Skip to main content

Advertisement

SpringerLink
Log in

A Novel Cooperative Fuzzy Classifier for Predicting the Permissible Wind Speed Range in Wind Farms

  • Research Paper
  • Published:
Iranian Journal of Science and Technology, Transactions of Electrical Engineering Aims and scope Submit manuscript

Abstract

It is important to forecast the wind speed range for managing the operation of wind turbines (WTs). Since the electrical power generated by WTs is highly dependent on the uncertain inherent of atmosphere meteorology, improving the accuracy of wind speed range forecasting models leads to the improvement of wind generation prediction. For the sake of uncertainties, it is very challenging to develop an effective and practical model to achieve accurate wind speed range forecasting in large forecasting horizons. This paper presents a novel hybrid classifier based on extended-classifier system with real input (XCSR) and an adaptive neuro-fuzzy inference system (ANFIS), for classification of the wind speed range. It should be mentioned that by employing a cooperative fuzzy classifier system (XCSR–ANFIS), the accuracy and number of the rules that XCSR system must be learned during the training process in the proposed model will be higher and fewer than the XCSR model, respectively. Finally, the comparison of obtained results by implementing the proposed model with other methods for long and short horizons confirms the desirable performance of the proposed model.

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
Fig. 10

Similar content being viewed by others

References

  • Aasim SSN, Mohapatra A (2019) Repeated wavelet transform based ARIMA model for very short-term wind speed forecasting. Renew Energy 136:758–768

    Article  Google Scholar 

  • Abbasi E, Naghavi N (2017) Offline auto-tuning of a PID controller using extended classifier system (XCS) algorithm. J Adv Comput Eng Technol 3(1):41–50

    Google Scholar 

  • Ali M, Khan A, Rehman N (2017) Hybrid multiscale wind speed forecasting based on variational mode decomposition. Int Trans Electr Energ Syst. https://doi.org/10.1002/etep.2466

    Article  Google Scholar 

  • Butz M (2007) Combining gradient-based with evolutionary online learning: an introduction to learning classifier systems. In: Proceedings of the international conference on hybrid intelligent systems

  • Cadenas E, Rivera W, Campos-Amezcua R, Heard C (2016) Winds peed prediction using a univariate ARIMA model and a multivariate NARX model. Energies 9(2):109

    Article  Google Scholar 

  • Cao Q, Ewing BT, Thompson MA (2012) Forecasting wind speed with recurrent neural networks. Eur J Oper Res 221(1):148–154

    Article  MathSciNet  Google Scholar 

  • Castronuovo ED, Lopes JAP (2004) On the optimization of the daily operation of a wind hydro power plant. IEEE Trans Power Syst 19(3):1599–1606

    Article  Google Scholar 

  • Chang GW, Lu HJ, Wang PK, Chang YR, Lee YD (2017) Gaussian mixture model-based neural network for short-term wind power forecast. Int Trans Electr Energ Syst. https://doi.org/10.1002/etep.2320

    Article  Google Scholar 

  • Dhiman HS, Deb D, Guerrero JM (2019) Hybrid machine intelligent SVR variants for wind forecasting and ramp events. Renew Sustain Energy Rev 108:369–379

    Article  Google Scholar 

  • Doherty R, O’Malley M (2003) Quantifying reserve demands due to increasing wind power penetration. In: Proceedings of the International Conference IEEE Power Tech Bologna, Italy

  • Doucoure B, Agbossou K, Cardenas A (2016) Time series prediction using artificial wavelet neural network and multi-resolution analysis: application to wind speed data. Renew Energy 192:202–211

    Article  Google Scholar 

  • Eseye AT, Zhang J, Zheng D, Ma H, Jingfu G (2017) A double-stage hierarchical ANFIS model for short-term wind power prediction. In: Proceedings of the international conference big data analysis

  • EWEA (2015) Wind energy scenarios for 2030′, reported by the European Wind Energy Association

  • Feng C, Cui M, Hodge BM, Zhang J (2017) A data-driven multimodel methodology with deep feature selection for short-term wind forecasting. Appl Energy 190(15):1245–1257

