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Bootstrapped Ensemble of Artificial Neural Networks Technique for Quantifying Uncertainty in Prediction of Wind Energy Production

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 La Masa 34, 20156 Milan, Italy)

  • Enrico Zio

    (Energy Department, Politecnico di Milano, Via La Masa 34, 20156 Milan, Italy
    MINES ParisTech, PSL Research University, CRC, 06560 Sophia Antipolis, France
    Department of Nuclear Engineering, Eminent Scholar, College of Engineering, Kyung Hee University, Seoul 130-701, Korea)

  • Lorenzo Montelatici

    (Research Development and Innovation, Edison Spa, Foro Buonaparte 31, 20121 Milan, Italy)

Abstract

The accurate prediction of wind energy production is crucial for an affordable and reliable power supply to consumers. Prediction models are used as decision-aid tools for electric grid operators to dynamically balance the energy production provided by a pool of diverse sources in the energy mix. However, different sources of uncertainty affect the predictions, providing the decision-makers with non-accurate and possibly misleading information for grid operation. In this regard, this work aims to quantify the possible sources of uncertainty that affect the predictions of wind energy production provided by an ensemble of Artificial Neural Network (ANN) models. The proposed Bootstrap (BS) technique for uncertainty quantification relies on estimating Prediction Intervals (PIs) for a predefined confidence level. The capability of the proposed BS technique is verified, considering a 34 MW wind plant located in Italy. The obtained results show that the BS technique provides a more satisfactory quantification of the uncertainty of wind energy predictions than that of a technique adopted by the wind plant owner and the Mean-Variance Estimation (MVE) technique of literature. The PIs obtained by the BS technique are also analyzed in terms of different weather conditions experienced by the wind plant and time horizons of prediction.

Suggested Citation

  • Sameer Al-Dahidi & Piero Baraldi & Enrico Zio & Lorenzo Montelatici, 2021. "Bootstrapped Ensemble of Artificial Neural Networks Technique for Quantifying Uncertainty in Prediction of Wind Energy Production," Sustainability, MDPI, vol. 13(11), pages 1-19, June.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:11:p:6417-:d:569086
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    References listed on IDEAS

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    2. Suqi Zhang & Ningjing Zhang & Ziqi Zhang & Ying Chen, 2022. "Electric Power Load Forecasting Method Based on a Support Vector Machine Optimized by the Improved Seagull Optimization Algorithm," Energies, MDPI, vol. 15(23), pages 1-17, December.

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