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A Novel and Alternative Approach for Direct and Indirect Wind-Power Prediction Methods

Author

Listed:
  • Neeraj Bokde

    (Department of Electronics and Communication Engineering, Visvesvaraya National Institute of Technology, Nagpur 440010, India
    These authors contributed equally to this work.)

  • Andrés Feijóo

    (Departamento de Enxeñería Eléctrica-Universidade de Vigo, Campus de Lagoas-Marcosende, 36310 Vigo, Spain
    These authors contributed equally to this work.)

  • Daniel Villanueva

    (Departamento de Enxeñería Eléctrica-Universidade de Vigo, Campus de Lagoas-Marcosende, 36310 Vigo, Spain
    These authors contributed equally to this work.)

  • Kishore Kulat

    (Department of Electronics and Communication Engineering, Visvesvaraya National Institute of Technology, Nagpur 440010, India
    These authors contributed equally to this work.)

Abstract

Wind energy is a variable energy source with a growing presence in many electrical networks across the world. Wind-speed prediction has become an important tool for many agents involved in energy markets. In this paper, an approach to this problem is proposed by means of a novel method that outperforms results obtained by current direct and indirect wind-power prediction procedures. The first difference is that it is not strictly a direct or indirect method in the conventional sense because it uses information from both wind-speed and wind-power data series to obtain a wind-power series. The second difference is that it smooths down the wind-power series obtained in the first stage, and uses the resulting series for predicting new wind-power values. The process of smoothing is based on the label sequence generation process discussed in the pattern sequence forecasting algorithm and the Naive Bayesian method-based matching process. The result is a less chaotic way to predict wind speed than those offered by other existing methods. It has been assessed in multiple simulations, for which three different error measures have been used.

Suggested Citation

  • Neeraj Bokde & Andrés Feijóo & Daniel Villanueva & Kishore Kulat, 2018. "A Novel and Alternative Approach for Direct and Indirect Wind-Power Prediction Methods," Energies, MDPI, vol. 11(11), pages 1-19, October.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:11:p:2923-:d:178573
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    References listed on IDEAS

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    Cited by:

    1. Yonggang Li & Yue Wang & Binyuan Wu, 2020. "Short-Term Direct Probability Prediction Model of Wind Power Based on Improved Natural Gradient Boosting," Energies, MDPI, vol. 13(18), pages 1-15, September.
    2. Ning Li & Fuxing He & Wentao Ma, 2019. "Wind Power Prediction Based on Extreme Learning Machine with Kernel Mean p -Power Error Loss," Energies, MDPI, vol. 12(4), pages 1-19, February.
    3. Neeraj Bokde & Andrés Feijóo & Nadhir Al-Ansari & Siyu Tao & Zaher Mundher Yaseen, 2020. "The Hybridization of Ensemble Empirical Mode Decomposition with Forecasting Models: Application of Short-Term Wind Speed and Power Modeling," Energies, MDPI, vol. 13(7), pages 1-23, April.
    4. Honghai Niu & Yu Yang & Lingchao Zeng & Yiguo Li, 2021. "ELM-QR-Based Nonparametric Probabilistic Prediction Method for Wind Power," Energies, MDPI, vol. 14(3), pages 1-15, January.
    5. Kirchner-Bossi, Nicolas & Kathari, Gabriel & Porté-Agel, Fernando, 2024. "A hybrid physics-based and data-driven model for intra-day and day-ahead wind power forecasting considering a drastically expanded predictor search space," Applied Energy, Elsevier, vol. 367(C).
    6. Yakoub, Ghali & Mathew, Sathyajith & Leal, Joao, 2023. "Intelligent estimation of wind farm performance with direct and indirect ‘point’ forecasting approaches integrating several NWP models," Energy, Elsevier, vol. 263(PD).
    7. Sobolewski, Robert Adam & Tchakorom, Médane & Couturier, Raphaël, 2023. "Gradient boosting-based approach for short- and medium-term wind turbine output power prediction," Renewable Energy, Elsevier, vol. 203(C), pages 142-160.

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