Skip to main content
SpringerLink
Log in
Book cover

International Conference on Sustainable Energy for Smart Cities

SESC 2021: Sustainable Energy for Smart Cities pp 189–204Cite as

A Short Term Wind Speed Forecasting Model Using Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System Models

  • Conference paper
  • First Online:

Abstract

Future power systems encourage the use of renewable energy resources, among them wind power is of great interest, but its power output is intermittent in nature which can affect the stability of the power system and increase the risk of blackouts. Therefore, a forecasting model of the wind speed is essential for the optimal operation of a power supply with an important share of wind energy conversion systems. In this paper, two wind speed forecasting models based on multiple meteorological measurements of wind speed and temperature are proposed and compared according to their mean squared error (MSE) value. The first model concerns the artificial intelligence based on neural network (ANN) where several network configurations are proposed to achieve the most suitable structure of the problem, while the other model concerned the Adaptive Neuro-Fuzzy Inference System (ANFIS). To enhance the results accuracy, the invalid input samples are filtered. According to the computational results of the two models, the ANFIS has delivered more accurate outputs characterized by a reduced mean squared error value compared to the ANN-based model.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   64.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   84.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Buck, M., Graf, A., Graichen, P.: The big picture. Ten priorities for the next European Commission to meet the EU’s 2030 targets and accelerate towards 2050. European Energy Transition (2019). https://www.agora-energiewende.de

  2. Tascikaraoglu, A., Uzunoglu, M.: A review of combined approaches for prediction of short-term wind speed and power. Renew. Sustain. Energy Rev. 34, 243–254 (2014). https://doi.org/10.1016/j.rser.2014.03.033

    Article  Google Scholar 

  3. Naik, J., Satapathy, P., Dash, P.K.: Short-term wind speed and wind power prediction using hybrid empirical mode decomposition and kernel ridge regression. Appl. Soft Comput. 70, 1167–1188 (2018). https://doi.org/10.1016/j.asoc.2017.12.010

    Article  Google Scholar 

  4. Georgilakis, P.S.: Technical challenges associated with the integration of wind power into power systems. Renew. Sustain. Energy Rev. 12(3), 852–863 (2008). https://doi.org/10.1016/j.rser.2006.10.007

    Article  Google Scholar 

  5. Sohoni, V., Gupta, S.C., Nema, R.K.: A critical review on wind turbine power curve modelling techniques and their applications in wind based energy systems. J. Energy 2016, 1–18 (2016). https://doi.org/10.1155/2016/8519785

    Article  Google Scholar 

  6. Lei, M., Shiyan, L., Chuanwen, J., Hongling, L., Yan, Z.: A review on the forecasting of wind speed and generated power. Renew. Sustain. Energy Rev. 13(4), 915–920 (2009). https://doi.org/10.1016/j.rser.2008.02.002

    Article  Google Scholar 

  7. Soman, S.S., Zareipour, H., Malik, O., Mandal, P.: A review of wind power and wind speed forecasting methods with different time horizons. In: North American Power Symposium (2010). https://doi.org/10.1109/naps.2010.5619586

  8. Chang, W.Y.: A Literature review of wind forecasting methods. J. Power Energy Eng. 2, 61–168 (2014). ee.2014.24023. https://doi.org/10.4236/jp

  9. Samadianfard, S., et al.: Wind speed prediction using a hybrid model of the multi-layer perceptron and whale optimization algorithm. J. Energy Rep. 6, 1147–1159 (2020). https://doi.org/10.1016/j.egyr.2020.05.001

    Article  Google Scholar 

  10. Zheng, D., Shi, M., Wang, Y., Eseye, A., Zhang, J.: Day-ahead wind power forecasting using a two-stage hybrid modeling approach based on SCADA and meteorological information, and evaluating the impact of input-data dependency on forecasting accuracy. Energies 10(12), 1988 (2017). https://doi.org/10.3390/en10121988

    Article  Google Scholar 

  11. Wu, Y.K., Lee, C.Y., Tsai, S.H., Yu, S.N.: Actual experience on the short-term wind power forecasting at Penghu-From an Island perspective. In: Proceedings of the 2010 International Conference on Power System Technology, pp. 1–8 (2010). https://doi.org/10.1109/powercon.2010.5666619

  12. Chang, W.Y.: Application of back propagation neural network for wind power generation forecasting. Int. J. Digit. Cont. Technol. Its Appl. 502–509 (2013). https://doi.org/10.4156/jdcta.vol7.issue4.61

