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Offshore wind speed assessment with statistical and attention-based neural network methods based on STL decomposition

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  • Xu, Li
  • Ou, Yanxia
  • Cai, Jingjing
  • Wang, Jin
  • Fu, Yang
  • Bian, Xiaoyan

Abstract

This work proposes a novel offshore wind speed prediction approach by combining statistical method and attention-based neural network with seasonal-trend decomposition procedure with loess (STL). STL is utilized to decompose the processed data into season, trend and residual terms. Then, an attention-based long short-term memory neural network model (AT-LSTM), possessing the advantages of generalization and high-dimensional function approximation, is modeled to train season and residual terms with obvious volatility characteristics. And a hybrid model of auto-regressive integrated moving average (ARIMA) and LSTM is applied to predict the linear and nonlinear sequences in trend term with relatively gentle feature, yielding the proposed STL-AR-LSTM-ATLSTM model. Wherein, the proposed method is verified through sufficient pre-judgment experiments on season, trend and residual terms, as well as detailed multi-model comparison experiments. Finally, microcosmic prediction results and predicted statistical frequency distributions indicate that the new model has better prediction effect on offshore wind, compared to ARIMA, AT-LSTM, ARIMA-AT-LSTM models. Meanwhile, the presented model can reduce the lag problem of predicted values and perform well in the prediction of extreme values.

Suggested Citation

  • Xu, Li & Ou, Yanxia & Cai, Jingjing & Wang, Jin & Fu, Yang & Bian, Xiaoyan, 2023. "Offshore wind speed assessment with statistical and attention-based neural network methods based on STL decomposition," Renewable Energy, Elsevier, vol. 216(C).
    • Handle: RePEc:eee:renene:v:216:y:2023:i:c:s096014812301011x
    DOI: 10.1016/j.renene.2023.119097
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    2. Zeng, Hang & Zhang, Hongmei & Guo, Jiansheng & Ren, Bo & Cui, Lijie & Wu, Jiangnan, 2024. "A novel hybrid STL-transformer-ARIMA architecture for aviation failure events prediction," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
    3. Liu, Wenhui & Bai, Yulong & Yue, Xiaoxin & Wang, Rui & Song, Qi, 2024. "A wind speed forcasting model based on rime optimization based VMD and multi-headed self-attention-LSTM," Energy, Elsevier, vol. 294(C).

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