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A novel deep interval prediction model with adaptive interval construction strategy and automatic hyperparameter tuning for wind speed forecasting

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  • Xie, Yuying
  • Li, Chaoshun
  • Tang, Geng
  • Liu, Fangjie

Abstract

Wind energy is a renewable energy source with great development potential. However, its inherent instability and randomness have brought great challenges to the maximum utilization of wind energy. Wind speed forecasting is one of the most effective ways to mitigate these challenges, which plays an important role in the operational management and decision-making of wind power system operators. In this study, a novel wind speed interval prediction model based on gated recurrent unit, Variational Mode Decomposition, and Particle Swarm Optimization was proposed. The original wind speed sequence was decomposed into several smoother sub-sequences through the Variational Mode Decomposition algorithm, and corresponding sub-models were established based on the gated recurrent unit. To better supervise the training process, artificial prediction intervals with adaptive adjustment strategies were devised. Moreover, the Particle Swarm Optimization algorithm was adopted to search for the optimal superposition weights of PIs to achieve the integral optimization of the model. The qualitative and quantitative performance of the proposed method has been fully tested and verified in a series of real cases.

Suggested Citation

  • Xie, Yuying & Li, Chaoshun & Tang, Geng & Liu, Fangjie, 2021. "A novel deep interval prediction model with adaptive interval construction strategy and automatic hyperparameter tuning for wind speed forecasting," Energy, Elsevier, vol. 216(C).
    • Handle: RePEc:eee:energy:v:216:y:2021:i:c:s0360544220322866
    DOI: 10.1016/j.energy.2020.119179
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    2. Khodayar, Mahdi & Saffari, Mohsen & Williams, Michael & Jalali, Seyed Mohammad Jafar, 2022. "Interval deep learning architecture with rough pattern recognition and fuzzy inference for short-term wind speed forecasting," Energy, Elsevier, vol. 254(PB).
    3. Neshat, Mehdi & Nezhad, Meysam Majidi & Mirjalili, Seyedali & Garcia, Davide Astiaso & Dahlquist, Erik & Gandomi, Amir H., 2023. "Short-term solar radiation forecasting using hybrid deep residual learning and gated LSTM recurrent network with differential covariance matrix adaptation evolution strategy," Energy, Elsevier, vol. 278(C).
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    5. Saeed, Adnan & Li, Chaoshun & Gan, Zhenhao & Xie, Yuying & Liu, Fangjie, 2022. "A simple approach for short-term wind speed interval prediction based on independently recurrent neural networks and error probability distribution," Energy, Elsevier, vol. 238(PC).
    6. Xinyue Fu & Zhongkai Feng & Xinru Yao & Wenjie Liu, 2023. "A Novel Twin Support Vector Regression Model for Wind Speed Time-Series Interval Prediction," Energies, MDPI, vol. 16(15), pages 1-23, July.
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