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A data-driven deep sequence-to-sequence long-short memory method along with a gated recurrent neural network for wind power forecasting

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  • Ahmad, Tanveer
  • Zhang, Dongdong

Abstract

Large amounts of wind power generation have an impact not only on energy markets but also on wholesale and retail market designs. Simultaneously, technological issues arise as a result of the need to ensure the smooth operation of the power grid. In the long run, high-quality wind data series are required to generate model results that lead to robust policy advice. Several techniques are commonly used to forecast short-term, nonstationary, and nonlinear wind speeds. These techniques are lacking in model optimization and data processing abilities. This lack of expertise is posing a significant challenge for reliable and stable wind power forecasting. Furthermore, the medium and long-term forecasting criteria for models (e.g., robustness, accuracy, speed) are higher, making it difficult to obtain reliable results. This study used the extended deep sequence-to-sequence long short-term memory regression (STSR-LSTM) model for time-series wind power forecasting to overcome these challenges for accurate forecasting decisions. The statistical-learning technique is used to improve the dependability of the derived features as well as the expected performance. Three different locations/sites were used and analyzed (e.g., Belgian, DSO-Connected, Elia), three different forecasting classes (e.g., week-ahead forecast, day-ahead forecast, and most recent forecast), three different seasons (e.g., monthly, seasonal, and annual), and three experimental setups. The performance of Deep STSR-LSTM is validated using two existing models and four performance evaluation indexes. Despite the fact that the input wind power load curve had a lot of variation, we were able to achieve higher forecast accuracy. Using different sites and classes, similar forecast accuracy was achieved for each season (e.g., monthly, seasonal, and annual).

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  • Ahmad, Tanveer & Zhang, Dongdong, 2022. "A data-driven deep sequence-to-sequence long-short memory method along with a gated recurrent neural network for wind power forecasting," Energy, Elsevier, vol. 239(PB).
  • Handle: RePEc:eee:energy:v:239:y:2022:i:pb:s0360544221023574
    DOI: 10.1016/j.energy.2021.122109
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    References listed on IDEAS

    as
    1. Sun, Mucun & Feng, Cong & Zhang, Jie, 2019. "Conditional aggregated probabilistic wind power forecasting based on spatio-temporal correlation," Applied Energy, Elsevier, vol. 256(C).
    2. Sun, Mucun & Feng, Cong & Zhang, Jie, 2020. "Multi-distribution ensemble probabilistic wind power forecasting," Renewable Energy, Elsevier, vol. 148(C), pages 135-149.
    3. Ahmad, Tanveer & Zhang, Dongdong & Huang, Chao, 2021. "Methodological framework for short-and medium-term energy, solar and wind power forecasting with stochastic-based machine learning approach to monetary and energy policy applications," Energy, Elsevier, vol. 231(C).
    4. Meka, Rajitha & Alaeddini, Adel & Bhaganagar, Kiran, 2021. "A robust deep learning framework for short-term wind power forecast of a full-scale wind farm using atmospheric variables," Energy, Elsevier, vol. 221(C).
    5. Hu, Huanling & Wang, Lin & Lv, Sheng-Xiang, 2020. "Forecasting energy consumption and wind power generation using deep echo state network," Renewable Energy, Elsevier, vol. 154(C), pages 598-613.
    6. Liu, Hui & Chen, Chao, 2019. "Data processing strategies in wind energy forecasting models and applications: A comprehensive review," Applied Energy, Elsevier, vol. 249(C), pages 392-408.
    7. Antonio Bracale & Pasquale De Falco, 2015. "An Advanced Bayesian Method for Short-Term Probabilistic Forecasting of the Generation of Wind Power," Energies, MDPI, vol. 8(9), pages 1-22, September.
    8. Niu, Zhewen & Yu, Zeyuan & Tang, Wenhu & Wu, Qinghua & Reformat, Marek, 2020. "Wind power forecasting using attention-based gated recurrent unit network," Energy, Elsevier, vol. 196(C).
    9. Wang, Huai-zhi & Li, Gang-qiang & Wang, Gui-bin & Peng, Jian-chun & Jiang, Hui & Liu, Yi-tao, 2017. "Deep learning based ensemble approach for probabilistic wind power forecasting," Applied Energy, Elsevier, vol. 188(C), pages 56-70.
    10. Jafarzadeh Ghoushchi, Saeid & Manjili, Sobhan & Mardani, Abbas & Saraji, Mahyar Kamali, 2021. "An extended new approach for forecasting short-term wind power using modified fuzzy wavelet neural network: A case study in wind power plant," Energy, Elsevier, vol. 223(C).
    11. Tsao, Hao-Han & Leu, Yih-Guang & Chou, Li-Fen, 2021. "A center-of-concentrated-based prediction interval for wind power forecasting," Energy, Elsevier, vol. 237(C).
    12. Calif, Rudy & Schmitt, François G. & Huang, Yongxiang, 2013. "Multifractal description of wind power fluctuations using arbitrary order Hilbert spectral analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(18), pages 4106-4120.
    13. Hong, Ying-Yi & Rioflorido, Christian Lian Paulo P., 2019. "A hybrid deep learning-based neural network for 24-h ahead wind power forecasting," Applied Energy, Elsevier, vol. 250(C), pages 530-539.
    14. Erick López & Carlos Valle & Héctor Allende & Esteban Gil & Henrik Madsen, 2018. "Wind Power Forecasting Based on Echo State Networks and Long Short-Term Memory," Energies, MDPI, vol. 11(3), pages 1-22, February.
    15. Prósper, Miguel A. & Otero-Casal, Carlos & Fernández, Felipe Canoura & Miguez-Macho, Gonzalo, 2019. "Wind power forecasting for a real onshore wind farm on complex terrain using WRF high resolution simulations," Renewable Energy, Elsevier, vol. 135(C), pages 674-686.
    16. Zhang, Yu & Li, Yanting & Zhang, Guangyao, 2020. "Short-term wind power forecasting approach based on Seq2Seq model using NWP data," Energy, Elsevier, vol. 213(C).
    Full references (including those not matched with items on IDEAS)

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