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A New Prediction Model Based on Cascade NN for Wind Power Prediction

Author

Listed:
  • Amirhosein Torabi

    (University of Isfahan)

  • Sayyed Ali Kiaian Mousavy

    (Sharif University of Technology)

  • Vahideh Dashti

    (Islamic Azad University)

  • Mohammadhossein Saeedi

    (Texas Tech University)

  • Nasser Yousefi

    (Islamic Azad University)

Abstract

This paper presents a new prediction model based on empirical mode decomposition, feature selection and hybrid forecast engine. The whole structure of proposed model is based on nonstationarity and non-convex nature of wind power signal. The hybrid forecast engine consists of three main stages as; empirical mode decomposition, an intelligent algorithm and three stage neural network. All parameters of proposed neural network will be optimized by intelligent algorithm. Effectiveness of the proposed model is tested with real-world hourly data of wind farms in Canada, Spain and Texas. In order to demonstrate the validity of the proposed model, it is compared with several other wind speed and power forecast techniques where, different prediction horizons are considered from day-ahead to one week forecasting. Obtained results confirm the validity of the developed approach in prediction model for different forecast horizons.

Suggested Citation

  • Amirhosein Torabi & Sayyed Ali Kiaian Mousavy & Vahideh Dashti & Mohammadhossein Saeedi & Nasser Yousefi, 2019. "A New Prediction Model Based on Cascade NN for Wind Power Prediction," Computational Economics, Springer;Society for Computational Economics, vol. 53(3), pages 1219-1243, March.
    • Handle: RePEc:kap:compec:v:53:y:2019:i:3:d:10.1007_s10614-018-9795-8
    DOI: 10.1007/s10614-018-9795-8
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    References listed on IDEAS

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    1. Panayotis G. Michaelides & Efthymios G. Tsionas & Angelos T. Vouldis & Konstantinos N. Konstantakis & Panagiotis Patrinos, 2018. "A Semi-Parametric Non-linear Neural Network Filter: Theory and Empirical Evidence," Computational Economics, Springer;Society for Computational Economics, vol. 51(3), pages 637-675, March.
    2. Khurshid Kiani & Terry Kastens, 2008. "Testing Forecast Accuracy of Foreign Exchange Rates: Predictions from Feed Forward and Various Recurrent Neural Network Architectures," Computational Economics, Springer;Society for Computational Economics, vol. 32(4), pages 383-406, November.
    3. Liu, Yang & Wang, Wei & Ghadimi, Noradin, 2017. "Electricity load forecasting by an improved forecast engine for building level consumers," Energy, Elsevier, vol. 139(C), pages 18-30.
    4. A. Golbabai & A. Saeedi, 2015. "An Improved RBF Method for Solving Variational Problems Arising from Dynamic Economic Models," Computational Economics, Springer;Society for Computational Economics, vol. 46(2), pages 275-285, August.
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    Cited by:

    1. Brucke, Karoline & Arens, Stefan & Telle, Jan-Simon & Steens, Thomas & Hanke, Benedikt & von Maydell, Karsten & Agert, Carsten, 2021. "A non-intrusive load monitoring approach for very short-term power predictions in commercial buildings," Applied Energy, Elsevier, vol. 292(C).
    2. Yang, Mao & Wang, Da & Zhang, Wei, 2024. "A novel ultra short-term wind power prediction model based on double model coordination switching mechanism," Energy, Elsevier, vol. 289(C).
    3. Paweł Piotrowski & Inajara Rutyna & Dariusz Baczyński & Marcin Kopyt, 2022. "Evaluation Metrics for Wind Power Forecasts: A Comprehensive Review and Statistical Analysis of Errors," Energies, MDPI, vol. 15(24), pages 1-38, December.

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