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

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  • 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
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    References listed on IDEAS

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    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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    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.
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