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Censored spatial wind power prediction with random effects

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  • Croonenbroeck, Carsten
  • Ambach, Daniel

Abstract

We investigate the importance of taking the spatial interaction of turbines inside a wind park into account for power forecasting. This paper provides two tests that check for spatial interdependence such as wake effects. Those effects are suspected to have a negative influence on wind power production. After that, we introduce a new modeling approach that is based on the generalized wind power prediction tool (GWPPT) and therefore respect both-sided censoring of the data. The new model makes use of a spatial lag model (SLM) specification and allows for random effects in the panel data. Finally, we provide a short empirical study that compares the forecasting accuracy of our model to the established models WPPT, GWPPT, and the naïve persistence predictor. We show that our new model provides significantly better forecasts than the established models.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:rensus:v:51:y:2015:i:c:p:613-622
    DOI: 10.1016/j.rser.2015.06.047
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    References listed on IDEAS

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    1. 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.
    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.
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    6. 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.
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    9. Jursa, René & Rohrig, Kurt, 2008. "Short-term wind power forecasting using evolutionary algorithms for the automated specification of artificial intelligence models," International Journal of Forecasting, Elsevier, vol. 24(4), pages 694-709.
    10. 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.
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    Cited by:

    1. Matthias Ritter & Simone Pieralli & Martin Odening, 2017. "Neighborhood Effects in Wind Farm Performance: A Regression Approach," Energies, MDPI, vol. 10(3), pages 1-16, March.
    2. Yang, Mao & Huang, Yutong & Xu, Chuanyu & Liu, Chenyu & Dai, Bozhi, 2025. "Review of several key processes in wind power forecasting: Mathematical formulations, scientific problems, and logical relations," Applied Energy, Elsevier, vol. 377(PC).
    3. 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.
    4. De Siano, Rita & Sapio, Alessandro, 2022. "Spatial merit order effects of renewables in the Italian power exchange," Energy Economics, Elsevier, vol. 108(C).
    5. repec:hum:wpaper:sfb649dp2016-012 is not listed on IDEAS
    6. 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.
    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. Ritter, Matthias & Pieralli, Simone & Odening, Martin, 2016. "Neighborhood effects in wind farm performance: An econometric approach," SFB 649 Discussion Papers 2016-012, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

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