IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v145y2015icp191-197.html
   My bibliography  Save this article

Recursive wind speed forecasting based on Hammerstein Auto-Regressive model

Author

Listed:
  • Ait Maatallah, Othman
  • Achuthan, Ajit
  • Janoyan, Kerop
  • Marzocca, Pier

Abstract

A new Wind Speed Forecasting (WSF) model, suitable for a short term 1–24h forecast horizon, is developed by adapting Hammerstein model to an Autoregressive approach. The model is applied to real data collected for a period of three years (2004–2006) from two different sites. The performance of HAR model is evaluated by comparing its prediction with the classical Autoregressive Integrated Moving Average (ARIMA) model and a multi-layer perceptron Artificial Neural Network (ANN). Results show that the HAR model outperforms both the ARIMA model and ANN model in terms of root mean square error (RMSE), mean absolute error (MAE), and Mean Absolute Percentage Error (MAPE). When compared to the conventional models, the new HAR model can better capture various wind speed characteristics, including asymmetric (non-gaussian) wind speed distribution, non-stationary time series profile, and the chaotic dynamics. The new model is beneficial for various applications in the renewable energy area, particularly for power scheduling.

Suggested Citation

  • Ait Maatallah, Othman & Achuthan, Ajit & Janoyan, Kerop & Marzocca, Pier, 2015. "Recursive wind speed forecasting based on Hammerstein Auto-Regressive model," Applied Energy, Elsevier, vol. 145(C), pages 191-197.
    • Handle: RePEc:eee:appene:v:145:y:2015:i:c:p:191-197
    DOI: 10.1016/j.apenergy.2015.02.032
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261915002093
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2015.02.032?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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. Cadenas, Erasmo & Rivera, Wilfrido, 2009. "Short term wind speed forecasting in La Venta, Oaxaca, México, using artificial neural networks," Renewable Energy, Elsevier, vol. 34(1), pages 274-278.
    2. Jung, Jaesung & Broadwater, Robert P., 2014. "Current status and future advances for wind speed and power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 762-777.
    3. Liu, Heping & Shi, Jing & Erdem, Ergin, 2010. "Prediction of wind speed time series using modified Taylor Kriging method," Energy, Elsevier, vol. 35(12), pages 4870-4879.
    4. 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.
    5. Adeodato, Paulo J.L. & Arnaud, Adrian L. & Vasconcelos, Germano C. & Cunha, Rodrigo C.L.V. & Monteiro, Domingos S.M.P., 2011. "MLP ensembles improve long term prediction accuracy over single networks," International Journal of Forecasting, Elsevier, vol. 27(3), pages 661-671, July.
    6. Kavasseri, Rajesh G. & Seetharaman, Krithika, 2009. "Day-ahead wind speed forecasting using f-ARIMA models," Renewable Energy, Elsevier, vol. 34(5), pages 1388-1393.
    7. Mirzaee, Hossein, 2009. "Long-term prediction of chaotic time series with multi-step prediction horizons by a neural network with Levenberg–Marquardt learning algorithm," Chaos, Solitons & Fractals, Elsevier, vol. 41(4), pages 1975-1979.
    8. Sfetsos, A., 2000. "A comparison of various forecasting techniques applied to mean hourly wind speed time series," Renewable Energy, Elsevier, vol. 21(1), pages 23-35.
    9. Adeodato, Paulo J.L. & Arnaud, Adrian L. & Vasconcelos, Germano C. & Cunha, Rodrigo C.L.V. & Monteiro, Domingos S.M.P., 2011. "MLP ensembles improve long term prediction accuracy over single networks," International Journal of Forecasting, Elsevier, vol. 27(3), pages 661-671.
    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. Jiang, Yu & Song, Zhe & Kusiak, Andrew, 2013. "Very short-term wind speed forecasting with Bayesian structural break model," Renewable Energy, Elsevier, vol. 50(C), pages 637-647.
    12. Wang, Shuai & Yu, Lean & Tang, Ling & Wang, Shouyang, 2011. "A novel seasonal decomposition based least squares support vector regression ensemble learning approach for hydropower consumption forecasting in China," Energy, Elsevier, vol. 36(11), pages 6542-6554.
    13. Hong, Ying-Yi & Chang, Huei-Lin & Chiu, Ching-Sheng, 2010. "Hour-ahead wind power and speed forecasting using simultaneous perturbation stochastic approximation (SPSA) algorithm and neural network with fuzzy inputs," Energy, Elsevier, vol. 35(9), pages 3870-3876.
    14. Cadenas, Erasmo & Rivera, Wilfrido, 2007. "Wind speed forecasting in the South Coast of Oaxaca, México," Renewable Energy, Elsevier, vol. 32(12), pages 2116-2128.
    15. 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.
    16. Guerra, Fábio A. & Coelho, Leandro dos S., 2008. "Multi-step ahead nonlinear identification of Lorenz’s chaotic system using radial basis neural network with learning by clustering and particle swarm optimization," Chaos, Solitons & Fractals, Elsevier, vol. 35(5), pages 967-979.
