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Smart Urban Wind Power Forecasting: Integrating Weibull Distribution, Recurrent Neural Networks, and Numerical Weather Prediction

Author

Listed:
  • Navid Shirzadi

    (Gina Cody School of Engineering and Computer Science, Concordia University, 1455 Boulevard de Maisonneuve, Montreal, QC H3G 1M8, Canada)

  • Fuzhan Nasiri

    (Gina Cody School of Engineering and Computer Science, Concordia University, 1455 Boulevard de Maisonneuve, Montreal, QC H3G 1M8, Canada)

  • Ramanunni Parakkal Menon

    (Gina Cody School of Engineering and Computer Science, Concordia University, 1455 Boulevard de Maisonneuve, Montreal, QC H3G 1M8, Canada)

  • Pilar Monsalvete

    (Gina Cody School of Engineering and Computer Science, Concordia University, 1455 Boulevard de Maisonneuve, Montreal, QC H3G 1M8, Canada)

  • Anton Kaifel

    (Centre for Solar Energy and Hydrogen Research (ZSW), Meitnerstr. 1, 70563 Stuttgart, Germany)

  • Ursula Eicker

    (Gina Cody School of Engineering and Computer Science, Concordia University, 1455 Boulevard de Maisonneuve, Montreal, QC H3G 1M8, Canada)

Abstract

The design, operational planning, and integration of wind power plants with other renewables and the grid face challenges attributed to the intermittent nature of wind power generation. Addressing this issue necessitates the development of a smart wind power (and in particular wind speed) forecasting approach. This is a complex task due to substantial fluctuations in wind speed. To overcome the inherent stochastic nature of wind speed and mitigate related challenges, traditionally, numerical weather prediction (NWP) models are employed for wind speed forecasting. However, the applicability of NWP models is limited to short-term forecasting due to their computational constraints. In this study, a hybrid AI-based approach is proposed to improve forecast accuracy over a 48 h horizon for the city of Montreal. The results demonstrate that by integrating the probability distribution of wind speed with a deep learning model, the forecasted values align closely with the observed values in terms of seasonality and trend, exhibiting enhanced accuracy. Evaluation metrics reveal a substantial reduction in the root mean squared error (13–31%) across three prediction horizons (summer, fall, and winter) compared to a single long, short-term memory model. Furthermore, integrating the improved model with the numerical weather prediction model yields increased accuracy and decreased error compared to the LSTM–Weibull model.

Suggested Citation

  • Navid Shirzadi & Fuzhan Nasiri & Ramanunni Parakkal Menon & Pilar Monsalvete & Anton Kaifel & Ursula Eicker, 2023. "Smart Urban Wind Power Forecasting: Integrating Weibull Distribution, Recurrent Neural Networks, and Numerical Weather Prediction," Energies, MDPI, vol. 16(17), pages 1-17, August.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:17:p:6208-:d:1225960
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    References listed on IDEAS

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    Cited by:

    1. Dongran Song & Xiao Tan & Qian Huang & Li Wang & Mi Dong & Jian Yang & Solomin Evgeny, 2024. "Review of AI-Based Wind Prediction within Recent Three Years: 2021–2023," Energies, MDPI, vol. 17(6), pages 1-22, March.
    2. Dimitrios Michos & Francky Catthoor & Dimitris Foussekis & Andreas Kazantzidis, 2024. "Ultra-Short-Term Wind Power Forecasting in Complex Terrain: A Physics-Based Approach," Energies, MDPI, vol. 17(21), pages 1-20, November.

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