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Feature Selection by Binary Differential Evolution for Predicting the Energy Production of a Wind Plant

Author

Listed:
  • Sameer Al-Dahidi

    (Department of Mechanical and Maintenance Engineering, School of Applied Technical Sciences, German Jordanian University, Amman 11180, Jordan)

  • Piero Baraldi

    (Energy Department, Politecnico di Milano, Via Lambruschini 4, 20156 Milan, Italy)

  • Miriam Fresc

    (Energy Department, Politecnico di Milano, Via Lambruschini 4, 20156 Milan, Italy)

  • Enrico Zio

    (Energy Department, Politecnico di Milano, Via Lambruschini 4, 20156 Milan, Italy
    MINES-Paris, PSL University, CRC, 06904 Sophia Antipolis, France)

  • Lorenzo Montelatici

    (Research Development and Innovation, Edison Spa, 20121 Milan, Italy)

Abstract

We propose a method for selecting the optimal set of weather features for wind energy prediction. This problem is tackled by developing a wrapper approach that employs binary differential evolution to search for the best feature subset, and an ensemble of artificial neural networks to predict the energy production from a wind plant. The main novelties of the approach are the use of features provided by different weather forecast providers and the use of an ensemble composed of a reduced number of models for the wrapper search. Its effectiveness is verified using weather and energy production data collected from a 34 MW real wind plant. The model is built using the selected optimal subset of weather features and allows for (i) a 1% reduction in the mean absolute error compared with a model that considers all available features and a 4.4% reduction compared with the model currently employed by the plant owners, and (ii) a reduction in the number of selected features by 85% and 50%, respectively. Reducing the number of features boosts the prediction accuracy. The implication of this finding is significant as it allows plant owners to create profitable offers in the energy market and efficiently manage their power unit commitment, maintenance scheduling, and energy storage optimization.

Suggested Citation

  • Sameer Al-Dahidi & Piero Baraldi & Miriam Fresc & Enrico Zio & Lorenzo Montelatici, 2024. "Feature Selection by Binary Differential Evolution for Predicting the Energy Production of a Wind Plant," Energies, MDPI, vol. 17(10), pages 1-19, May.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:10:p:2424-:d:1397275
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    References listed on IDEAS

    as
    1. Hapfelmeier, A. & Ulm, K., 2013. "A new variable selection approach using Random Forests," Computational Statistics & Data Analysis, Elsevier, vol. 60(C), pages 50-69.
    2. Shufu Yuan & Yuzhang Ji & Yongxu Chen & Xin Liu & Weijun Zhang, 2023. "An Improved Differential Evolution for Parameter Identification of Photovoltaic Models," Sustainability, MDPI, vol. 15(18), pages 1-28, September.
    3. Abualkasim Bakeer & Gaber Magdy & Andrii Chub & Francisco Jurado & Mahmoud Rihan, 2022. "Optimal Ultra-Local Model Control Integrated with Load Frequency Control of Renewable Energy Sources Based Microgrids," Energies, MDPI, vol. 15(23), pages 1-20, December.
    4. 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.
    Full references (including those not matched with items on IDEAS)

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