IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v76y2015icp637-647.html
   My bibliography  Save this article

Daily wind speed forecasting through hybrid KF-ANN model based on ARIMA

Author

Listed:
  • Shukur, Osamah Basheer
  • Lee, Muhammad Hisyam

Abstract

The accuracy of wind speed forecasting is important to control, and optimize renewable wind power generation. The nonlinearity in the patterns of wind speed data is the reason of inaccurate wind speed forecasting using a linear autoregressive integrated moving average (ARIMA) model. The inaccurate forecasting of ARIMA model reflects the uncertainty of modelling process. The aim of this study is to improve the accuracy of wind speed forecasting by suggesting a more appropriate approach. An artificial neural network (ANN) and Kalman filter (KF) will be used to handle nonlinearity and uncertainty problems. Based on the ARIMA model, a hybrid KF-ANN model will improve the accuracy of wind speed forecasting. First, the effectiveness of ARIMA will be helped to determine the inputs structure for KF, ANN and their hybrid model. A case study will be carried out using daily wind speed data from Iraq and Malaysia. The hybrid KF-ANN model was the most adequate and provided the most accurate forecasts. In conclusion, the hybrid KF-ANN model will result in better wind speed forecasting accuracy than its separate components, while the KF model and ANN separately will be provide acceptable forecasts compared to ARIMA model that will provide ineffectual wind speed forecasts.

Suggested Citation

  • Shukur, Osamah Basheer & Lee, Muhammad Hisyam, 2015. "Daily wind speed forecasting through hybrid KF-ANN model based on ARIMA," Renewable Energy, Elsevier, vol. 76(C), pages 637-647.
  • Handle: RePEc:eee:renene:v:76:y:2015:i:c:p:637-647
    DOI: 10.1016/j.renene.2014.11.084
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148114008155
    Download Restriction: Full text for ScienceDirect subscribers only

