IDEAS home Printed from https://ideas.repec.org/p/zbw/euvwdp/355.html
   My bibliography  Save this paper

A comparison of different wind power forecasting models to the Mycielski approach

Author

Listed:
  • Croonenbroeck, Carsten
  • Ambach, Daniel

Abstract

In the wind power industry, wind speed forecasts are obtained and transformed into wind power forecasts. The Mycielski algorithm has proven to be an accurate predictor for wind speed in short-term scenarios. Moreover, Mycielski has the capability of forecasting wind power directly, instead of wind speed. This article compares wind power forecasts calculated by the Mycielski algorithm to state-of-the-art forecasters. As such, we use the Wind Power Prediction Tool (WPPT) and the recently developed generalization of it, GWPPT (Generalized WPPT). Furthermore, we evaluate statistical time series models such as autoregressive and vector autoregressive models. As an additional benchmark we use the persistence model, which is often used to assess forecasting accuracy. Each model is evaluated and we give a recommendation for the best forecasting model.

Suggested Citation

  • Croonenbroeck, Carsten & Ambach, Daniel, 2014. "A comparison of different wind power forecasting models to the Mycielski approach," Discussion Papers 355, European University Viadrina Frankfurt (Oder), Department of Business Administration and Economics.
    • Handle: RePEc:zbw:euvwdp:355
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/98735/1/789540274.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Croonenbroeck, Carsten & Møller Dahl, Christian, 2014. "Accurate medium-term wind power forecasting in a censored classification framework," Discussion Papers 351, European University Viadrina Frankfurt (Oder), Department of Business Administration and Economics.
    2. Croonenbroeck, Carsten & Dahl, Christian Møller, 2014. "Accurate medium-term wind power forecasting in a censored classification framework," Energy, Elsevier, vol. 73(C), pages 221-232.
    3. Lei, Ma & Shiyan, Luan & Chuanwen, Jiang & Hongling, Liu & Yan, Zhang, 2009. "A review on the forecasting of wind speed and generated power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(4), pages 915-920, May.
    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. Croonenbroeck, Carsten & Stadtmann, Georg, 2015. "Minimizing asymmetric loss in medium-term wind power forecasting," Renewable Energy, Elsevier, vol. 81(C), pages 197-208.
    2. Croonenbroeck, Carsten & Ambach, Daniel, 2014. "Censored spatial wind power prediction with random effects," Discussion Papers 362, European University Viadrina Frankfurt (Oder), Department of Business Administration and Economics.
    3. Croonenbroeck, Carsten & Stadtmann, Georg, 2019. "Renewable generation forecast studies – Review and good practice guidance," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 312-322.
    4. Croonenbroeck, Carsten & Ambach, Daniel, 2015. "Censored spatial wind power prediction with random effects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 613-622.
    5. 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.
    6. Jannik Schütz Roungkvist & Peter Enevoldsen, 2020. "Timescale classification in wind forecasting: A review of the state‐of‐the‐art," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(5), pages 757-768, August.
    7. Croonenbroeck, Carsten & Hüttel, Silke, 2017. "Quantifying the economic efficiency impact of inaccurate renewable energy price forecasts," Energy, Elsevier, vol. 134(C), pages 767-774.
    8. Daniel Ambach & Carsten Croonenbroeck, 2016. "Space-time short- to medium-term wind speed forecasting," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 25(1), pages 5-20, March.
    9. Daniel Ambach & Carsten Croonenbroeck, 2016. "Space-time short- to medium-term wind speed forecasting," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 25(1), pages 5-20, March.
    10. Song, MengXuan & Wu, BingHeng & Chen, Kai & Zhang, Xing & Wang, Jun, 2016. "Simulating the wake flow effect of wind turbines on velocity and turbulence using particle random walk method," Energy, Elsevier, vol. 116(P1), pages 583-591.
    11. Piotr Powroźnik & Paweł Szcześniak & Łukasz Sobolewski & Krzysztof Piotrowski, 2022. "Novel Functionalities of Smart Home Devices for the Elastic Energy Management Algorithm," Energies, MDPI, vol. 15(22), pages 1-17, November.
    12. Marčiukaitis, Mantas & Žutautaitė, Inga & Martišauskas, Linas & Jokšas, Benas & Gecevičius, Giedrius & Sfetsos, Athanasios, 2017. "Non-linear regression model for wind turbine power curve," Renewable Energy, Elsevier, vol. 113(C), pages 732-741.
    13. Zhongrong Zhang & Yiliao Song & Feng Liu & Jinpeng Liu, 2016. "Daily Average Wind Power Interval Forecasts Based on an Optimal Adaptive-Network-Based Fuzzy Inference System and Singular Spectrum Analysis," Sustainability, MDPI, vol. 8(2), pages 1-30, January.
    14. María Pérez-Ortiz & Silvia Jiménez-Fernández & Pedro A. Gutiérrez & Enrique Alexandre & César Hervás-Martínez & Sancho Salcedo-Sanz, 2016. "A Review of Classification Problems and Algorithms in Renewable Energy Applications," Energies, MDPI, vol. 9(8), pages 1-27, August.
    15. García, Irene & Huo, Stella & Prado, Raquel & Bravo, Lelys, 2020. "Dynamic Bayesian temporal modeling and forecasting of short-term wind measurements," Renewable Energy, Elsevier, vol. 161(C), pages 55-64.
    16. Tascikaraoglu, Akin & Sanandaji, Borhan M. & Poolla, Kameshwar & Varaiya, Pravin, 2016. "Exploiting sparsity of interconnections in spatio-temporal wind speed forecasting using Wavelet Transform," Applied Energy, Elsevier, vol. 165(C), pages 735-747.
    17. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    18. Méndez-Gordillo, Alma Rosa & Cadenas, Erasmo, 2021. "Wind speed forecasting by the extraction of the multifractal patterns of time series through the multiplicative cascade technique," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    19. 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.
    20. Yuya Tanigawa & Narayanan Krishnan & Eitaro Oomine & Atushi Yona & Hiroshi Takahashi & Tomonobu Senjyu, 2023. "Clustering Method for Load Demand to Shorten the Time of Annual Simulation," Energies, MDPI, vol. 16(5), pages 1-22, February.

    More about this item

    Keywords

    Mycielski algorithm; WPPT; GWPPT; Wind Power; Wind Energy; Forecasting; Prediction;
    All these keywords.

    JEL classification:

    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:zbw:euvwdp:355. 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: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/fwffode.html .

    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.