    17. Liu, Hui & Tian, Hong-qi & Pan, Di-fu & Li, Yan-fei, 2013. "Forecasting models for wind speed using wavelet, wavelet packet, time series and Artificial Neural Networks," Applied Energy, Elsevier, vol. 107(C), pages 191-208.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Jianzhou & Qin, Shanshan & Zhou, Qingping & Jiang, Haiyan, 2015. "Medium-term wind speeds forecasting utilizing hybrid models for three different sites in Xinjiang, China," Renewable Energy, Elsevier, vol. 76(C), pages 91-101.
    2. Tascikaraoglu, A. & Uzunoglu, M., 2014. "A review of combined approaches for prediction of short-term wind speed and power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 243-254.
    3. Sandra Minerva Valdivia-Bautista & José Antonio Domínguez-Navarro & Marco Pérez-Cisneros & Carlos Jesahel Vega-Gómez & Beatriz Castillo-Téllez, 2023. "Artificial Intelligence in Wind Speed Forecasting: A Review," Energies, MDPI, vol. 16(5), pages 1-28, March.
    4. Wang, Jianzhou & Xiong, Shenghua, 2014. "A hybrid forecasting model based on outlier detection and fuzzy time series – A case study on Hainan wind farm of China," Energy, Elsevier, vol. 76(C), pages 526-541.
    5. Zhao, Weigang & Wei, Yi-Ming & Su, Zhongyue, 2016. "One day ahead wind speed forecasting: A resampling-based approach," Applied Energy, Elsevier, vol. 178(C), pages 886-901.
    6. Rana Muhammad Adnan & Zhongmin Liang & Xiaohui Yuan & Ozgur Kisi & Muhammad Akhlaq & Binquan Li, 2019. "Comparison of LSSVR, M5RT, NF-GP, and NF-SC Models for Predictions of Hourly Wind Speed and Wind Power Based on Cross-Validation," Energies, MDPI, vol. 12(2), pages 1-22, January.
    7. Niu, Tong & Wang, Jianzhou & Zhang, Kequan & Du, Pei, 2018. "Multi-step-ahead wind speed forecasting based on optimal feature selection and a modified bat algorithm with the cognition strategy," Renewable Energy, Elsevier, vol. 118(C), pages 213-229.
    8. Zhao, Yongning & Ye, Lin & Li, Zhi & Song, Xuri & Lang, Yansheng & Su, Jian, 2016. "A novel bidirectional mechanism based on time series model for wind power forecasting," Applied Energy, Elsevier, vol. 177(C), pages 793-803.
    9. Zhang, Chi & Wei, Haikun & Zhao, Junsheng & Liu, Tianhong & Zhu, Tingting & Zhang, Kanjian, 2016. "Short-term wind speed forecasting using empirical mode decomposition and feature selection," Renewable Energy, Elsevier, vol. 96(PA), pages 727-737.
    10. Wang, Jian-Zhou & Wang, Yun & Jiang, Ping, 2015. "The study and application of a novel hybrid forecasting model – A case study of wind speed forecasting in China," Applied Energy, Elsevier, vol. 143(C), pages 472-488.
    11. 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.
    12. Ata, Rasit, 2015. "Artificial neural networks applications in wind energy systems: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 534-562.
    13. Wu, Xuedong & Zhu, Zhiyu & Su, Xunliang & Fan, Shaosheng & Du, Zhaoping & Chang, Yanchao & Zeng, Qingjun, 2015. "A study of single multiplicative neuron model with nonlinear filters for hourly wind speed prediction," Energy, Elsevier, vol. 88(C), pages 194-201.
    14. Jiang, Ping & Wang, Yun & Wang, Jianzhou, 2017. "Short-term wind speed forecasting using a hybrid model," Energy, Elsevier, vol. 119(C), pages 561-577.
    15. Jung, Jaesung & Broadwater, Robert P., 2014. "Current status and future advances for wind speed and power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 762-777.
    16. Liu, Hui & Tian, Hong-qi & Pan, Di-fu & Li, Yan-fei, 2013. "Forecasting models for wind speed using wavelet, wavelet packet, time series and Artificial Neural Networks," Applied Energy, Elsevier, vol. 107(C), pages 191-208.
    17. Yu, Jie & Chen, Kuilin & Mori, Junichi & Rashid, Mudassir M., 2013. "A Gaussian mixture copula model based localized Gaussian process regression approach for long-term wind speed prediction," Energy, Elsevier, vol. 61(C), pages 673-686.
    18. Song, Jingjing & Wang, Jianzhou & Lu, Haiyan, 2018. "A novel combined model based on advanced optimization algorithm for short-term wind speed forecasting," Applied Energy, Elsevier, vol. 215(C), pages 643-658.
    19. Ramasamy, P. & Chandel, S.S. & Yadav, Amit Kumar, 2015. "Wind speed prediction in the mountainous region of India using an artificial neural network model," Renewable Energy, Elsevier, vol. 80(C), pages 338-347.
    20. Erasmo Cadenas & Wilfrido Rivera & Rafael Campos-Amezcua & Christopher Heard, 2016. "Wind Speed Prediction Using a Univariate ARIMA Model and a Multivariate NARX Model," Energies, MDPI, vol. 9(2), pages 1-15, February.

    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:appene:v:145:y:2015:i:c:p:191-197. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.