    Article  Google Scholar 

  • Finamore AR, Calderaro V, Galdi V, Piccolo A, Conio G, Grasso S (2015) A day-ahead wind speed forecasting using data-mining model-a feedforward NN algorithm. In: International conference on renewable energy research and applications

  • Firat U, Engin SN, Saraclar M, Ertuzun AB (2010) Wind speed forecasting based on second order blind identification and autoregressive model. In: Proceedings of the international conference machine learning and applications, Washington

  • Holland JH (1975) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. University of Michigan Press, Ann Arbor

    MATH  Google Scholar 

  • Holland JH (1986) A mathematical framework for studying learning in classifier systems. Phys D 22(1):37–317

    MathSciNet  MATH  Google Scholar 

  • Holmes JH, Lanzi PL, Stolzmann W, Wilson SW (2002) Learning classifier systems: new models, successful applications. Inf Proces Lett 82:23–30

    Article  MathSciNet  Google Scholar 

  • Hossin M, Sulaiman MDN, Mustapha A, Mustapha N (2011) A novel performance metric for building an optimized classifier. J Comput Sci 7(4):582–590

    Article  Google Scholar 

  • Hu YL, Chen L (2018) A nonlinear hybrid wind speed forecasting model using LSTM network, hysteretic ELM and differential evolution algorithm. Energy Convers Manage 173:123–142

    Article  Google Scholar 

  • Hu Q, Su P, Yu D, Liu J (2014) Pattern-based wind speed prediction based on generalized principal component analysis. IEEE Trans Sust Energy 5(3):866–874

    Article  Google Scholar 

  • Jiang P, Yang H, Heng J (2019) A hybrid forecasting system based on fuzzy time series and multi-objective optimization for wind speed forecasting. Appl Energy 235:786–801

    Article  Google Scholar 

  • Kecman V (2001) Leaning and soft computing support vector machines, neural networks, and fuzzy logic models. MIT Press, Cambridge

    MATH  Google Scholar 

  • Kehe W, Yue Y, Bohao C, Jinshui W (2013) Research of wind power prediction model based on RBF neural network. In: Proceedings international conference computational and information sciences

  • Khosravi A, Machado L, Nunes RO (2018) Time-series prediction of wind speed using machine learning algorithms: a case study Osorio wind farm, Brazil. Appl Energy 224:550–566

    Article  Google Scholar 

  • Kostas P, Deligiorgi D (2012) Application of artificial neural networks for the spatial estimation of wind speed in a coastal region with complex topography. Renew Energy 1:75–82

    Google Scholar 

  • Kovacs T (1996) Evolving optimal populations with XCS classifier systems. Master’s thesis, University of Birmingham U.K.

  • Lee D, Baldick R (2014) Short-term wind power ensemble prediction based on gaussian processes and neural networks. IEEE Trans Smart Grid 5(1):501–510

    Article  Google Scholar 

  • Li H, Wang J, Lu H, Guo Z (2018) Research and application of a combined model based on variable weight for short term wind speed forecasting. Renew Energy 116:669–684

    Article  Google Scholar 

  • Ling-Ling L, Li JH, He PJ, Wang CS (2011) The use of wavelet theory and ARMA model in wind speed prediction. In: Proceedings of the international conference electric power equipment-switching technology

  • Liu Z (2014) Wind speed forecasting model based on fuzzy manifold support vector machine. J Inf Comput Sci 11(7):2387–2395

    Article  Google Scholar 

  • Liu H, Tian HQ, Pan DF, Li YF (2013) Forecasting models for wind speed using wavelet, wavelet packet, time series and Artificial Neural Networks. Appl Energy 107:191–208

    Article  Google Scholar 

  • Lu P, Ye L, Sun B, Zhang C, Zhao Y, Teng J (2018) A new hybrid prediction method of ultra-short-term wind power forecasting based on EEMD-PE and LSSVM optimized by the GSA. Energies 11(4):697