  13. Yang, Z.L., Liu, Y.K., Li, C.R.: Interpolation of missing wind data based on ANFIS. Renew. Energy 36, 993–998 (2011). https://doi.org/10.1016/j.renene.2010.08.033

    Article  Google Scholar 

  14. Zeng, J.W., Qiao. W.: support vector machine-based short-term wind power forecasting. In: Proceedings of the IEEE/PES Power Systems Conference and Exposition, Phoenix, pp. 1–8 (2011). https://doi.org/10.1109/psce.2011.5772573

  15. Jursa, R., Rohrig, K.: Short-term wind power forecasting using evolutionary algorithms for the automated specification of artificial intelligence models. Int. J. Forecast. 24, 694–709 (2008). https://doi.org/10.1016/j.ijforecast.2008.08.007

    Article  Google Scholar 

  16. Catalão, J.P.S., Pousinho, H.M.I., Mendes, V.M.F.: Short-term wind power forecasting in Portugal by neural networks and wavelet transform. Renew. Energy 36, 1245–1251 (2011). https://doi.org/10.1016/j.renene.2010.09.016

  17. Hecht-Nielsen. R.: Theory of the backpropagation neural network. In: Neural Networks for Perception, pp. 65–93. Academic Press, Boston (1992). https://doi.org/10.1109/ijcnn.1989.118638

  18. Dongale, T.D., Patil, K.P., Vanjare, S.R., Chavan, A.R., Gaikwad, P.K., Kamat, R.K.: Modelling of nanostructured memristor device characteristics using artificial neural network (ANN). J. Comput. Sci. 11, 82–90 (2015). https://doi.org/10.1016/j.jocs.2015.10.007

    Article  MathSciNet  Google Scholar 

  19. Ronald, P.K.: Outliers in process modeling and identification. IEEE Trans. Control Syst. Technol. 10, 55–63 (2002). https://doi.org/10.1109/87.974338

    Article  Google Scholar 

  20. Gholami, A., et al.: A methodological approach of predicting threshold channel bank profile by multi-objective evolutionary optimization of ANFIS. Eng. Geol. 239, 298–309 (2018). https://doi.org/10.1016/j.enggeo.2018.03.030

  21. Abdulshahed, A.M., Longstaff, A.P., Fletcher, S.: The application of ANFIS prediction models for thermal error compensation on CNC machine tools. Appl. Soft Comput. 27, 158–168 (2015). https://doi.org/10.1016/j.asoc.2014.11.012

    Article  Google Scholar 

  22. Sushmita, M., Hayashi, Y.: Neuro-fuzzy rule generation: survey in soft computing framework. IEEE Trans. Neural Netw. 11, 748–768 (2000). https://doi.org/10.1109/72.846746

    Article  Google Scholar 

  23. Vahidnia, M.H., Alesheikh, A.A., Alimohammadi, A., Hosseinali, F.: A GIS-based neuro-fuzzy procedure for integrating knowledge and data in landslide susceptibility mapping. Comput. Geosci. 36, 1101–1114 (2017). https://doi.org/10.1016/j.cageo.2017.04.004

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Research Centre in Digitalization and Intelligent Robotics (CeDRI), Instituto Politécnico de Bragança, Bragança, Portugal

    Yahia Amoura, Ana I. Pereira & José Lima

  2. ALGORITMI Center, University of Minho, Braga, Portugal

    Ana I. Pereira

  3. INESC TEC - INESC Technology and Science, Porto, Portugal

    José Lima

Authors
  1. Yahia Amoura
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ana I. Pereira
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. José Lima
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yahia Amoura .

Editor information

Editors and Affiliations

  1. Department of Industrial Electronics, University of Minho, Guimaraes, Portugal

    Joao L. Afonso

  2. Department of Industrial Electronics, University of Minho, Guimaraes, Portugal

    Vitor Monteiro

  3. University of Minho, Guimaraes, Portugal

    José Gabriel Pinto

Rights and permissions

Reprints and permissions

Copyright information

© 2022 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Amoura, Y., Pereira, A.I., Lima, J. (2022). A Short Term Wind Speed Forecasting Model Using Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System Models. In: Afonso, J.L., Monteiro, V., Pinto, J.G. (eds) Sustainable Energy for Smart Cities. SESC 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 425. Springer, Cham. https://doi.org/10.1007/978-3-030-97027-7_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-97027-7_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-97026-0

  • Online ISBN: 978-3-030-97027-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation