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Short-Term Energy Generation Forecasts at a Wind Farm—A Multi-Variant Comparison of the Effectiveness and Performance of Various Gradient-Boosted Decision Tree Models

Author

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  • Marcin Kopyt

    (Electrical Power Engineering Institute, Warsaw University of Technology, Koszykowa 75 Street, 00-662 Warsaw, Poland)

  • Paweł Piotrowski

    (Electrical Power Engineering Institute, Warsaw University of Technology, Koszykowa 75 Street, 00-662 Warsaw, Poland)

  • Dariusz Baczyński

    (Electrical Power Engineering Institute, Warsaw University of Technology, Koszykowa 75 Street, 00-662 Warsaw, Poland)

Abstract

High-quality short-term forecasts of wind farm generation are crucial for the dynamically developing renewable energy generation sector. This article addresses the selection of appropriate gradient-boosted decision tree models (GBDT) for forecasting wind farm energy generation with a 10-min time horizon. In most forecasting studies, authors utilize a single gradient-boosted decision tree model and compare its performance with other machine learning (ML) techniques and sometimes with a naive baseline model. This paper proposes a comprehensive comparison of all gradient-boosted decision tree models (GBDTs, eXtreme Gradient Boosting (XGBoost), Light Gradient-Boosting Machine (LightGBM), and Categorical Boosting (CatBoost)) used for forecasting. The objective is to evaluate each model in terms of forecasting accuracy for wind farm energy generation (forecasting error) and computational time during model training. Computational time is a critical factor due to the necessity of testing numerous models with varying hyperparameters to identify the optimal settings that minimize forecasting error. Forecast quality using default hyperparameters is used here as a reference. The research also seeks to determine the most effective sets of input variables for the predictive models. The article concludes with findings and recommendations regarding the preferred GBDT models. Among the four tested models, the oldest GBDT model demonstrated a significantly longer training time, which should be considered a major drawback of this implementation of gradient-boosted decision trees. In terms of model quality testing, the lowest nRMSE error was achieved by the oldest model—GBDT in its tuned version (with the best hyperparameter values obtained from exploring 40,000 combinations).

Suggested Citation

  • Marcin Kopyt & Paweł Piotrowski & Dariusz Baczyński, 2024. "Short-Term Energy Generation Forecasts at a Wind Farm—A Multi-Variant Comparison of the Effectiveness and Performance of Various Gradient-Boosted Decision Tree Models," Energies, MDPI, vol. 17(23), pages 1-21, December.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:23:p:6194-:d:1539447
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    References listed on IDEAS

    as
    1. G. Ponkumar & S. Jayaprakash & Karthick Kanagarathinam, 2023. "Advanced Machine Learning Techniques for Accurate Very-Short-Term Wind Power Forecasting in Wind Energy Systems Using Historical Data Analysis," Energies, MDPI, vol. 16(14), pages 1-24, July.
    2. Yingya Zhou & Linwei Ma & Weidou Ni & Colin Yu, 2023. "Data Enrichment as a Method of Data Preprocessing to Enhance Short-Term Wind Power Forecasting," Energies, MDPI, vol. 16(5), pages 1-18, February.
    3. Eric Stefan Miele & Nicole Ludwig & Alessandro Corsini, 2023. "Multi-Horizon Wind Power Forecasting Using Multi-Modal Spatio-Temporal Neural Networks," Energies, MDPI, vol. 16(8), pages 1-15, April.
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