    Article  Google Scholar 

  • Ma L, Shiyan L, Chuanwen J, Hongling L, Yan Zhang Y (2009) A review on the forecasting of wind speed and generated power. Renew Sust Energy Rev 13(4):915–920

    Article  Google Scholar 

  • Manyonge AW, Ochieng RM, Onyango FN, Shichikha JM (2012) Mathematical Modelling of Wind Turbine in a Wind Energy Conversion System Power Coefficient Analysis. Appl Math Sci 6(91):4527–4536

    MATH  Google Scholar 

  • Sabzehzar A, Shana WL, Shariat PM, Saremi O (2015) An improved extended classifier system for the real-time-input real-time-output (XCSRR) stability control of a biped robot. Procedia Comput Sci 61:492–499

    Article  Google Scholar 

  • Shi J, Ding Z, Lee WJ, Yang Y, Liu Y, Zhang M (2014) Hybrid forecasting model for very-short term wind power forecasting based on grey relational analysis and wind speed distribution features. IEEE Trans Smart Grid 5(1):521–526

    Article  Google Scholar 

  • Song J, Wang J, Lu H (2018) A novel combined model based on advanced optimization algorithm for short-term wind speed forecasting. Appl Energy 215:643–658

    Article  Google Scholar 

  • Vahidinasab V, Jadid S (2010) Bayesian neural network model to predict day-ahead electricity prices. Euro Trans Elec Power. 20(2):231–246

    Google Scholar 

  • Vahidinasab V, Vafa HS (2018) Wind power forecasting by a new hybrid forecast engine composed of GA/EPSO-based mutual information and group method of data handling (GMDH). Comput Int Electron Eng 2:1–14 (In Persian)

    Google Scholar 

  • Vahidinasab V, Jadid S, Kazemi A (2008) Day-ahead price forecasting in restructured power systems using artificial neural networks. Elec Power Syst Res. 78(8):1332–1342

    Article  Google Scholar 

  • Wang J, Du P, Niu T, Yang W (2017) A novel hybrid system based on a new proposed algorithm—Multi-objective whale optimization algorithm for wind speed forecasting. Appl Energy 208:344–360

    Article  Google Scholar 

  • Wilson SW (1995) Classifier fitness based on accuracy. Evol Comput 3(2):149–175

    Article  Google Scholar 

  • Wilson SW (1998) Generalization in the XCS classifier system. In: Proceedings conference in genetic programming, Morgan Kaufmann. San Francisco

  • Wilson SW (2000) Get real! XCS with continuous-valued inputs. Learn Classif Syst 15:209–219

    Article  Google Scholar 

  • Zhang W, Wang J, Wang J, Zhao Z, Tian M (2013) Short-term wind speed forecasting based on a hybrid model. Appl Soft Comput 13(7):3225–3233

    Article  Google Scholar 

  • Zhang C, Zhou J, Li C, Fu W, Peng T (2017) A compound structure of ELM based on feature selection and parameter optimization using hybrid backtracking search algorithm for wind speed forecasting. Energy Convers Manage 143:360–376

    Article  Google Scholar 

  • Zhao X, Wang S, Li T (2011) Review of evaluation criteria and main methods of wind power forecasting. Energy Procedia 12:761–769

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran

    Mohammadali Alipour, Jamshid Aghaei & Mohammadali Norouzi

  2. Department of Computer Engineering, Shiraz University, Shiraz, Iran

    Sattar Hashemi

  3. Department of Electrical Engineering and Automation, Aalto University, Espoo, Finland

    Matti Lehtonen

Authors
  1. Mohammadali Alipour
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jamshid Aghaei
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohammadali Norouzi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sattar Hashemi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Matti Lehtonen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jamshid Aghaei.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Alipour, M., Aghaei, J., Norouzi, M. et al. A Novel Cooperative Fuzzy Classifier for Predicting the Permissible Wind Speed Range in Wind Farms. Iran J Sci Technol Trans Electr Eng 45, 29–45 (2021). https://doi.org/10.1007/s40998-020-00347-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40998-020-00347-z

Keywords

Search

Navigation