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Guo, Zhenhai & Zhao, Weigang & Lu, Haiyan & Wang, Jianzhou, 2012. "Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model," Renewable Energy, Elsevier, vol. 37(1), pages 241-249.
    2. Xinxin Zhu & Marc G. Genton, 2012. "Short‐Term Wind Speed Forecasting for Power System Operations," International Statistical Review, International Statistical Institute, vol. 80(1), pages 2-23, April.
    3. Gould, Phillip G. & Koehler, Anne B. & Ord, J. Keith & Snyder, Ralph D. & Hyndman, Rob J. & Vahid-Araghi, Farshid, 2008. "Forecasting time series with multiple seasonal patterns," European Journal of Operational Research, Elsevier, vol. 191(1), pages 207-222, November.
    4. Jurate Saltyte Benth & Fred Espen Benth, 2010. "Analysis and modelling of wind speed in New York," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(6), pages 893-909.
    5. Cadenas, Erasmo & Rivera, Wilfrido, 2010. "Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA–ANN model," Renewable Energy, Elsevier, vol. 35(12), pages 2732-2738.
    6. Liu, Hui & Tian, Hong-qi & Li, Yan-fei, 2012. "Comparison of two new ARIMA-ANN and ARIMA-Kalman hybrid methods for wind speed prediction," Applied Energy, Elsevier, vol. 98(C), pages 415-424.
    7. Chen, Kuilin & Yu, Jie, 2014. "Short-term wind speed prediction using an unscented Kalman filter based state-space support vector regression approach," Applied Energy, Elsevier, vol. 113(C), pages 690-705.
    8. Cassola, Federico & Burlando, Massimiliano, 2012. "Wind speed and wind energy forecast through Kalman filtering of Numerical Weather Prediction model output," Applied Energy, Elsevier, vol. 99(C), pages 154-166.
    9. Peng, Huaiwu & Liu, Fangrui & Yang, Xiaofeng, 2013. "A hybrid strategy of short term wind power prediction," Renewable Energy, Elsevier, vol. 50(C), pages 590-595.
    10. Li, Gong & Shi, Jing, 2010. "On comparing three artificial neural networks for wind speed forecasting," Applied Energy, Elsevier, vol. 87(7), pages 2313-2320, July.
    11. Cadenas, Erasmo & Rivera, Wilfrido, 2007. "Wind speed forecasting in the South Coast of Oaxaca, México," Renewable Energy, Elsevier, vol. 32(12), pages 2116-2128.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. repec:gam:jeners:v:8:y:2015:i:12:p:14346-14360:d:60861 is not listed on IDEAS
    2. repec:eee:renene:v:113:y:2017:i:c:p:1345-1358 is not listed on IDEAS
    3. Ambach, Daniel & Schmid, Wolfgang, 2015. "Periodic and long range dependent models for high frequency wind speed data," Energy, Elsevier, vol. 82(C), pages 277-293.
    4. repec:eee:renene:v:116:y:2018:i:pa:p:669-684 is not listed on IDEAS
    5. Xuejiao Ma & Dandan Liu, 2016. "Comparative Study of Hybrid Models Based on a Series of Optimization Algorithms and Their Application in Energy System Forecasting," Energies, MDPI, Open Access Journal, vol. 9(8), pages 1-34, August.
    6. Reihani, Ehsan & Motalleb, Mahdi & Ghorbani, Reza & Saad Saoud, Lyes, 2016. "Load peak shaving and power smoothing of a distribution grid with high renewable energy penetration," Renewable Energy, Elsevier, vol. 86(C), pages 1372-1379.
    7. repec:eee:renene:v:118:y:2018:i:c:p:180-212 is not listed on IDEAS
    8. repec:gam:jeners:v:10:y:2017:i:9:p:1422-:d:112222 is not listed on IDEAS
    9. Tansu Filik, 2016. "Improved Spatio-Temporal Linear Models for Very Short-Term Wind Speed Forecasting," Energies, MDPI, Open Access Journal, vol. 9(3), pages 1-15, March.
    10. Wang, Yun & Wang, Jianzhou & Wei, Xiang, 2015. "A hybrid wind speed forecasting model based on phase space reconstruction theory and Markov model: A case study of wind farms in northwest China," Energy, Elsevier, vol. 91(C), pages 556-572.
    11. repec:gam:jeners:v:10:y:2017:i:10:p:1572-:d:114760 is not listed on IDEAS
    12. Qunli Wu & Chenyang Peng, 2015. "Wind Power Grid Connected Capacity Prediction Using LSSVM Optimized by the Bat Algorithm," Energies, MDPI, Open Access Journal, vol. 8(12), pages 1-15, December.
    13. Liu, Hui & Tian, Hong-qi & Liang, Xi-feng & Li, Yan-fei, 2015. "Wind speed forecasting approach using secondary decomposition algorithm and Elman neural networks," Applied Energy, Elsevier, vol. 157(C), pages 183-194.
    14. repec:eee:appene:v:197:y:2017:i:c:p:151-162 is not listed on IDEAS
    15. Sepasi, Saeed & Reihani, Ehsan & Howlader, Abdul M. & Roose, Leon R. & Matsuura, Marc M., 2017. "Very short term load forecasting of a distribution system with high PV penetration," Renewable Energy, Elsevier, vol. 106(C), pages 142-148.
    16. Ziel, Florian & Croonenbroeck, Carsten & Ambach, Daniel, 2016. "Forecasting wind power – Modeling periodic and non-linear effects under conditional heteroscedasticity," Applied Energy, Elsevier, vol. 177(C), pages 285-297.
    17. Rossi, Francesco & Velázquez, David, 2015. "A methodology for energy savings verification in industry with application for a CHP (combined heat and power) plant," Energy, Elsevier, vol. 89(C), pages 528-544.
    18. Akçay, Hüseyin & Filik, Tansu, 2017. "Short-term wind speed forecasting by spectral analysis from long-term observations with missing values," Applied Energy, Elsevier, vol. 191(C), pages 653-662.
    19. repec:gam:jeners:v:9:y:2016:i:3:p:168:d:65196 is not listed on IDEAS
    20. Chang, G.W. & Lu, H.J. & Chang, Y.R. & Lee, Y.D., 2017. "An improved neural network-based approach for short-term wind speed and power forecast," Renewable Energy, Elsevier, vol. 105(C), pages 301-311.
    21. Yuqi Dong & Xuejiao Ma & Chenchen Ma & Jianzhou Wang, 2016. "Research and Application of a Hybrid Forecasting Model Based on Data Decomposition for Electrical Load Forecasting," Energies, MDPI, Open Access Journal, vol. 9(12), pages 1-30, December.
    22. Zhilong Wang & Chen Wang & Jie Wu, 2016. "Wind Energy Potential Assessment and Forecasting Research Based on the Data Pre-Processing Technique and Swarm Intelligent Optimization Algorithms," Sustainability, MDPI, Open Access Journal, vol. 8(11), pages 1-32, November.
    23. Arunraj, Nari Sivanandam & Ahrens, Diane, 2015. "A hybrid seasonal autoregressive integrated moving average and quantile regression for daily food sales forecasting," International Journal of Production Economics, Elsevier, vol. 170(PA), pages 321-335.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:renene:v:76:y:2015:i:c:p:637-647. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.