 

  92. Babu MR, Badar QHA, Balasubramani S (2020) Fuzzy-C Means Clustering Based ANFIS wind speed forecast. 21st Natl. Power Syst. Conf., p. 1–6

  93. Barak S, Sadegh SS (2016) Forecasting energy consumption using ensemble ARIMA-ANFIS hybrid algorithm. Int J Electr Power Energy Syst 82:92–104. https://doi.org/10.1016/j.ijepes.2016.03.012

    Article  Google Scholar 

  94. Rezakazemi M, Dashti A, Asghari M, Shirazian S (2017) H2-selective mixed matrix membranes modeling using ANFIS, PSO-ANFIS. GA-ANFIS Int J Hydrog Energy 42:15211–15225. https://doi.org/10.1016/j.ijhydene.2017.04.044

    Article  Google Scholar 

  95. Shamshirband S, Ćojbašić Ž, Yee PL, Al-Shammari ET, Petković D, Zalnezhad E et al (2016) Comparative study of clustering methods for wake effect analysis in wind farm. Energy 95:573–579. https://doi.org/10.1016/j.energy.2015.11.064

    Article  Google Scholar 

  96. Ross TJ (2004) Fuzzy logic with engineering applications. Wiley, New Jersey. https://doi.org/10.1002/9781119994374

    Book  MATH  Google Scholar 

  97. Alba E, Chicano JF (2004) Training Neural Networks with GA Hybrid Algorithms. Genet. Evol. Comput.–GECCO 2004, p. 852–63. https://doi.org/10.1007/978-3-540-24854-5_87

  98. Abdel-Khalek S, Ben Ishak A, Omer OA, Obada ASF (2017) A two-dimensional image segmentation method based on genetic algorithm and entropy. Optik (Stuttg) 131:414–422. https://doi.org/10.1016/j.ijleo.2016.11.039

    Article  Google Scholar 

  99. Saeidian B, Mesgari MS, Ghodousi M (2016) Evaluation and comparison of genetic algorithm and bees algorithm for location-allocation of earthquake relief centers. Int J Disaster Risk Reduct 15:94–107. https://doi.org/10.1016/j.ijdrr.2016.01.002

    Article  Google Scholar 

  100. Pan WT (2009) Forecasting classification of operating performance of enterprises by zscore combining ANFIS and genetic algorithm. Neural Comput Appl 18:1005–1011. https://doi.org/10.1007/s00521-009-0243-5

    Article  Google Scholar 

  101. Millonas M (1994) Swarms, phase transitions, and collective intelligence. Artificial Lue Ill, Addison Wesley, Reading, MA

    Google Scholar 

  102. Cagnoni S, Mordonini M (2009) Particle swarm optimization and image analysis. InEncyclopedia of Artificial Intelligence (pp. 1303-1309). IGI Global: Pennsylvania

  103. Eberhart R, Kennedy J (2002) A new optimizer using particle swarm theory. Sixth Int. Symp. Micro Mach. Hum. Sci., p. 39–43. https://doi.org/10.1109/mhs.1995.494215

  104. Couceiro M, Ghamisi P (2016) Particle swarm optimization. Fract. Order darwinian part. Swarm optim., Applied Sciences and Technology; p. 1–10. https://doi.org/10.1007/978-3-319-19635-0

  105. Adedeji PA, Akinlabi S, Madushele N, Olatunji OO (2020) Wind turbine power output very short-term forecast: a comparative study of data clustering techniques in a PSO-ANFIS model. J Clean Prod 254:1–16. https://doi.org/10.1016/j.jclepro.2020.120135

    Article  Google Scholar 

  106. Semero YK, Zheng D, Zhang J (2018) A PSO-ANFIS based hybrid approach for short term PV power prediction in microgrids. Electr Power Compon Syst 46:1–9. https://doi.org/10.1080/15325008.2018.1433733

    Article  Google Scholar 

  107. Kennedy J (1998) The Behaviour of Particles. In: Porto VW, Saravanan N, Waagen D, Eiben AE, editors. Evol. Program. VII Proc. 7th Annu. Conf. Evol. Program. Conf. San Diego, Springer-Verlag; p. 581–9

  108. Engelbrecht AP, Cleghorn CW, Engelbrecht A. Recent advances in particle swarm optimization analysis and understanding. GECCO ’19 Proc. Genet. Evol. Comput. Conf. Companion, 2019, p. 1–28. https://doi.org/10.1145/3319619.3323368

  109. Engelbrecht AP (2007) Computational Intelligence: An introduction

  110. van den Bergh F, Engelbrecht AP (2006) A Study of Particle Swarm Optimization Particle Trajectories. Inf Sci (Ny) 176:937–971

    Article  MathSciNet  Google Scholar 

  111. Lee JCY, Jason Fields M, Lundquist JK (2018) Assessing variability of wind speed: Comparison and validation of 27 methodologies. Wind Energy Sci 3:845–868. https://doi.org/10.5194/wes-3-845-2018

    Article  Google Scholar 

  112. Hallgren W, Gunturu UB, Schlosser A (2014) The potential wind power resource in Australia A new perspective. PLoS ONE. https://doi.org/10.1371/journal.pone.0099608

    Article  Google Scholar 

  113. Saini S, Rohaya D, Awang B, Zakaria MNB, Sulaiman SB (2014) A review on particle swarm optimization algorithm and its variants to human motion tracking. Math Probl Eng. 2014:13–15

    Article  Google Scholar 

  114. Yip CMA, Gunturu UB, Stenchikov GL (2016) Wind resource characterization in the Arabian Peninsula. Appl Energy 164:826–836. https://doi.org/10.1016/j.apenergy.2015.11.074

    Article  Google Scholar 

  115. Olatunji O, Akinlabi S, Madushele N, Adedeji P, Fatoba S (2019) Comparative analysis of the heating values of biomass based on GA-ANFIS and PSO-ANFIS models. Am Soc Mech Eng Power Div POWER 2019: 1–5. https://doi.org/10.1115/POWER2019-1825

  116. Khosravi A, Koury RNN, Machado L, Pabon JJG (2018) Prediction of wind speed and wind direction using artificial neural network, support vector regression and adaptive neuro-fuzzy inference system. Sustain Energy Technol Assess 25:146–160. https://doi.org/10.1016/j.seta.2018.01.001

    Article  Google Scholar 

  117. Noorollahi Y, Ali M, Kalhor A (2016) Using artificial neural networks for temporal and spatial wind speed forecasting in Iran. Energy Convers Manag 115:17–25. https://doi.org/10.1016/j.enconman.2016.02.041

    Article  Google Scholar 

Download references

Acknowledgements

The authors appreciate Birdlife International and Wind Atlas South Africa (WASA) for providing relevant data for this study and the University of Johannesburg for the support provided.

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering Science, University of Johannesburg, Johannesburg, South Africa

    Paul A. Adedeji, Nkosinathi Madushele & Obafemi O. Olatunji

  2. Department of Mechanical and Industrial Engineering, University of Johannesburg, Johannesburg, South Africa

    Stephen A. Akinlabi

  3. Department of Mechanical Engineering, Walter Sisulu University, Butherworth Campus, East London, Eastern Cape, South Africa

    Stephen A. Akinlabi

Authors
  1. Paul A. Adedeji
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Stephen A. Akinlabi
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Nkosinathi Madushele
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Obafemi O. Olatunji
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Paul A. Adedeji.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

See Fig. 

Fig. 12
figure 12figure 12

a Wind speed b distance to transmission lines c distance to roads d elevation e distance to wetlands f distance to waterbodies g distance to airports h distance to urban places i distance to railway lines

Full size image

12

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Adedeji, P.A., Akinlabi, S.A., Madushele, N. et al. Hybrid neurofuzzy investigation of short-term variability of wind resource in site suitability analysis: a case study in South Africa. Neural Comput & Applic 33, 13049–13074 (2021). https://doi.org/10.1007/s00521-021-06001-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-021-06001-x

Keywords